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9 Hidden Costs of Software Development: Tips to Save Your Budget

9 Hidden Costs of Software Development

Despite the growth of software development, individuals and companies often get overwhelmed when it comes to establishing a budget for a particular project. It is crucial to understand that although initial costs can be easily identifiable, many more costs may undermine the financial plan. Some of these hidden costs may include underestimating the time it takes to fix bugs or the extra cost of enhancing the infrastructure to cater to the increased user activity each time this occurs.

Considering some less obvious expenses when planning a project is essential since they may quickly go unnoticed and lead to extended time and money spent. Just the cost of creating applications and software can be a challenging zone to navigate, so where does it end? If you find ways to avoid the following potential budget problems, preventing or reducing such costs becomes easier.

9 Hidden Expenses in Software Development

There are certain peculiarities that one needs to take into account, such as how to better deal with the budget. There is always a potential for things to turn out more expensive than expected, which is particularly dangerous when the budget and timeline are tight.

Bug Fixes and Maintenance

Whether you have built software already and deployed, You will need to maintain it like any other types of applications (if not more) as there are bug fixes waiting for your contributions. These costs compound and presume a dedicated group for handling these complexities, as well as one that keeps it running.

Scalability Enhancements

This suggests that the more users you have, the user load on your software already explodes another boring tech server. It is however worthwhile to work with a software development agency in order to smoothly get through this scaling process.

Integration Costs

Cost of Using third-party applications/adopting other systems: Use or adoption can expose software development costs. Compatibility issues as well as the level of customization are common cost areas that create worry.

Training and Onboarding

Introducing new software into your team or clients own processes is always key, and so you need long training regiments. These sessions and training can make more on your costs.

Security Enhancements

Understanding and protecting your software against vulnerabilities is important. Supporting encryption and security checks is expensive, but they are essential in maintaining data integrity and establishing credibility with your clients.

Regulatory Compliance

Compliance and regulation Abiding with strict rules of compliance increases the likelihood that an application will be accurate, but normally experience more resources compared to changes in software. While a number of these changes may be long and expensive, they are needed in order to prevent legal issues.

Performance Tuning

It is vital to ensure that the program you developed performs well and quickly meets users’ needs. To optimize performance means make code load faster, occupy less space; however, it often costs more.

User Support and Documentation

The dependability and usefulness of the software product depend on how good its user manuals and customer support are done. These factors might require the teams dealing with their development to be constantly supported.

Hardware and Software Upgrade

A developer has to take care of his/her development environment and tools in order for them to work fine. You may also need upgrading hardware or software systems which will lead to some new additions that would have to be added into your budget.

Understanding such hidden costs in software development helps you plan and avoid unfavorable situations that may arise from costs you had not anticipated in software development.

Actionable Tips to Save Your Budget

Below are some tips on how to develop software and manage expenses in order to be cost effective.

Use Agile Methodologies

Be agile about what you do so that there is room for change or improvement, as well as a reduction of costs and the project being on track.

Prioritize Features

Therefore, important things should be discussed first as we allocate resources and time accordingly through which this will help in managing resources and most importantly ensure that basics are provided at the right time.

Automate Testing

This way, one can identify issues and provide solutions for them faster than having to go through manual testing that takes too long and it is even more expensive.

Leverage Open-Source Solutions

Consequently, when addressing such problems, many open-source softwares or libraries should be used in order to minimize expenses incurred because of licensing or development.

Outsource Strategically

It’s advisable to have some sections of the project done by excellent developers or teams from countries with low wages while they maintain quality.

Plan for Maintenance

All these other costs like support and fixing any issues after implementing software need to be taken into account during costing so that no additional monetary commitments are made.

Invest in Training

Provide enough training for your personnel such that they will be able to know and not waste additional time in trying to learn the tools and technologies, thus reducing productivity.

With these strategies in managing a budget of your software project, you will discover how to economize on software development and minimize eventual expenses that might come uninvited.

Conclusion

The management of hidden costs is one area which must be looked upon when tackling any development project. This helps you make better decisions, looking into other things that may have been otherwise forgotten and probably would have caused surprise if implemented much further down the line.

At this stage, good planning and strategic resource allocation are what will keep you going in a fast moving software industry. This sets a firm foundation for smoother, efficient development processes by taking into consideration possible hidden costs as well as learning how to save time within your budget. Make better financial choices resulting in your software project blooming right before you without using too much money.

Author Bio

Ankit Agarwal is the CEO of Dean Infotech, a leading software development agency known for its exceptional Salesforce development, integration, CRM and enterprise software development services. With years of experience under his belt, Ankit’s leadership has been instrumental in the company’s growth and success. His blog posts offer a unique perspective on software development, the Salesforce ecosystem, and the challenges and rewards of entrepreneurship.

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How to Leverage Software Development Lifecycle Diagram

Whether for the commercial or games market, software development is a complicated process. With over 26.3 million developers around the world, there are many tried and proven methods that can help make the development process easier. One main methodology is the Software Development Life Cycle (SDLC) which allows development teams to make high-quality software in the quickest time possible. 

As with any proven process, following the various steps can help people avoid mistakes that could delay deployment or create errors in the software. One helping hand that can be useful with the SDLC is the Software Development Lifecycle Diagram (SDLD). Just what are the SDLC and SDLD? And how can using them help ensure that your software is deployed on time and of the highest quality?

What is the Software Development Life Cycle (SDLC)?

As mentioned, the SDLC is a methodology with well-defined processes that allows developers and DevOps teams to create quality software (usually within a short timeframe). To make things easier for developers, the SDLC breaks the development process into six main phases:

  • Requirement analysis.
  • Planning phase.
  • Software design incorporating features like architectural design.
  • Software development.
  • Testing.
  • Deployment.

As well as the six phases outlined above, there are various SDLC models such as the waterfall model, spiral model, iterative model, or the agile model. Developers will utilize different models according to their needs and the type of software they are developing. For example, the model used to develop software for a hosted VoIP service might be different from that used for inventory management software. 

In cases like eCommerce platforms, incorporating eCommerce analytics into the development process can enhance user experience and provide valuable business insights. This flexibility allows developers to choose the most effective approach for the specific requirements of their project.

What is a Software Development Lifecycle Diagram?

A software development lifecycle diagram is simply a pictorial representation of the particular SDLC that may apply to the software you are developing. It breaks the cycle down into the steps you need to take to successfully develop a product and can act as a checklist; complete one step before moving on to the next. 

The stages of your SDLC/SDLD

If you’re developing a new piece of software, there are many things to consider before you even move to designing and coding it. Is the software standalone or will it be added to a suite of existing applications? If so, does it need to fit with an application portfolio management APM system that’s already in place? 

Your starting point is always going to be purpose. What will the software be used for and what are the users’ requirements?

1. Analyze user requirements

Before you even start planning the software, you need to understand what it’s for and what features it needs to have. Is it to solve problems or fulfill wants? You need to get input from all relevant stakeholders and that can include everyone from customers to programmers.

For example, a business may be migrating legacy applications and some of the apps or software may not be compatible with the new system. You will need to be aware of any improvements that need to be made as well as any security risks the software may face. 

2. Planning

Once you know what is required from the software in the previous phase, you can move to creating a detailed plan. In this step, your DevOps team is going to look at various factors such as cost estimates for developing the software and what resources will be required. 

One major consideration at this stage is security. What sort of data will the software be handling, are there any regulatory requirements such as the CCPA, and what vulnerabilities may exist? 

In this stage, it is also crucial to consider the database concepts that will underpin the software, ensuring data integrity, scalability, and performance meet the anticipated needs.

A comprehensive security assessment should be conducted to identify and mitigate potential security risks before proceeding further in the development process. You need to be sure that every base is covered in the planning stage to prevent issues from arising later. 

3. Design phase

Now that you know that the plan is feasible and what resources are needed, you can start designing the software. You should be letting all stakeholders review your design specification to ensure it’s ticking all their boxes and so that they can give you feedback and offer any suggestions as to changes that may be needed. 

It’s essential that you have this feedback – and that you listen to it – as failure to do so could lead to exceeding planned costs or even project failure. If a stakeholder identifies that you are missing something from lead enrichment software, for example, not taking that feedback on board and rectifying any omission could mean the project is doomed. 

Ensuring all necessary features and functionalities are included and aligned with stakeholder requirements is critical for the success of the project.

4. Build it and they shall come

You have a roadmap of the software you are developing so can now move on to the actual building of the product. If your DevOps team is working from the same location, it should be fairly easy to stick to the blueprint and follow any guidelines you have established. If some of the team are working remotely, then you will need to ensure you have good communication tools that will foster collaboration. 

You will need to establish guidelines as to what code style you will use and what practices to follow. Have a set (or variable) naming practice for your files so that every member of your team can write code that is consistent and well-organized and will be easier for you to test during the next stage. 

5. Testing one two

Before you even think about deployment, you need to be sure that everything works as intended. Testing at this stage should also be looking for any defects and any potential security vulnerabilities as well as integration testing. You may operate a test-driven environment and do all your testing in-house or, especially for larger projects, you may choose to have external beta testers.

There are various types of beta testing that can allow you to focus on special features such as headless CMS or test the product’s overall functionality. Testing different aspects of your software is essential as identifying and fixing any problems prior to deployment can save a lot of headaches later. 

6. Ready, steady, go

Once the testing process is complete – and any problems rectified – you’re ready to deploy the software. During the deployment phase, utilizing a cloud computer can be crucial for ensuring the scalability and reliability of the software. This allows for seamless access by end users from various locations, enhancing the overall deployment strategy and user experience

This could be to consumers as a purchasable software product or as part of the apps supporting a business capabilities model. Even with the most rigorous testing, your DevOps team should be monitoring use and any feedback that comes from end users as to whether the product met customer expectations or not. 

The thing to remember here is that while testing may have a small pool of testers, actual deployment will have a pool of thousands of users if not more. In an ideal scenario, your software will be deployed with no problems but the reality is that you will probably expect some issues that are hopefully only minor ones. 

Software development lifecycle security

Security is always going to be a primary concern when developing and deploying software. You need to be aware of the answers to several questions such as ‘What sort of data will the software be handling’ or ‘What is enterprise architecture management and what role will the software play in it?’

With SDLC, and your SDLD, security should not be seen as a separate stage but as an integral part of the process that is involved in every stage through DevSecOps practices. This methodology assesses security throughout the development process and looks at how secure the various features of the software are and how well it can stand up to potential threats once deployed.

These assessments can include tasks such as analysis of the architecture, automated detection, penetration testing, and code review. Your assessments should be part of the integrated development environment (IDE), servers used for the build, and code repositories. You should be looking to integrate DevSecOps into your SDLC in the following ways:

  • Planning analysis: In this stage, you should be identifying any security needs, mitigation plans, and potential threats the software may face.
  • Design. During the design stage, think about what features will meet your security needs. You could utilize threat modeling and a risk analysis of your planned architecture. You could also consider features such as encryption mechanisms and access control. 
  • Development and testing. You should be carrying out code reviews to ensure that any code meets your standards and that the security measures are implemented. During the testing phase, carry out tests such as penetration testing to identify any vulnerabilities. 
  • Deployment. There are automated DevSecOps tools that can help ensure and improve app security. You should also be looking at other factors such as access controls, firewalls, and security settings. 
  • Maintenance. Cybercriminals are constantly looking for new ways to infiltrate software and systems. Cybersecurity experts need to be just as proactive in finding ways to stop attacks. If any new risks or vulnerabilities arise, have your team look at any required updates or patches. 

The takeaway

Every software project is important, whether it’s for a gaming app or a commercial application. Both simple and complex projects should follow the SDLC and SDLD process. Your end goal is always customer satisfaction and to avoid as many errors as possible, especially when it comes to security. 

Having a software development lifecycle diagram to guide you through every development project can ensure that you follow all required steps and adhere to the relevant best practices. You should always be aware of the most common errors and be aiming for consistency from all of your development team. Keep to the plan and you’ll have a quality product and happy customers. 

Bio:

Diana Nechita – Director of Product Marketing

Diana is the Director of Product Marketing at Ardoq. Her passion lies in fostering a deep understanding of Ardoq’s value in delivering tangible results for organizations navigating the complexities of digital transformation. This is her LinkedIn.

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Generative AI and Its Evolving Role in Software Development

Remember the days when software development was solely the domain of humans painstakingly writing lines of code? Those days are evolving rapidly. Generative AI, a branch of artificial intelligence capable of creating original content, is quickly becoming the co-pilot for software developers worldwide. This technology, leveraging advanced models like GPT-4, is not only automating mundane tasks but is also opening doors to unprecedented creativity and efficiency in the software development lifecycle.

 The Rise of AI Coding Companions

Generative AI models, like OpenAI’s ChatGPT or GitHub’s Copilot, have emerged as powerful allies for developers. These models can:

  • Generate Code: Need a function to sort a list? Just describe what you need, and the AI can generate the code for you.
  • Complete Code: Start typing a line of code, and the AI can suggest how to finish it, saving you keystrokes and brainpower.
  • Refactor Code: Want to clean up or optimize your code? The AI can suggest improvements.
  • Explain Code: Encountered a complex piece of code? Ask the AI to break it down for you in simple terms.
  • Detect Bugs: The AI can scan your code for potential bugs and suggest fixes, reducing the time spent on debugging.

Boosting Productivity and Creativity

The benefits of these AI coding companions are clear:

  • Increased Efficiency: Developers can accomplish tasks much faster, from writing boilerplate code to debugging complex issues.
  • Enhanced Creativity: The AI can offer alternative solutions or suggest innovative approaches, sparking new ideas.
  • Reduced Tedium: Developers can focus on higher-level problem-solving instead of getting bogged down in repetitive tasks.
  • Accelerated Learning: Newcomers can learn faster by getting instant feedback and explanations from the AI.

Real-World Applications

Generative AI isn’t just a theoretical concept; it’s already being used in the real world:

  • GitHub Copilot: This popular tool integrates with various code editors, providing code suggestions and completions in real time.
  • Tabnine: Another AI-powered code completion tool that supports multiple languages and frameworks.
  • Replit Ghostwriter: This tool offers AI-powered code generation, completion, and transformation features.
  • Deep TabNine: A deep learning-based code completion tool that can be integrated with various IDEs and text editors.

Challenges and Considerations

While the potential of generative AI is exciting, there are also challenges to address:

  • Accuracy: AI-generated code might not always be perfect and may require human review and correction. While these tools can significantly speed up the coding process, developers should still verify the AI’s suggestions to ensure they meet required standards and project specifications.
  • Bias: AI models can inherit biases from their training data, leading to potentially biased or unfair code suggestions. This requires developers to remain vigilant and critically assess any suggestions made, ensuring equitable and inclusive coding practices.
  • Security: The security of code generated by AI needs careful consideration to prevent vulnerabilities. Developers must be aware of potential security gaps and rigorously test AI-generated code to protect against cyber threats and maintain the integrity of their applications.
  • Ethics: As with any technology, the ethical implications of AI in coding should be carefully evaluated and addressed. This entails considering the broader impact of AI-generated solutions and ensuring that their use aligns with ethical standards and promotes positive societal outcomes.

The Future of AI-Assisted Development

The future of AI software development services is undoubtedly intertwined with generative AI. As these models continue to improve, we can expect even more sophisticated tools that will:

  • Understand Natural Language Better: Allowing developers to communicate with AI in a more intuitive way. As natural language processing capabilities advance, developers will be able to describe the functionality they need in plain English, and the AI will generate the corresponding code, reducing the need for detailed programming knowledge.
  • Generate More Complex Code: Tackling larger, more complex programming tasks. Future AI models will be capable of handling intricate logic, cross-functional dependencies, and larger codebases, thus enabling the automation of more sophisticated software projects.
  • Integrate with More Development Tools: Becoming a seamless part of the developer’s workflow. As generative AI tools continue to evolve, their integration with a wider range of development environments, version control systems, and project management tools will ensure a smoother and more cohesive development experience.

The Developer Nation Survey, a comprehensive look at developer trends, already highlights a growing interest in AI tools for coding. This indicates a shift in how developers perceive and use AI, moving from skepticism to embracing its potential.

Conclusion

Generative AI is a game-changer for software development, offering a glimpse into a future where humans and AI collaborate to create more efficient, innovative, and secure software. While challenges remain, the potential benefits are too significant to ignore. As we move forward, developers who embrace these AI-powered tools will be well-positioned to thrive in the ever-evolving landscape of software development.

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Embedded Software Development

Embedded Software Development

Embedded software refers to computer programs designed to perform specific functions in systems or on hardware devices that are not traditionally considered personal computers. Embedded software is found in various electronic items and plays a critical role in the functioning of modern-day technology. These necessary systems are found in simpler appliances, such as thermostats and security cameras, as well as more complex systems like medical equipment, point of sale (bank card) terminals, automobiles, and aeroplanes.

Embedded software developers are traditionally one of the smallest software development groups. As of Q1 2023, we at SlashData estimate that developers self-identifying as embedded developers comprise only around 5% of developers worldwide, a proportion that has been relatively stable for the last two years. Despite embedded developers’ modest community size, they can be found across the globe, with the largest two population clusters being North America and Western Europe – with 18% of embedded software developers in each of the respective regions. The next highest regional group is the Middle East and Africa, which collectively accounts for 15%. 

Analysing data collected from more than 25,000 developers working in 160+ countries, we delve into the lives of embedded software developers. These developers are responsible for how humans interface with many critical technologies. Hence, understanding the landscape of those building and developing embedded systems can offer salient insight into industry trends and orient companies and developers alike as to where the field is heading. 

Embedded systems and data processing

In 2022 we noted an almost 100% increase in the number of embedded developers who describe data science or data analysis as a part of their role compared to 2021. In our most recent global developer survey, around a third of embedded software developers described their role as having a data science or data analysis component. 

Data processing and analysis are becoming more intertwined with embedded systems. One contributing factor is the growth in the number of Internet of Things (IoT) devices. Embedded software is an essential operating component of IoT devices, and as the number of IoT devices continues to multiply, managing, processing, and understanding the vast amounts of data accompanying this growth is a key challenge. Many embedded system developers appear to have recognised this trend and either adapted their roles or had their roles adapted for them to include this necessary data analysis and handling component. 

What are embedded developers working on, and what markets are they targeting?

In order to better understand the embedded developer landscape, we asked developers working in this field to describe the projects they had worked on in the last six months. We find that the most common embedded software project description is “network-connected”, mentioned by 41% of developers, or “internet-connected” (36%). Both categories are fundamental to IoT devices. ​

Furthermore, 35% of embedded software developers stated they had worked on projects that processed data, while 30% stated their projects involved data storage. This project reporting again highlights the importance of data management in the embedded software development field and reinforces the importance of data analysis and processing as part of an embedded developer’s tool kit. 

“23% of embedded developers have recently worked on projects that involved signal processing. They have, on average, 24% more experience compared to embedded developers working on other projects” 

Around a third of the projects embedded software developers worked on recently involved sensor or monitoring devices. Meanwhile, 23% of embedded developers have recently worked on projects that involved signal processing – audio, video, etc. As these types of technologies that interface with their local environment continue to evolve, they will increasingly shape how humans interact with devices and their surroundings.

Embedded developers working on sensors and monitoring and signal processing technologies have, on average, 24% more software development experience than embedded developers working on other projects (an average of 5.8 years vs 7.6 years). Embedded technologies that incorporate signal processing require complex algorithms that can be computationally intensive and require specialised knowledge. This increased knowledge requirement is reflected in the additional software development experience embedded developers working with these technologies have. ​

In addition to being slightly more experienced, embedded developers working on sensory projects utilise the C programming language significantly more than other embedded developers. Embedded developers working on sensory projects use the C language more than half the time, 54%, compared to 40% of other embedded developers. We believe the inflated use of C here is likely due to its efficiency and popularity in the field of signal processing data. 

Where is embedded software used?

Embedded software is utilised in an array of devices and for various applications. Hence, the markets that embedded software developers target are as diverse as the features the embedded software provides. We find that the most popular market is smart home appliances, targeted by 30% of embedded developers. As many IoT devices are increasingly sought after and can be found in the home – refrigerators, washing machines, doorbell cameras, etc. – this category’s lead aligns with our market observations. 

“30% of embedded software developers are targeting smart home appliances – where many IoT devices are traditionally found” 

Robotics comes in second place, with 24% of embedded developers reporting that they are targeting this market with their projects. The field of robotics heavily relies on embedded software to control movement, sensors, and environmental information processing – all crucial components for a robot’s functionality. As technology continues to advance, the field of robotics and embedded software will become even more intertwined in the development of intelligent systems that can be of benefit to various commercial markets such as manufacturing, transportation, and defence. 

Embedded Software Development

Embedded software developers are at the forefront of how humans interface with many technologies. With the increasing growth in the number of IoT devices, an increased number of devices will be connected to the internet and through networks and require embedded software to operate. This will necessitate embedded software developers to handle new demands in their workflow. We expect that these increasing expectations to come from both companies and consumers – such as the ability to process and analyse data and increased demand for device internet/network connectivity in IoT, respectively – will continue to push embedded developers to further broaden their skillset to be successful in keeping up with market requirements.