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Getting Started with EVM (Ethereum Virtual Machine)

Getting Started with EVM 

Ethereum has been a game-changer since its launch in 2015. It revolutionized the way people think about blockchain technology and decentralization. For a quick refresher,  Ethereum is a public, open-source, decentralized blockchain which can run smart-contracts and enable developers to build and deploy decentralized applications (DApps).

Many used to believe that blockchain was all about cryptocurrencies. And Ether is just another cryptocurrency like the well-known Bitcoin. However, Ethereum took the blockchain technology to new heights by shifting its concept from being just another digital currency to a new decentralized platform with endless applications and possibilities. 

It gave birth to the ICO (Initial Coin Offering) wave, introduced a completely new programming language, supported the creation of DApps (decentralized applications), and, foremost, polarized the term “smart-contracts.” What makes all these possible is the heart of Ethereum’s success: the Ethereum Virtual Machine (EVM)

In this article, we’ll take a closer look at the EVM, what it is and how it works. We’ll also give hints and tips on how to develop on EVM using Solidity. So, without further ado, let’s get started!

Basics of Ethereum Virtual Machine 

We’ve already mentioned that EVM makes Ethereum what it is today. But we should establish a stronger foundation for understanding EVM. 

What is an Ethereum Virtual Machine (EVM)?

Ethereum Virtual Machine or EVM is a “world computer” that executes programs called “smart-contracts.” Smart-contracts are immutable computer programs intended to digitally facilitate, verify or enforce the negotiation or performance of a contract. These are applications that run precisely as programmed without the possibility of fraud or third-party interference.

Additionally, EVM is responsible for processing and executing all other transactions on the Ethereum network, such as handling DApps, and token transfers. It runs on every node in the Ethereum network and processes every transaction that goes through it. It is Turing-complete, meaning it can run any type of program as long as there are enough resources or “gas” to process it.

How does EVM work?

EVM works by executing a program called bytecode. This bytecode is generated from the high-level programming language Solidity (we will discuss this later in this article). The bytecode is then fed into the EVM, which processes and executes it.

To better understand this process, let’s compare it to how a traditional computer works. A desktop computer runs programs written in high-level coding languages like C++, Java, or Python. These programs are then converted into machine code, a low-level language the computer can understand. And the machine code is fed into the CPU (central processing unit), which processes and executes it.

Similarly, the bytecode generated from Solidity is fed into the EVM, which processes and executes it. The main difference here is that a traditional computer can only run one program at a time, while the EVM can run multiple programs simultaneously. This is because each program that runs on the EVM has its own isolated environment, which is called an “Ethereum Virtual Machine.” 

Developing on EVM with Solidity

Solidity as Programming Language

As we’ve mentioned before, the EVM executes a program called bytecode. This bytecode is generated from the high-level programming language called Solidity. So, to develop on EVM, you will need to understand the use of Solidity. 

Solidity is a contract-oriented, high-level programming language for implementing smart contracts. It was created specifically for the EVM and had syntax inspired by existing languages such as C++, Python, and JavaScript. However, there are a few things that you should know about Solidity. 

First of all, Solidity is a statically typed language, which means you will need to declare the type of each variable before using it. For example, before using it, you must declare whether a variable is an integer or a string. Secondly, Solidity is case-sensitive, so you will need to be careful about the casing of your variables. For instance, the variable “MyVariable” differs from “myvariable.” Third, Solidity does not have a concept of “null,” meaning you will need to use the keyword “require” to check if a variable is null or not.

Tools to Get Started

You can use the following few tools to get started on EVM. The first tool that you will need is the Remix IDE. A Remix is a browser-based IDE that allows you to write, compile, and debug Solidity contracts. It also comes with a built-in debugger and an integrated testing environment.

The next tool you can use is Hardhat. Hardhat is a toolkit for Ethereum development that allows you to automate many of the tasks involved in smart contract development, such as compiling, testing, deploying, and upgrading contracts.

The last tool that you can check is Truffle. Truffle is a development environment, testing framework, and asset pipeline for Ethereum. It makes it easy to develop smart contracts and provides a suite of tools for testing, debugging, and deploying contracts.

After choosing the tools, you must decide which Ethereum network you want to deploy your contract to.  You can choose from two main networks: the testnet and the mainnet. Testnet is a global testing environment in which developers can obtain and spend ether with no real-world value.” In other words, it is a test network where you can experiment with your contracts without worrying about losing any real money.

On the other hand, the mainnet is the “live” Ethereum network, where all transactions have real-world value. Contracts deployed on the mainnet are live and irreversible. Also, it is accessible to anyone in the world. Hence, ensure that your contracts are thoroughly tested before deploying them on the mainnet.

Lastly, once you have deployed your contract on either the testnet or the mainnet, you can view it on Etherscan. Etherscan is a block explorer and analytics platform for Ethereum that allows you to view all of the transactions you have made on the Ethereum network and information about individual addresses and contracts.

Tips on Developing on EVM

Here are some tips that you can use in developing on EVM: 

  1. Make sure to test your contracts thoroughly before deploying them on the mainnet because once a contract is deployed on the mainnet, it cannot be changed or deleted.
  2. Secure your private keys and keep them safe. If someone gets ahold of your private keys, they can access all of your Ether.
  3. Consider using tools to automate the tasks involved in smart contract development to save you a lot of time and effort in the long run. 
  4. Be aware of the gas costs associated with each transaction. Every transaction on the Ethereum network costs a certain amount of Ether to execute.
  5. Keep your contract code simple and easy to understand. Complex contracts are more difficult to debug and likely to contain errors.

Conclusion 

It’s safe to say that Ethereum is a disruptive innovation with the potential to change how we interact with the digital world. That’s why it’s no wonder Ethereum’s price today continuously rises. With its powerful smart contract functionality, Ethereum provides a whole new level of flexibility and control.

While it is still in its early stages, Ethereum Virtual Machine (EVM) has already established impactful changes, and its further development is definitely worth keeping an eye on.

Sophia Young recently quit a non-writing job to finally be able to tell stories and paint the world through her words. She loves talking about fashion and weddings and travel, but she can also easily kick ass with a thousand-word article about the latest marketing and business trends, blockchain, cryptocurrency, finance-related topics, and can probably even whip up a nice heart-warming article about family life. She can totally go from fashion guru to your friendly neighbourhood cat lady with mean budgeting skills and home tips real quick.

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What is Code Review? — Best Practices, guidelines and tools.

Code review

Code reviews are a type of software quality assurance activity that involves rigorous evaluations of code in order to identify bugs, improve code quality, and assist engineers in understanding the source code.

Implementing a systematic approach for human code reviews is one of the most effective ways to enhance software quality and security. Given the probability of mistakes during code authorship, using many fresh eyes with complementary knowledge may disclose flaws that the original programmer may have overlooked.

A successful peer review process requires a careful balance of well-established protocols and a non-threatening, collaborative atmosphere. Highly structured peer evaluations can hinder productivity, while lax approaches are frequently unsuccessful. Managers must find a happy medium that allows for fast and successful peer review while also encouraging open communication and information sharing among coworkers.

The Benefit/Importance of Code Reviews

The fundamental goal of code review is to guarantee that the codebase’s code health improves with time.

Code health is a “concept” used to measure if the codebase on which one or more developers are working is — manageable, readable, stable (or less error-prone), buildable, and testable.

Code reviews enhance code quality by detecting issues before they turn unmanageable, it ensures a consistent design and implementation and also assures consistency of standards. It contributes to the software’s maintainability and lifespan, resulting in sturdy software created from components for smooth integration and functioning. It is inevitable that adjustments will be required in the future, thus it is critical to consider who will be accountable for implementing such changes.

When source code is regularly reviewed, developers can learn dependable techniques and best practices, as well as provide better documentation, because some developers may be oblivious of optimization approaches that could be applicable to their code. The code review process allows these engineers to learn new skills and improve the efficiency of their code, and produce better software.

Another significant benefit of code reviews is that they make it easier for analysts and testers to comprehend. In Quality Assurance (QA) testing, testers must not only evaluate the code quality but also discover issues that contribute to bad test results. This can result in ongoing, needless development delays owing to further testing and rewriting.

Performing Code Reviews

Good code reviews should be the standard that we all strive towards. Here are some guidelines for establishing a successful code review to ensure high-quality and helpful reviews in the long run:

Use checklists

Every member of your team is quite likely to repeat the same mistakes because omissions are the most difficult to identify since it is hard to evaluate something that does not exist. Checklists are the most effective method for avoiding frequent errors and overcoming the challenges of omission detection. Checklists for code reviews can help team members understand the expectations for each type of review and can be beneficial for reporting and process development.

Set limits for review time and code lines checked

It might of course be very much tempting to rush through a review and expect someone else to detect the mistakes you omitted. However, a SmartBear study indicates a considerable decline in defect density at speeds quicker than 500 LOC per hour. The most effective code review is performed in a suitable quantity, at a slower speed, for a limited period of time.

Code review is vital, but it can also be a time-consuming as well as a painstaking process. As a result, it is critical to control how much time a reviewer or team spends on the specifics of each line of code. Best practices in this area include ensuring that team members do not spend more than an hour on code reviews and that the team does not examine more than a few hundred lines in a certain amount of hours.

In essence, it is strongly advised not to review for more than 60 minutes at a time, as studies suggest that taking pauses from a task over time can significantly increase work quality. More regular evaluations should lessen the need for a review of this length in the future.

Performing a security code review.

A security code review is a manual or automated method that assesses an application’s source code. Manual reviews examine the code’s style, intent, and functional output, whereas automated tools check for spacing or name errors and compare it to known standard functions. A security code review, the third sort of evaluation, examines the developer’s code for security resilience.

The goal of this examination is to identify any current security weaknesses or vulnerabilities. Among other things, code review searches for logic flaws, reviews spec implementation, and verifies style guidelines. However, it is also important that a developer should be able to write code in an environment that protects it against external attacks, that can have effects on everything from intellectual property theft to revenue loss to data loss. Limiting code access, ensuring robust encryption, and establishing Secrets Management to safeguard passwords and hardcodes from widespread dissemination are some examples.

Make sure pull requests are minimal and serve a single function.

Pull requests (PRs) are a typical way of requesting peer code evaluations. The PR triggered the review process when a developer completes an initial code modification. To improve the effectiveness and speed of manual code review, the developer should submit PRs with precise instructions for reviewers. The lengthier the review, the greater the danger that the reviewer may overlook the fundamental goal of the PR. In fact, a PR should be no more than 250 lines long because a study shows reviewers may find 70–90 percent of errors in under an hour.

Offer constructive feedback.

Giving constructive feedback are very essential as code reviews play very important roles in software development, however, it is also important to be constructive rather than critical or harsh in your feedback to maintain your team’s morale and ensure keep the team learns from the mistake.

Code review examples

The main outcome of a code review process is to increase efficiency. While these traditional methods of code review have worked in the past, you may be losing efficiency if you haven’t switched to a code review tool. A code review tool automates the process of code review so that a reviewer solely focuses on the code.

A code review tool integrates with your development cycle to initiate a code review before new code is merged into the main codebase. You can choose a tool that is compatible with your technology stack to seamlessly integrate it into your workflow.

A great example of code review, especially in Python, which is my favored language, would be dealing with Duck Typing, which is strongly recommended in Python to be more productive and adaptable. Emulating built-in Python types such as containers is a common use-case:

 # Pythonic!
    class DictLikeType:
        def __init__(self, *args, **kwargs):
            self.store = dict(*args, **kwargs)

        def __getitem__(self, key):
            return self.store[key]

        ...

Full container protocol emulation involves the presence and effective implementation of several magic methods. This can become time-consuming and error-prone. A preferable approach is to build user containers on top of a respective abstract base class:

# Extra Pythonic!
    class DictLikeType(collections.abc.MutableMapping):
        def __init__(self, *args, **kwargs):
            self.store = dict(*args, **kwargs)

        def __getitem__(self, key):
            return self.store[key]

        ...

We would not only have to implement fewer magic methods, but the ABC harness would also verify that all necessary protocol methods were in place. This mitigates some of the inherent instability of dynamic typing.

Top code review tools for Developers

The fundamental purpose of a code review process, as described earlier in this article, is to enhance efficiency. While the traditional code review approaches outlined above have worked in the past (and continue to work), you may be losing efficiency if you haven’t switched to using a code review tool. A code review tool automates the code review process, freeing up the reviewer’s time to concentrate solely on the code.

Before adding new code to the main codebase, code review tools interact with your development cycle to initiate a code review. You should choose a tool that is compatible with your technological stack so that it can be readily integrated into your workflow. Here is a list of some of the top code review tools:

1. Github
You may have previously used forks and pull requests to evaluate code if you use GitHub to manage your Git repositories in the cloud.

code review - GitHub

Github also stands out due to it’s discussion feature during a pull request, with github you can analyze the difference, comment inline, and view the history of changes. You can also use the code review tool to resolve small Git conflicts through the web interface. To establish a more thorough procedure, GitHub even allows you to integrate with other review tools via its marketplace.

2. Crucible

Atlassian’s Crucible is a collaborative code review tool that lets you to examine code, discuss plan modifications, and find bugs across a variety of version control systems.

Code review - Crucible
Source — Crucible Code review

Crucible integrates well with other products in Atlassian’s ecosystem, including Confluence and Enterprise BitBucket. And, just like with any product that is encircled by other products in its ecosystem, combining Crucible with Jira, Atlassian’s Issue, and Project Tracker, will provide the greatest advantage. It allows you to do code reviews and audits on merged code prior to committing.

3. Smartbear Collaborator

SmartBear Collaborator is a peer code and document review tool for development teams working on high-quality code projects. Collaborator allows teams to review design documents in addition to source code.

Code review - Smartbear
Source — Smartbear Overview Review

You can use Collaborator to see code changes, identify defects, and make comments on specific lines of code. You can also set review rules and automatic notifications to ensure that reviews are completed on time. It also allows for easy integration with multiple SCMs and IDEs such as Visual Studio and Eclipse amongst others.

4. Visual Expert

Visual Expert is an enterprise solution for code review specializing in database code. It has support for three platforms only: PowerBuilder, SQL Server, and Oracle PL/SQL. If you are using any other DBMS, you will not be able to integrate Visual Expert for code review.

Visual Expert
Source — Visual Expert for Oracle

Visual Expert spares no line of code from rigorous testing. The code review tool delivers a comprehensive analysis of code gathered from a customer’s preferred platform.

5. RhodeCode
Rhodecode is a secured, open-source enterprise source code management tool. It is a unified tool for Git, Subversion, and Mercurial. Its primary functions are team collaboration, repository management, and code security and authentication.

RhodeCode
Source — Rhodecode

RhodeCode distinguishes itself by allowing teams to synchronize their work through commit code commentary, live code discussions, and sharing code snippets. Teams may also use coding tools to assign review jobs to the appropriate person, resulting in a more frictionless workflow for teams.

Conclusion

We learned what code review is and why it is crucial in the software life cycle in this tutorial. We also discussed best practices for reviewing code and the various approaches for doing so, as well as an example of a code review and lists of top code review tools to assist you get started reviewing code throughout your organization or team.

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How to Deploy Your Lambda Functions with CloudFormation

Deploy your Lambda Functions

AWS Lambda is a powerful tool for developing serverless applications and on-demand workflows. However, this power comes at a cost in terms of flexibility and ease of deployment, as the manual deployment process that AWS Lambda recommends can be error-prone and hard to scale. 

CloudFormation revolutionizes this process, replacing copied zip files with dependable and repeatable template-based deployment schemes. With CloudFormation, your Lambda functions will be easier to maintain, easier for your developers to understand, and easier to scale as your application grows.

Reviewing AWS Lambda Deployments

AWS Lambda function deployments are based around file handling—namely, by zipping your code into an archive and uploading the file to AWS. At its core, all AWS Lambda functions follow this pattern:

  • Create a zip file.
  • Upload to an S3 bucket.
  • Set the function to active.

This takes place whether you’re manually deploying the code, have outsourced your deployments to a tool, or are following any protocol in-between.

Once the file is received, AWS unzips your code into the appropriate folder structure, making it available to run when the Lambda container is spun up. This approach is a key point to remember as we discuss Lambda deployments and also exposes one of the first holes in the manual deployment process—AWS Lambda functions have an unstated structure that you need to follow. 

Simply put, you do not want to right-click on a file and create an archive; otherwise, you’ll encounter an error when you try to run your deployed Lambda code. The following screenshots illustrate this issue:

Figure 1: Do not zip the folder using this method

If you examine the zip files produced by the above method, you’ll find that their root level consists of your code folder:

Figure 2: This zip file will not be parsable by AWS Lambda

The issue this introduces is specifically related to how AWS Lambda deploys the code—namely, it simply unzips the provided code archive to an executable folder, then routes invocation requests to the application code found in that folder. When you provide a zip archive with a folder at the root level, instead of the application code itself, AWS Lambda has no idea what to do and throws errors. So, make sure that you zip the folder contents themselves, as follows:

Figure 3: Zipped at the appropriate level, the function code should be the root of the archive

When you do this, your code is put at the root level of the zip folder. This allows AWS Lambda to easily deploy your published code:

Figure 4: The code file is present at the root of the zip archive

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Each Lambda function exists independently, meaning that you cannot easily share resources between Lambda functions—shared libraries, source data files, and all other information sources that need to be included with the zip archive you upload. This additional fragility and duplication can be resolved with Lambda layers. Lambda layers provide you with a common base for your functions, letting you easily deploy shared libraries without the duplication that would be required when using only the base container.

While you can set up a scriptable and maintainable deployment process, once the project size grows, the brittleness of the above steps will quickly become apparent. AWS CloudFormation solves this very complex problem by categorizing infrastructure as code; this lets your developers and development operations teams create, deploy, and tear down resources with simple configuration-file modifications. These configuration files are human-readable and can be modified in any text configuration, programming language, or UI tools that you desire. 

Furthermore, CloudFormation lets you centralize the deployment of your infrastructure, creating a build process for your serverless functions that is both repeatable and predictable.

Improving Lambda Deployments with CloudFormation

Moving from the error-prone manual process of Lambda deployment to the superpowered CloudFormation model is a straightforward process of translating your function’s infrastructure needs into the appropriate CloudFormation template language. CloudFormation lets you then consolidate the disparate resource deployments for your application into a small set of configuration files, allowing your infrastructure to be maintained alongside your application code.

All in all, CloudFormation makes deploying AWS Lambda functions incredibly simple.

Start by creating the template file that will define your resources. This will be your working folder for your code. Next, create your function in the appropriate file for your desired Lambda runtime. Finally, create an S3 bucket and provide its address to your Lambda function; once you’ve done this, you can deploy functions simply by copying your zip file to the correct S3 bucket.

CloudFormation will be the tool that ties together all the resources your function requires. In CloudFormation, you will define the function, the function’s IAM role, the function’s code repository in S3, and execution policies to ensure that your function can do everything it needs to do within the AWS ecosystem. CloudFormation further gathers these resources together, centralizing all of your infrastructure definitions in a single template file that lives alongside your code.

Running Through a Sample Deployment

In this section, we’ll run through a quick example of creating a CloudFormation-driven deployment process for an AWS Lambda function. Start with the following Node.JS code to create a simple Lambda function using the nodejs12.x runtime:

exports.handler = async (event) => {
        // TODO implement
        const response = {
            statusCode: 200,
            body: JSON.stringify('CloudFormation deployment
     successful!'),
         };
         return response;
      };

This code is deliberately simple, allowing you to highlight the deployment process itself. Once you’ve created the function code, you can begin creating all of the items that will allow you to deploy and run the code with CloudFormation.

First, create a new file in the same directory as the function. These instructions assume that your file will be named template.yml. Once you‘ve created the empty template file, start including resources needed to get your function running. You can begin with defining an S3 bucket to hold your function code:

 AWSTemplateFormatVersion: '2010-09-09'
     Description: 'Example Lambda zip copy'
     Resources:
        LambdaZipsBucket:
          Type: AWS::S3::Bucket

Then, create the resources needed for your function, including an IAM role and the function definition itself:

MyFunctionRole:
          Type: AWS::IAM::Role
          Properties:
             AssumeRolePolicyDocument:
                Version: '2012-10-17'
                Statement:
                   - Effect: Allow
                     Principal:
                        Service: lambda.amazonaws.com
                     Action: sts:AssumeRole
              ManagedPolicyArns:
                -
arn:aws:iam::aws:policy/service role/AWSLambdaBasicExecutionRole
        MyFunction:
            DependsOn: CopyZips
            Type: AWS::Lambda::Function
            Properties:
               Description: Example
               Handler: index.handler
               Runtime: nodejs12.x
               Role: !GetAtt 'MyFunctionRole.Arn'
               Timeout: 300
               Code:
                   S3Bucket: !Ref 'LambdaZipsBucket'
                   S3Key: !Sub '${QSS3KeyPrefix}/lambda.zip

Once you’ve created the template file and modified it to reflect the resources above, you can deploy your functions from the command line with a single call:

aws cloudformation deploy --template-file template.yml
    --stack-name your-stack-name-here

This basic configuration will allow you to deploy your functions once they‘ve been uploaded to the S3 bucket specified in the function definition. You can now build upon this basic set of deployment functionality to automate any aspect of your stack creation. For a fully functional deployment sample, you can clone the excellent quickstart repo from AWS.

Some Tips and Additional Resources

As you work CloudFormation into your Lambda development pipeline, you’re bound to encounter headaches. Here are a few tips to help avoid unnecessary frustration from this immensely helpful AWS blog article on the topic:

  • Did you know that you can deploy in-line Lambda code? Simply include your (small) Lambda function code as lines appended after the zipfile key.
  • If you only need to release your functions to a small subset of AWS regions, you can provide a list of regional buckets to populate with your code; simply expand the resource listing when defining your source Lambda zip files.
  • With a simple name format policy and some custom code, you can create a system that allows you to upload your S3 file once, then publish it to any AWS region that supports AWS Lambda.

In addition to the AWS blog post above, my fellow IOD experts also had a few thoughts on the best ways to achieve serverless deployment zen:

Once again, the excellent Quickstart repo provided by AWS also offers a useful CloudFormation-driven tool for deploying your AWS Lambda code across multiple regions from a single bucket.

Wrapping Up

AWS Lambda deployments are brittle and prone to error out-of-the-box, requiring you to wade through numerous user interfaces and dialog flows to create your function, associated execution roles, and the resources you need to host your deployable code. 

With CloudFormation, you can convert all of this manual configuration into a single template file with the power to describe an entire application stack. CloudFormation replaces the complex and error-prone manual process of deploying Lambda functions with a repeatable, maintainable process that can be maintained alongside your code.

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Understanding developer personalities

developer personalities

Personality theories provide a blueprint for understanding why people behave the way they do. In the latest edition of our State of the Developer Nation 22nd Edition – Q1 2022, we incorporated a measure of the widely accepted ‘Big Five’ personality dimensions. We did this in order to better understand the personality traits of software developers. Here, we share some of our findings on developer personalities. Our aim is to discuss how this kind of information can help to support interactions with developers.

Personality measures are a powerful tool for understanding people’s preferences and behaviours. Software teams need diversity not only in terms of skills, experience, and knowledge, but also require a variety of personalities. This will help teams collaborate effectively on complex and challenging projects.

The Ten-Item Personality Inventory

We used the Ten-Item Personality Inventory (TIPI) methodology in order to measure the ‘Big Five’ personality dimensions. These dimensions are: emotional stability, extraversion, openness to experiences, agreeableness, and conscientiousness. The TIPI method is well-suited for situations where short measures are required. The results have been shown to have good alignment with other widely used Big Five measures1. Although more comprehensive and accurate personality measures than TIPI exist, they typically require an entire survey to themselves.

The TIPI method presents respondents with ten pairs of personality traits and asks them to rate how strongly these traits apply to them. Below, we show responses to these items for over 12,000 developers. We find that developers, in general, see themselves as complex and open to new experiences (86% agree or strongly agree that this applies to them), dependable and self-disciplined (79%), calm and emotionally stable (76%), and sympathetic and warm (74%). 

Developer personalities - developers are most likely to agree that they are dependable, self-disciplined, and open to new experiences

Diving deeper into the TIPI data allows us to identify more specific personality types within the general developer population. We collapsed these ten items into five distinct measures, one for each of the Big Five personality dimensions. For example, statements about being ‘sympathetic, warm’ and ‘critical, quarrelsome’ combine to give an overall measure of agreeableness. We then derived a score for each developer on each of the five dimensions. This helped us identify the developer personalities at the polar ends of each dimension, e.g. labelling those who are at the top end of the agreeableness scale as ‘agreeable’ and those at the bottom end as ‘disagreeable’. 

Finally, we segmented all developers into a set of distinct personality types. We did this by using the personality labels that they had been assigned as inputs to our segmentation algorithms.

Approximately 8% of all developers differ from the aforementioned group. They showcase a higher level of openness to experiences – often related to intellectual curiosity. These software developers have personality traits that suggest they are likely to investigate new tools and technologies. They are also more likely to stay up to date with the cutting edge of technology.

The Five Developer Personalities

The following charts show the characteristics of five example developer personalities revealed within our data. A well-rounded, ‘balanced’ personality type accounts for 52% of the developer population. These are developers who sit firmly at the centre of each dimension. They are neither introverted nor extroverted, highly agreeable nor disagreeable, emotionally unstable nor lacking emotion, etc.

5% of developers fit a ‘responsible and cooperative’ personality type. These developers score highly in conscientiousness, openness to experiences, and agreeableness in comparison to the majority of developers. Increased conscientiousness often relates to setting long-term goals and planning routes to achieve them, e.g being career-driven. Higher scores for openness to experiences reflects a preference for creativity and flexibility rather than repetition and routine. Our data backs this up. These developers are more receptive to personal development-related vendor resources. For example, 35% engage with seminars, training courses, and workshops compared to 25% of ‘balanced’ developers. Their high scores for agreeableness also correlate with greater engagement with community offerings. For example 23% attend meetup events compared with 17% of ‘balanced’ developers.

5% of developers conform to an ‘achievement-driven and emotionally stable’ profile. As with the previous personality type, they are conscientious and open to experiences. However, they score much higher in terms of emotional stability but slightly lower in terms of agreeableness. Developers who score high in emotional stability react less emotionally. For example they favour data over opinions. Lower agreeableness can be a useful trait for making objective decisions, free from the obligation of pleasing others.

We also find a segment of developers with an ‘introverted and unreliable’ profile. They indicate that they are less involved in social activities, disorganised, closed to new experiences, and less agreeable than other developers. Fortunately, these developers, who are likely hard to reach and engage in new activities and communities, are a very small minority, at 2% of all developers.

Common developer personality profiles
Common developer personality profiles

Developer Personalities, Roles and Content Preferences

Finally, we show how the characteristics of these developer personalities vary, in terms of both associations with developer roles and the kinds of information and content that they consume. Developers in the ‘balanced’ profile are most likely to have ‘programmer/ developer’ job titles. However, those who fit the ‘responsible and cooperative’ profile are disproportionately more likely to occupy creative (e.g UX designer) roles. This aligns with their increased creativity/openness, and senior CIO/CTO/IT manager positions, reflecting their self-discipline and achievement striving.

Those who are ‘achievement-driven and emotionally stable’ are less likely than other personality types to have ‘programmer/developer’ job titles, but disproportionately more likely to be data scientists, machine learning (ML) developers, or data engineers. They tend to deal mainly in facts and data rather than opinions and emotions. Those in the ‘introverted and unreliable’ profile are more likely to have test/QA engineer and system administrator job titles than those in other personality types. 

Developer personalities - achievement-driven developers with high emotional stability are 50% more likely to be data scientists than those with a balanced personality

When it comes to where developers go to find information and stay up to date, perhaps unsurprisingly, the ‘introverted and unreliable’ personality type uses the fewest information sources overall, affirming that they are a difficult group to engage via community-focussed events and groups. However, their use of social media is in line with other personality types, suggesting that this may be a suitable channel for catching the attention of this hard-to-reach group.

Both of the high-conscientiousness and high-openness personality types use the widest range of information sources overall, however, those who are more cooperative are considerably more likely to turn to social media for information about software development (53% of the ‘responsible and cooperative’ type vs. 44% of the ‘achievement-driven and emotionally stable’ type).

‘Intellectually curious’ developers are the most likely to make use of official vendor resources and open source communities. Hence, the audience that vendors reach via these resources may be slightly more keen to experience new products and offerings, than the typical ‘balanced’ developer.

What’s Next with Developer Personalities

We just began to scratch the surface of developers’ personality profiles. The personality types we have shown are indicative of just a few of the differences that exist among developers. By capturing this kind of data, we’ve opened the door for more extensive profiling and persona building, along with a deeper analysis of how the many other developer behaviours and preferences that we track align with personality traits. If you’re interested in learning more about developer personalities and how this can help you to reach out to developers, then we’re very excited to see how our data can support you.

Developer personalities - Achievement-driven developers use more information sources than those with a balanced personality
Categories
Analysis

Are Low/No-Code tools living up to their disruptive promise?

Low/No-Code tools

You may be wondering why software development is a slow and expensive exercise. Its complexity and the need for technical resources may be hard to find or very expensive to hire. Due to this, low/no-code tools have become increasingly popular among developers today. In this article, we explore low/no-code development, the advantages/disadvantages, and try to understand if it is disrupting the software industry today with data-driven facts.

What is low/no-code tools software?

Low/no-code tools are visual software development platforms. Unlike traditional software development, which involves programmers writing lines of code, the low-code/no-code platforms encapsulate all this behind the tool.

As per the State of the Developer Nation 22nd Edition – Q1 2022 report,  46% of professional developers use low/no-code tools for some portion of their development work.

The difference between Low-code and No-code development platforms

Before we proceed further, hope you know the difference between low-code and no-code software.

Low-code platforms require technical knowledge and it helps the developers to code faster. The main benefit is that these platforms have powerful tools that speed up technical software development activities and are built for coders. 

No-code platforms are built for standard business users. There are no options for manually editing code and rather focus on the user experience aspect in creating functionality and abstracting the technical details away from the user. 

Despite some level of automation in low-code platforms, coding is still core to the development process. Openness is a key difference between low-code platforms and no-code ones. As a developer, you can modify existing code or add new ones to change the application. The ability to add code provides flexibility with more use cases and customization possibilities. However, it limits backward compatibility.

Any new version changes to the low-code platform may affect custom code developed and may need a proper review before an upgrade. That means whenever there is a launch of a new version of the low-code platform, customers will need to test if their customized code functionality works well after the upgrade. 

In the case of no-code versions, customers do not have to worry about any functionality or breaking changes due to the platform being a closed system.

Low-code platforms offer easy integration capabilities. Unlike No-code which can lead to users creating programs without proper scrutiny with risks like security concerns, integration, and regulatory challenges besides increasing technical debt.

How do you use low/no-code tools and software?

As a user, you visually select and connect reusable components representing the steps in a process. You then link them to create the desired workflow. The code is very much present behind the steps, which drives the functionality.

Low-code/no-code tools enable non-technical staff at workplaces or anyone to develop business workflow applications. Moreover, low-code/no-code platforms allow easy integration with other business applications. For example, a sales staff could use a low-code/no-code application to develop qualified leads or opportunities into a database. They could then set triggers to send out targeted communications based on the occurrence of specified events.

Advantages and disadvantages of low code/no-code software.

Low-code/no-code platforms have both advantages and disadvantages. Here are some of them.

Lower costs & faster development: Time is money, and you can reduce your costs when you create more applications faster that automate and help improve productivity. You save costs on recruiting additional developers as applications that took a few months can be completed in a few days leading to faster availability of business applications.

Integration feasibility & challenges: Today’s application programming interfaces, or APIs, enable a high level of integration between applications. Integration works seamlessly in many cases. However, when we look at scalability and speed, custom integration is preferred for critical enterprise business applications.

Creating APIs is not easy and requires a better understanding of the IT landscape and related applications. Hence creating significant and sizeable applications will require experienced developers rather than non-technical hands-on low code/no-code software.

Time to market gains: As low code/no-code software replaces conventional hard coding with drag and drop functionality, reusable components, ready-to-use templates, and minimal coding, organizations can deliver applications faster to the market. It, therefore, helps organizations gain a competitive edge and improve productivity.

Performance: The standard view on low code/no-code software is that it focuses on saving time and is effective and successful. However, low code/no-code software platforms are not designed for performance and limit the number of functions one can implement. Moreover, adding new features to an application built using low code/no-code software can get challenging.

Privacy and Security Issues: With low-code/no-code software, there are limitations to configuring data protection and privacy aspects. You do not have access to all the source code, making it challenging to detect any security gaps.

The future of software development

Low-code/no-code software platforms offer many advantages in creating business applications faster. There are some disadvantages to its limitations in coding functions and features. What is the ground situation today with low-code/no-code software platforms?

The State of the Developer Nation 22nd Edition – Q1 2022 report has some interesting insights on the actual usage of low-code/no-code software platforms. Here are some findings:

Who is using low-code/no-code tools?

  • 46% of professional developers use low-code/no-code (LCNC) tools for some portion of their development work.
  • Experienced developers, particularly those with more than ten years of experience, are the least likely to use LCNC tools.
  • Most developers that use LCNC tools do so for less than a quarter of their development work.
  • The Greater China area has the highest LCNC tool adoption rate. 69% of developers in this region report using LCNC tools, compared to the global average of 46%.
  • 19% of developers in North America use LCNC products for more than half of their coding work – almost twice the global average of 10%. This provides strong evidence that these tools can supplant traditional development approaches

Wrapping up

Low-code/No-code tools have great potential and disrupt the traditional software industry but at a slower rate. State of the Developer Nation 22nd Edition – Q1 2022 report shows us fascinating insights.

Experienced developers with ten or more years of experience are less likely to use low-code/no-code tools. It could probably be due to the flexibility that coding offers the experienced developers and their comfort with it. It may also have an angle related to the job security of software developers and the risks of automated LCNC tools taking away significant parts of programming activity. Experienced developers work on complex tasks and the low-code tools are more suited for simple programming tasks, which the experienced hands may find easy to do.

On the other hand, North American developers seem to be progressive in using LCNC products for half of their coding (twice the global average of 10%), showing massive potential for LCNC tools to supplement software development activities. A lot of initiative in using LCNC tools also rests with the software organizations leading initiatives and implementing these solutions. Younger developers may find it easier to automate some parts of coding using LCNC tools and speed up their development activities. 

The adapted LCNC approach each programmer takes to code and develop a feature can come from their learning experience. A younger developer may prefer to use LCNC for about 25% of their development work as they are familiar with using the tools and it is a way of working. An experienced developer may shun the tools as he has always been building applications from scratch by coding and no LCNC tools. 

As technology advances, and pressure to have business solutions quicker build up, organizations will need to use the latest LCNC tools. Developing robust functional and secure software solutions faster to get competitive gains will be a mandate amid the rapid pace of digital transformation. Today LCNC tools are progressing successfully in that direction and programmers irrespective of their experience need to adapt LCNC tools where an opportunity to improve productivity exists.

Categories
Analysis

How Developers Generate Revenues

How do developers generate revenues

How businesses and developers as individuals make money from software projects is one of the most important decisions they have to make. Of all the business models and strategies available, companies and freelancers need to pick the ones that best match their market and goals. This post focuses on the popularity of revenue models among professional developers and the companies they work for.

Of all the revenue models we track in our surveys, contracted development / consulting is the most popular model. As of Q1 2022, 31% of professional developers are using this model, 7 percentage points more than the next closest revenue model – selling apps or software. Contracted development can span months or even years, allowing for developers and companies to properly plan out resources during the project. In addition, professional developers and their companies may find the clients they contract for require additional services, thus leading to additional revenue. Contracted development is tried-and-true as it’s been the most popular revenue model for the past five surveys.

Selling apps/software through an app store or their own portal is the second most popular revenue model, with almost a quarter (24%) of professional developers making money in this way. Furthermore, adoption of this model has been stable over two and a half years, despite “Epic” lawsuits against Apple and Google in 2021, which argued that these app stores had excessive fees and restrictive payment collection processes. App stores and portals are popular now, but other technologies, such as progressive web apps (PWAs), could start to impact the popularity of app stores. PWAs can work across multiple platforms, provide a native experience, and can help developers avoid high commission fees from app stores; all of which are big incentives to embrace the power of the web.

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7% of professional developers are generating revenue from selling data

Interestingly, less than a tenth (7%) of professional developers are generating revenue by selling data. Data has often been referred to as the new gold and data breaches are heavily covered in news articles as well. If data is so valuable, why are so few professional developers using this model? Regulatory measures, such as the EU’s General Data Protection Regulation (GDPR), could be hampering developers’ ability to sell user data based on the “right to be informed” principle. The California Consumer Privacy Act (CCPA) also has multiple restrictions for selling user data including an earnings cap based on a company’s total revenue. These are just a couple of examples of why selling data is difficult, which impacts its popularity as a revenue model.

Next, we will look at how the industries that developers are active in influence their revenue models. Contracted development is the most popular revenue model across all sectors, further emphasising the effectiveness of this model.

Developers active in the software products and services, data analytics, and financial services verticals tend to have the same revenue strategies. Professional developers in all three of these sectors have the same top-three revenue model choices. In addition to contracted development, app stores and selling services/APIs are the more popular methods for generating revenue in these sectors.

In-app purchases break into the top three among developers in the entertainment and media sector. 28% of professional developers in this vertical are using this method, double the percentage of the general developer population. In-app purchases are strongly associated with the freemium strategy where users are able to use/download applications for free with some features restricted to micro-purchases. This strategy has become quite popular in game development for building a base of users and incrementally generating revenue, as long as the quality of production is high. 

Contracted development is the revenue model of choice across all industry verticals

For professional developers working for companies in the marketing and advertising sectors, the advertising revenue model rises to second place, but it’s unable to unseat contracted development as the most used model. Looking across industries, there’s an apparent lack of usage of advertising as a revenue model among most other developers. On average, advertising is ranked eighth among professional developers outside of the marketing and advertising industry, being used about three times less often. Again, privacy protection may be hindering developers’ ability to use this revenue model effectively.

Finally, we evaluate revenue model usage among developers in different-sized companies. Again, contracted development remains the most popular model across every size of company. This strategy is the status quo for developers, and, with such popularity, it’s presumed to be the expectation by customers seeking professional development.

Developers working for micro-businesses are the most likely to report that they generate revenue from contracted development, with over a third (36%) of developers who work in them using this model. Professional developers in micro- businesses are also using multiple revenue models slightly more often than other developers. This indicates that companies of this size are trying to maximise their earning potential while relying heavily on the industry standard of contracted development. That being said, contracts don’t sell themselves, and micro-businesses have only 2-20 employees, so developers in these companies will likely be a close part of sales conversations.

Usage of the advertising revenue model declines as companies grow in size

Developers at large enterprises have a slightly different profile, as they tend to use the contracted development model less often than developers in other company sizes. We also see less use of the multiple revenue model, indicating that companies of this size have a more focused strategy for generating revenue.

Categories
Tips

Git Internals Part 2: How does Git store your data?

How does Git store your data

In this article, we’ll be learning about the basics of the data storage mechanism for git. 

The most fundamental term we know regarding git and data storage is repositories. Let’s first understand what a git repository is and where it stands in terms of data storage in git.

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Repositories

A git repository can be seen as a database containing all the information needed to retain and manage the revisions and history of a project. In git, repositories are used to retain a complete copy of the entire project throughout its lifetime. 

Git maintains a set of configuration values within each repository such as the repository user’s name and email address. Unlike the file data or other repository metadata, configuration settings are not propagated from one repository to another during a clone, or fork, or any other duplication operation. Instead of this, git manages and stores configuration settings on a per-site, per-user, and per-repository basis.

Inside a git repository, there are two data structures – the object store and the index. All of this repository data is stored at the root of your working directory inside a hidden folder named .git. You can read more about what’s inside your .git folder here.

As part of the system that allows a fully distributed VCS, the object store is intended to be effectively replicated during a cloning process. The index is temporary data that is private to a repository and may be produced or edited as needed.

Let’s discuss object storage and index in further depth in the next section.

Git Object Types

Object store lies at the heart of the git’s data storage mechanism. It contains your original data files, all the log messages, author information, and other information required to rebuild any version or branch of the project.

Git places the following 4 types of objects in its object store which form the foundation of git’s higher-level data structures:

  1. blobs
  2. trees
  3. commits
  4. tags

Let’s look a bit more about these object types:

Blobs

A blob represents each version of a file. “Blob” is an abbreviation for “binary big object,” a phrase used in computers to refer to a variable or file that may contain any data and whose underlying structure is disregarded by the application.

A blob is considered opaque it contains the data of a file but no metadata or even the file’s name.

Trees

A tree object represents a single level of directory data. It saves blob IDs, pathnames, and some metadata for all files in a directory. It may also recursively reference other (sub)tree objects, allowing it to construct a whole hierarchy of files and subdirectories.

Commits

Each change made into the repository is represented by a commit object, which contains metadata such as the author, commit date, and log message. 

Each commit links to a tree object that records the state of the repository at the moment the commit was executed in a single full snapshot. The initial commit, also known as the root commit, has no parents and the following most of the commits have single parents.

A Directed Acyclic Graph is used to arrange commits. For those who missed it in Data Structures, it simply implies that commits “flow” in one way. This is usually just the trail of history for your repository, which might be very basic or rather complicated if you have branches.

Tags

A tag object gives a given object, generally a commit, an arbitrary but presumably human-readable name such as Ver-1.0-Alpha.

All of the information in the object store evolves and changes over time, monitoring and modeling your project’s updates, additions, and deletions. Git compresses and saves items in pack files, which are also stored in the object store, to make better use of disc space and network traffic.

Index

The index is a transient and dynamic binary file that describes the whole repository’s directory structure. More specifically, the index captures a version of the general structure of the project at some point in time. The state of the project might be represented by a commit and a tree at any point in its history, or it could be a future state toward which you are actively building.

One of the primary characteristics of Git is the ability to change the contents of the index in logical, well-defined phases. The indicator distinguishes between gradual development stages and committal of such improvements.

How does git monitor object history?

The Git object store is organized and implemented as a storage system with content addresses. Specifically, each item in the object store has a unique name that is generated by applying SHA1 to the object’s contents, returning a SHA1 hash value.

Because the whole contents of an object contribute to the hash value, and because the hash value is thought to be functionally unique to that specific content, the SHA1 hash is a suitable index or identifier for that item in the object database. Any little modification to a file causes the SHA1 hash to change, resulting in the new version of the file being indexed separately.

For monitoring history, Git keeps only the contents of the file, not the differences between separate files for each modification. The contents are then referenced by a 40-character SHA1 hash of the contents, which ensures that it is almost certainly unique.

The fact that the SHA1 hash algorithm always computes the same ID for identical material, regardless of where that content resides, is a significant feature. In other words, the same file content in multiple folders or even on separate machines produces the same SHA1 hash ID. As a result, a file’s SHA1 hash ID is a globally unique identifier.

Every object has an SHA, whether it’s a commit, tree, or blob, so get to know them. Fortunately, they are easily identified by the first seven characters, which are generally enough to identify the entire string.

One fantastic benefit of saving only the content is that if you have two or more copies of the same file in your repository, Git will only save one internally.

Conclusion

In this article, we learned about the two primary data structures used by git to enable data storage, management, and tracking history. We also discussed the 4 types of object types and the different roles played by them in git’s data storage mechanism. 

This was all for this article, I hope you find it helpful. These are the fundamental components of Git as we know it today and use on a regular basis. We’ll be learning more about these Git internal concepts in the upcoming articles.
Keep reading. In case you want to connect with me, follow the links below:

LinkedIn | GitHub | Twitter | Dev

Categories
Community

From Interpreted Basic to Swift UI – Part 2

From Interpreted Basic to Swift UI

Developer Nation Community Stories

Part 2

Check out Deborah’s story as it started out here

Week of WWDC 2022 

Monday – Mapping down the journey

Apple’s WorldWide Developers Conference (WWDC) for 2022 kicked off with the Keynote (1 hour and 48 minutes) where I sat and enjoyed the presentation and did not take any notes. The Platforms State of the Union sessions (1 hour 10 minutes) was next, after an hour for lunch on the West Coast. This also is a session I watch and do not take notes. They also had the Apple Design Awards (18 minutes) and a Day 1 recap (3 minutes).

During the week, I looked over the list of sessions available each day and jotted down the ones that I was most interested in, listed by the days the videos would be available. I figured I would start with those and potentially watch others based on what I learned from the first set and how much energy I had left from the day.

Tuesday – The Swift Cookbook of Navigation 

On June 7, I was ambitiously planning on watching the following sessions, once they because available that day:

  • Build Your First App in Swift Playgrounds
  • Dive into App Intents
  • Get to Know Developer Mode
  • Implement App Shortcuts with App Intents
  • The SwiftUI Cookbook for Navigation
  • What’s New in SwiftUI

I opened a Word document and copied the transcript of each session and any code that was attached and pasted it into the document. I figured it might come in handy to look for terms, ideas, or code snippets someday. 

The first session, Build Your First App in Swift Playgrounds, was interesting, and I do not have a lot of hand-written notes about it, as it did not directly apply to my goal, to learn how to use SwiftUI to update my way-out-of-date-app. 

The second one, Dive into App Intents sounded promising and yet…it was about how to make it easier for a user to RUN your app, not how to describe what I intended it to do for me. I took lots of notes from Implement App Shortcuts with App Intents because I was determined to make them work for me somehow. 

Now, the SwiftUI Cookbook for Navigation was exactly what I needed. It talked about three-column navigation split view, and showed Recipe Categories, Recipe List, and Recipe Detail. This would work fine for me. I need to implement this! Yippee! I found what I came for! 

Now, I just needed to dig deeper and find out how to implement what they were cooking up! It talked about new container types: NavigationStack and NavigationSplitView. 

I did not watch the last session I had planned for Monday. I saved that for Tuesday morning, as the new sessions were not available when I get up early (I live on the East Coast of the United States and Apple and their timeline is based on their West Coast location) and I wanted to have something to do so I wasn’t tempted to log into my full-time job and check on some things. This was my “vacation” week after all. 

Wednesday- Design App Shortcuts & Privacy Nutrition Label

On June 8, I added to the one left-over session with the following choices:

  • Create Your Privacy Nutrition Label
  • Design App Shortcuts

Swift UI & Swift Charts 

I started with What’s New in Swift UI from Monday’s list. It was a list of sessions that talk about the details of new items in SwiftUI, such as The SwiftUI Cookbook for Navigation, which was the last session I watched the previous day, so I congratulated myself on that choice. 

The session also mentioned Swift Charts, and this was not of interest to me because I had no immediate plans to add charts to my app. It then talked about sharing, and since this is broken in my app currently, it peaked my interest until they talked about Mail, Messages, Air Drop, Notes, Add to Photos etc and  not  Facebook or Twitter, that are not not considered sharing now. The session ended with a peek at layout and how a mixed layout can be achieved with Grid, GridRow and GridColumn. 

Create your Privacy Nutrition Label

Next up was Create Your Privacy Nutrition Label. I went into this thinking there was an actual label that would need to be filled out to submit an app to the store. The areas are Data Used to Track You, Data Linked to You, Data Not Linked to You, and Data Not Collected. If the developer selects the last one, the label reads, “This developer does not collect any data from this app” and that applies to my app. So, that was all I really needed from this session.

Thursday – What’s new in Xcode

On June 9, I added just two more to my original list:

  • What’s New in AppStore Connect
  • Writing for Interfaces

I still have not watched either of those sessions. I started the day with What’s New in Xcode which gave me a list of other sessions to take a look at when I have time. Some new code was introduced and hints and tricks were shared to make coding faster by using code completion and using simple icons in a single size instead of needing all the sizes for all the different versions of pixel count now available. 

The new sessions I added to the watch list are:

  • Use XCode to Build a Multiplatform App
  • Meet Swift Package Plans
  • Create Swift Package Plugins
  • Building Global Apps: Localization by Example

Localization do or don’t?

I decided to watch the last one on that list first, Building Global Apps: Localization by Example. This sounded promising, and I was interested in how well the translation would work for my app. I thought it was  too much AI and not enough about how you will need to hire translators who would take the text you send them and return it in different languages, which you reference in code to use the localized version of the text. 

My small little app is not going to be translated. Not for this next version. 

Custom Layouts and Swift Playground 

Next, I watched the session Compose Custom Layouts with SwiftUI and learned about grids and geometry reader and the layout engine. It was way over my head, and not really relevant to what I was hoping I needed to do. I thought  starting in Swift Playgrounds is where I should turn my attention. I watched Create Engaging Content for Swift Playground only to find out that this was not relevant either since it was about how to write an app for learners.It was interesting though!

Friday  – Having a Design Lab appointment 

I had signed up for a Design Lab appointment so someone from the Apple Design department would take a look at my app currently available and make suggestions on how to improve it and bring it up-to-date. I took lots of notes on what he found when he used the app, and I have a few things to think about when re-designing the app that I hope to incorporate into the final product. Then, as I was looking around for sessions to watch, I noticed that my all-time favorite SwiftUI online instructor, Paul Hudson (@twostraws) had recorded a session at Apple Headquarters in their new developer lab podcast space so I just had to watch it. It was What’s New in SwiftUI for iOS16 and this is the session I took the most notes from and, as usual, after watching his session, I wanted to jump right in and start coding. 

Well, WWDC is over for the year. There are still sessions I would like to watch…you know, that magical time we all have called “someday”. It is time to get the app updated and into the app store before the time runs out and Apple pulls it from the App Store. That story was in part 1 and documents now I spent most of my Saturday after WWDC, all psyched up to get going with this new-found knowledge and enthusiasm for a redesigned version of my personality test app (Which 1 Are You). 

Sunday

After spending way too much time on Saturday downloading the Beta version of Xcode, making sure the app works, and then finding out I can’t submit to the App Store from Beta, and all the other pitfalls I tripped over, I was not too enthusiastic about redesigning the app. So, I reread some of my notes, and remembered that I had had a dream over night about how exactly I could do this.

 I started a new project in XCode, called TestingCode. To use as a proof of concept It has three structs, some state variables, and the body consists of a NavigationSplitView from the Cookbook session and Paul’s podcast. I thought I had understood it, and yet I cannot get it to work. And it’s back to my full-time job, too!

Coming up next : Debugging, NagivationSplitView and more

And that is where this blog ends, for now. Tune in for part 3, where I debug the issue, and learn more about this intriguing NavigationSplitView and how it actually should work. This is where the transcripts and code samples from the sessions I copied should come in handy to see what the pieces of the NavigationSplitView actually should be and how they work together.

Any comments? Suggestions? Email me at FromInterpretedBasicToSwiftUI@gmail.com.

Categories
Tips

Tips for Choosing a Programming Language for your IT Career & Projects

Tips for choosing

Choosing a programming language can be complicated as many aspects need consideration. You might wish it was as easy as choosing between various flavors of ice cream or pizza. Ask any developer or technical manager to understand what drives popular choices in the tech world. In this article, you can learn what drives a choice of programming languages and the data-driven decisions developers should take to safeguard their careers while ensuring success in the projects they deliver.

Technology changes rapidly in today’s digital race, and the chosen language must get future potential to remain in use with strong developer communities, or else organizations can face maintenance and integration issues. Even young developers are keen to know which languages have excellent career potential, so they invest their time wisely.

Young developers may make the mistake of choosing a programming language because it’s trendy and cool. As a young developer, you can avoid these mistakes by referring to various tech forums and authentic sources like Slashdata’s – 22nd edition of The State of the Developer Nation (Q1 2022) that offer insights into popular programming languages and their growth trends.

Choosing a programming language

The choice of a programming language gets intertwined between your career aspirations and work experience. You learn a programming language and need to work on projects to gain relevant industry experience. So as a developer, you need to have a holistic approach to choosing a suitable language.

Choosing a programming language depends on various factors, and you should know all the components to get a better view and then make a choice. A good selection of programming languages will lead to spending less time on scaling, maintenance, and other aspects like security in projects.

Here are some typical questions you must ask when choosing a programming language for a project.

  • Does the programming language have proper community ecosystem support? Is it going to work over the long term? Is vendor support available?
  • What is the type of environment for the project – web solution, mobile, cross-platform, etc.?
  • Are there any infrastructure considerations like new hardware or particular deployment needs?
  • What do the clients prefer?
  • Are there any specific requirements for the programming language’s libraries, tools, or features?
  • Are experienced developers available for the programming language?
  • Are there any performance considerations, and can the language accommodate this performance?
  • Is there a security consideration or requirement for any third-party tool?

It would help if you remembered that irrespective of the chosen programming language, you can write good or bad code with any language. Besides the typical questions above, it’s advisable to consider a few critical factors in-depth before choosing a programming language. In programming, adherence to widely accepted design principles and philosophies is essential.

Some critical considerations driving the choice of a language include the following:

1. Type of application

The type of application varies from complicated embedded firmware to web and mobile. Common programming languages like Java, Python, JavaScript and C# can build different types of applications on various platforms. There are also situations where specific languages work better. With the rise in mobile apps, for example, you would choose Java for building a native Android app or a C and C++ combination for an embedded firmware project.

2. Complexity of applications

Identifying the application’s size and complexity helps determine the choice of programming language. The smaller and simple applications like marketing websites or webforms can use content management systems (CMS) like WordPress that may need minimal programing. On the other hand, complex applications like e-commerce websites or enterprise applications or emerging technology applications like IoT devices or AI-based applications may require Java or C#. As a technical manager, you can be an expert in gauging complexity with various experiences.

3. Organization culture

The choice of Open source technologies vs. proprietary software tends to rest with the organization’s culture and a direction often set by management. All programming languages have a trade-off, and some companies may choose one that is scalable, while others may pick one that has a shorter learning curve and is easy for the developers. Whatever the culture, the priority should be on choosing a language that optimally addresses the project needs. You can easily understand an organization’s choice once you start working on their technology stack.

4. Time to market:

Businesses rely on getting their product to the market early for competitive gains. Choosing new programming technologies and languages is better for a project with longer timelines. You can complete your project faster by leveraging the developers’ existing skills. For example, if you already have an AWS-based cloud environment and relevant team expertise, it will be quicker to work on it than move to another technology environment.

5. Maintainability

Technology stacks have their library ecosystems and vendor support. Choose a programming language with regular update releases that will stay current for some time. Maintaining the codebase is essential, and maintenance costs depend on the availability of developers. For example, as per today’s trends hiring Java, C#, Python, or PHP developers is easy and cost-effective. Organizations can make a data-driven decision by looking at the size of programming language communities from various industry reports from Slashdata.

6. Scalability, performance, and security:

The performance of the application depends on your choice of programming languages. It becomes essential when the development environment has limitations on scaling. Some popular tech stacks with great scalability include Ruby on Rails (RoR), .NET, Java Spring, LAMP, and MEAN.

It would be best if you protected applications from cyber threats. Following the security guidelines are crucial before choosing any programming language for your application. For example, a financial application needs PCI compliance, while healthcare-related applications need HIPAA compliance. Your choice of programming languages must be able to deliver application compliance.

Insights – Slashdata – 22nd edition of The State of the Developer Nation (Q1 2022)

You know the factors that drive the choice of programming languages. Let us look at findings from the Slashdata – 22nd edition of The State of the Developer Nation (Q1 2022). It offers exciting statistics that can help you as a developer know if your skills are up to date or need an upgrade.

JavaScript remains the most prominent language community, with close to 17.5M developers worldwide using it.

Python has remained the second most widely adopted language behind JavaScript, with the gap between the two largest communities gradually closing. Python now counts 15.7M users after adding 3.3M net new developers in the past six months alone.

The rise of data science and machine learning (ML) is an apparent factor in Python’s growing popularity. About 70% of ML developers and data scientists report using Python versus only 17% using R.

Java is one of the most critical general-purpose languages and the cornerstone of the Android app ecosystem. Although it has been around for over two decades, it is experiencing strong and steady growth. Nearly 5M developers have joined the Java community since 2021. 

Data shows that Java’s growth gets fueled by the usual suspects, i.e., backend and mobile development, and its rising adoption in AR/VR projects.

Wrapping up

We hope you have more clarity and data-driven insights in choosing programming languages for your career and projects. We encourage you to regularly read the whole SlashData – 22nd edition of The State of the Developer Nation (Q1 2022) report and stay updated on trending technologies.

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Community

From Interpreted Basic to SwiftUI – Part 1

Rewriting my app to Swift Part 2

Developer Nation Community Stories

Part 1

It is with great excitement that we are now kicking off our new section of stories coming right from our Developer Nation community members.

Our first piece comes from Deborah Graham who recently attended Apple’s WWDC and decided to put her learnings from that experience into immediate action. 

This is the first part of Deborah’s story, recorded in her own vivid style.

Taking 250 pages of notes at Apple’s WWDC

Well, another WWDC (Apple’s WorldWide Developers Conference) has come and gone (week of June 6-10, 2022). I took a week off my regular, full-time job as a Finance BI Specialist (yeah, doesn’t really explain what I do, let’s say I work with Access, VBA, and SQL a lot and some source systems to get data into reports and SQL tables so others can create Tableau reports for employees to consume) to “attend” the conference and I watched 12 sessions(so far)  and a surprise video from my favorite Swift online teacher, Paul Hudson (@twostraws).

I took a lot of notes, and then created a Word document with the transcripts and code segments attached to the sessions. This Word document is over 250 pages already (and I have sessions I haven’t seen yet that will be added to the document). Why did I do that? It’s actually already come in handy…but let’s not get ahead of ourselves.

Rewriting my app to Swift

For starters: Using Xcode and Figma

Over the weekend, after the WorldWide Developers Conference concluded, I started working on getting my current, old app (written in Objective-C and hard to maintain and update) updated to reset the clock so the app would stay available in the App Store. It’s “Which 1 Are You”, if you are inclined to download it and see the “before” version as I work though re-writing it into Swift and using updated standards and design expectations. 

I downloaded the latest Beta version of Xcode to make sure the app would actually still run (if not, I had a LOT of work ahead of me to get this app updated before the deadline Apple set before they yanked it off the App Store due to not having updated it since June of 2017…yikes, it IS old!). 

I found out that I needed an App Store Icon (1024 by 1024). I wanted to find a free program to create an icon. A quick search found a video which suggested some paid programs and a free one called Figma. I figured that would work for me. I created the simplest icon I could. Luckily, it built and ran without changes. 

I figured I needed something different before I could submit to the App Store, so I marked it as working Upside down, too. I ran into a long list of issues during Archive and they were all small items.

Submitting to the App store

Then I ran into issues when submitting to the App Store. I found out that I could not submit an app from a Beta version of Xcode. So, it was back to the other version I had and tried again.

It seems, since I said I don’t collect data on users, the frameworks I used originally for ads, which no longer work, do collect (or might collect) data, so I either needed to revamp my statement or remove the frameworks. So, I removed the frameworks and tested it again. All was good. Back to submitting to the App Store. 

Then, I needed screenshots in all sorts of different sizes. Luckily, another search showed me that the Simulator Tool allows for screenshots. I spent an hour running the program in different device simulators to take screenshots and upload them to the App Store Connect page to get all the images I needed. 

It was late Sunday night when I finally got the green light that the app was in for review. I went to sleep wondering if the review would find anything else I needed to do. Woke up Monday and checked my email. My updated app was available on the App Store! Version 1.6.3 was a go! 

This means, I have some time to work on getting the app into Swift and taking the knowledge and advice I received at WWDC 2022 and re-vamping the app.

This blog will document the progress, insights, inspirations, and setbacks as I encounter them. Hope you enjoy the journey!

About the Author – Deborah Graham

Deborah Graham is a professional developer. She has over 20 years of experience with various programming languages. It all started while attending Community College to get an Associate degree in Electronics technology (hardware). One day, they delivered some Apple IIe computers to the computer room to keep the PDP-11s company. There was a pong-like game written in Interpreted Basic where the source code was available. She quickly figured out that by increasing her paddle to 99% of the goal area and the opponent’s to 1% and turning up the speed of the ball, it was a fun way to a win. One small change, one big difference. And a love of programming was born. When not in class, she could usually be found sitting at that Apple IIe and tweaking the program a little to see the changes. After graduating, she held several hardware-related jobs to match the degrees (two Associates: Electronics Technology and Computer Maintenance Technology, both hardware-based degrees), including Test Technician, Manager of the Test Department, Customer Support (where she created the Customer Support Newsletter that would collect errors in the hardware or documentation and send it out to the local sales and support reps to cut down on support calls to the home office) and then she went to the documentation department to correct all the errors and to create technical documentation with the hardware engineers. She was a Technical Writer for an automated testing machine manufacturer. The documentation software (this was WAY before Word) used LISP (now just Lisp) as the extension tool. The writing team found they needed/wanted to extend the documentation software to do things like index across all documents and spell-check all documents. Luckily, there was a class happening and she flew up to Montreal to attend the week-long class and the love of programming continued! When she got back, all sorts of extensions were created and shared with other users of the software. 

One day, during the yearly performance review and plan for next year, her manager said to get a promotion, she would need to get a bachelor’s degree. Didn’t matter in what, it was one of the requirements to move up in the company. So, she called the local state school and asked what degrees they offered. And BSCS (Bachelor of Science, Computer Science) was one of the choices. It was either that, or Nursing or Teaching. Easy choice!

Right before graduating (and being eligible for a promotion), she was laid off! But now, with a degree to go with her love of programming, she was hired by the documentation software provider as a technical writer. After that, she worked for an early browser-based distance learning provider as a technical writer. One Friday, the boss asked her if the online help she was writing in HTML could pop up when links where clicked instead of completing replacing the main help. Granted, that’s a no-brainer now…but this just wasn’t available in HTML at the time. On the way home, she went to the local bookstore, went to the Computer Science section, and checked the index on all the books to find one that would be of help. She bought the book, read it on Saturday and Sunday (yes, she reads programming books for fun!) and on Monday, she got into the office before the boss, coded up come JavaScript and when he walked in, she said, “like this?” and he was amazed. The thrill of writing some code and seeing the result is very motivating. She went on to teach a class called “JavaScript for Technical Writers” at various places. As she was creating the class and with a vacation scheduled, she emailed the author of her JavaScript book asking his permission to use the book and the sample code in her class, promising to give him credit for the code. When she received his okay, that started her habit of bringing a programming book to take on vacation with her to learn a new language. Some people may take a romance novel to read at the beach…she falls in love with a new programming language!

That leads to the list of the programming languages that she was used to professionally write code:

  • M/MUMPS/Caché ObjectScript
  • HTML
  • XML
  • ASP
  • Lisp
  • Pascal
  • Certified AES
  • Visual Basic/VBA
  • JavaScript
  • Postscript
  • PCL
  • Python
  • SQL

And some that she uses for her hobby

  • Objective-C
  • Swift

Which brings us to the hobby. One team she was on had a meeting habit of taking personality tests before the meeting and then declaring which one of something each team member was. Which flower are you? Which Major city? Which cartoon character? Each week, someone on the team would find an online test, and they would all take the test and report the results for the icebreaker at the start of the meeting. One week, she was looking for a test on which character from Scorpion (a television show about geniuses that solved problems for the US government each week and learned a little more about how to function in society with a help of their “normal” assistant and her genius son). Anyway, she could find tests on which genius, but not specifically about that show. When she mentioned this difficulty to the team manager, she was told to write one herself. Apple had just opened the AppStore to developers, so here was another opportunity to learn a new language. It took a few years of reading books, finding online classes on Objective-C, and finally getting an app into the AppStore. The CIO of her company mentioned it at a few All-Hands meetings, and he shared his test results. There are 15 tests available for a variety of TV, movies, cartoons, and general interest. If you are at all inclined, it is called “Which 1 Are You” and it has her name as the developer. 

As an Apple Developer, she is invited to attend Apple’s WWDC (World Wide Developers Conference). Before the pandemic, it was almost impossible to get tickets. Since the last few years have been online, it opened the gates and allowed anyone to participate. She has “attended” WWDC for two years in a row now and looks forward to attending them each year. She takes a week off from her full-time job as a SQL and VBA Programmer and soaks in the new information and the enthusiasm from the presenters and other attendees.

She was asked by Developer Nation to document her journey from WWDC 2022 to an updated version of the app, totally re-written in SwiftUI, into the AppStore. Follow along and see what works, what doesn’t work, and let’s see if she can get the app ready for version 2.0! You can email the author at  FromInterpretedBasicToSwiftUI@gmail.com.