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Salaries Tips

How well-paid do developers feel?

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Studying wages and compensation can offer insights into the supply and demand of various skill sets in an employment market. Despite recent layoffs across many technology companies, our data indicate that the number of professional developers across the globe continues to rise. A driving factor in the persistent growth of developers worldwide is that modern enterprises recognise the fact that, as technology becomes increasingly intertwined with society, all companies are or will ultimately become technology companies. 

This means that grocery store chains, online commerce platforms, and car manufacturers alike must all compete against one another to attract developers. Compensation is one of the principal means used to vie for said talent. Understanding the compensation landscape for developers can help companies make informed decisions about salary, bonuses, equity, and other benefits they offer to attract and retain skilled developers. Likewise, studying compensation can aid developers in making decisions about their own careers, including negotiating salaries and benefits. 

In this chapter, we present findings from SlashData’s latest Developer Nation survey – the 24th edition – exploring developers’ compensation patterns. We look at differences across regions and note how developers and companies alike, when negotiating compensation, need to take into account differences in costs of living and expenses. Further, we will examine developers’ self-perceptions regarding their salaries and what factors are associated with believing that they are under or overpaid. 

The compensation landscape for professional developers varies greatly across the globe. In our latest survey, we collected information from developers living in more than 160 countries across the globe. As expected, the distribution of reported annual compensation reflects the diversity of respondents and the myriad of personal situations. 

According to our data, 9% of professional developers earn less than $1,000 per year in total compensation – including base salary, bonuses, stock options, and other perks. This group encapsulates many of the developers working part-time, starting off their careers in internships, or working on commission. As expected, reported annual compensation is significantly correlated with overall experience in software development. Hence, as developers gain experience, they are able to command higher compensation. When we control for the differences across the globe, we find that, on average, for every year of experience a developer gains in software development, they earn nearly $4,000 more each year. 

On the upper end of the spectrum, we find that roughly 6% of professional developers earn more than $200,000 per year. According to the World Inequality Database, in almost every country in the world, workers earning above $200K a year belong to the top 1% of earners in that country. This is one indicator that developers’ average compensation is higher than in other sectors of the economy. Below, we break down the average compensation by region to offer a bit more context to the earnings of developers.

Regional differences

North American professional developers report the highest average annual compensation – more than $100,000. The median compensation in the region, however, is closer to $75,000. Meanwhile, on the opposite end of the spectrum, developers working in South Asia report the lowest average compensation of just under $27,000 and the median compensation is around $5,500 per year. As is frequently the case with compensation, those with higher earnings greatly inflate the average, as is evident when we compare the median vs the average annual compensation. 

Anyone who has travelled outside their hometown recognises that the costs of goods and services can vary depending on where you are in the world. Compensation very often reflects these differences in the cost of living. Should developers and companies wish to compare compensation between two locales, considering these differences is crucial. 

As an example, we examine two countries with large developer populations: the United States of America and the People’s Republic of China. The median compensation of developers in the USA is around $75,000 per year. This is five times greater than the median developer compensation in China of $15,000 per year. However, when we account for differences in costs of living using the purchasing power parity index, we see that the average developer in China earning $15,000 per year can afford similar goods and services as a developer in the USA earning $25,000 a year. In practical terms, this means that developers in the USA still generally enjoy a higher wage compared to Chinese developers, but by a lesser margin (3 times more vs 5 times more) than is apparent when we directly compare compensation. 

Perceptions surrounding compensation

On top of asking developers about their current annual compensation, we also asked them about the compensation they think would be fair for their role. Just over half (51%) believe that the compensation they currently receive is fair for their role. Meanwhile, 39% believe they are underpaid, whereas 11% of developers report that their current compensation was more than what they believe is fair for their role. 

To better understand what factors are associated with developers believing they are over or underpaid, we modelled developers’ sentiments about the compensation in their current role. We find that men are significantly more likely to report feeling underpaid in their current role. More specifically, 16% of men report feeling underpaid compared to 11% of women and 14% of developers who identified as non-binary. Conversely, 7% of women feel overpaid compared to 4% of men and 1% of non-binary individuals.

We additionally see that developers with more experience and those working for larger companies are more likely to report feeling underpaid. For each additional year that a developer gains in experience, we estimate that there is approximately a 7% increase in the odds that the developer will report feeling underpaid compared to fairly compensated. This suggests that companies do not financially value experience to the same degree as developers do amongst themselves. 

However, more experience and working for a larger company are both correlated with being compensated higher. This could indicate that more experienced developers working at larger companies have responsibilities that they feel are not commensurate with their compensation. On the other hand, sentiments of being underpaid could also stem from a perception based on a lack of information, being influenced by larger companies’ generally greater profit margins, or unrealistic thinking from the developer. 

Finally, if a developer has an undergraduate degree in software engineering, they are more likely to report feeling underpaid. The odds of a developer with an undergraduate degree in software engineering feeling underpaid vs paid fairly, are 9% greater when compared to all other developers. This effect disappears, however, once developers have a postgraduate degree; as having a postgraduate degree increases the odds of feeling overpaid by 50% compared to not having a postgraduate degree.

This could indicate that companies place a lesser value on undergraduate education than developers perceive they will; possibly leading to the sentiment of feeling underpaid by those who do not yet hold advanced degrees. Other external factors, such as geographical location, also affect how a developer perceives their compensation, likely due to cost of living differences, as discussed in the previous section. 

Compensation is often considered a difficult topic to discuss and research due to the taboo nature of discussing money in many companies and cultures. Our aim with this chapter is to open up the conversation surrounding developer compensation with our analysis.

Do you like data such as the above?

If you’re a professional or hobbyist developer into Web, Mobile, Desktop, Cloud, Industrial IoT, Consumer Electronics, Embedded Software, AR & VR, Apps/extensions for 3rd-party ecosystems, Games, Machine Learning & AI, and Data science, we would like to hear your voice.

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Tips Tools

Artificial Intelligence Developer Toolkit: Essential Tools and Frameworks for AI Development

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Artificial Intelligence has been around for a long time. People have studied it and have made progress but it’s only in recent years that people have started to recognize how AI is being used. Research on AI shows how much it can affect different industries in the years to come.

AI can be used correctly to enhance the human experience as a whole. AI is used for automation so that processes that are tedious and repetitive can be done easily. Automation is needed by different industries. Whether it is businesses that are focused on healthcare or those who are running their Ecommerce website, automation is going to play a huge role in how consumers will use apps and purchase other needed items.

Importance of Essential Tools and Frameworks for AI Development

An AI engineer can work better with the right tools and frameworks to develop the right apps that people will love. People from various industries who would like to start an AI project should understand that there are always different requirements that are needed. Some tools and frameworks can work well together depending on the project being created.

A wide variety of tools and frameworks are thoroughly discussed by other professionals on the internet. They are available in forums, on some websites, and even through videos. An artificial intelligence developer will have no issues in trying to find the right information for a project.

Data Preparation and Preprocessing

If there is one thing that is crucial in data preparation and processing, it is data quality. Machines are created by people and they will not work unless people have set them up very well. It has always been said that people are only as good as their mentors. If they want to become better, they need experience, they need more than one mentor, and they need different inputs and outputs. 

This is the same with the machines for AI. The data quality can highly impact the reliability of the machines. The accuracy and performance of the machine will only be as good as the people who are making the machines work. Artificial intelligence programmers are aware of this fact. This is why they always make an effort to provide a diverse and quality dataset to the machines.

Tools and Techniques for Data Cleaning, Transformation, and Feature Engineering

Good data and bad data can help businesses. Recognizing the bad data can eliminate the extra work that will be done to create the right dataset for any industry. Data cleaning is not people’s favorite activity but it is required to create the right type of data for the project.

It is through data cleaning that bad data can be weeded out from the dataset. Once this is figured out, the issues will be properly corrected. Some data can be considered unfixable which means that they need to be removed.

The usual reasons for unclean or bad data are the following:

  • Human error
  • Getting scrap data
  • Combining data from different sources

The use of bad data can make businesses spend more money than they should which is why it should be eradicated.

To do effective data cleaning, transformation, and engineering, these are the techniques to do:

  • Get rid of duplicates – You can get duplicated entries when you get information from different sources. Get rid of duplicates so that you will not skew the results.
  • Get rid of irrelevant data – Data that is not important to the project will only slow down the results. Remove things that will add nothing to the data that you have.
  • Make sure that text is consistent – One way that you can do this is by standardizing capitalization. Those who can also do NLP labeling can help with this.
  • Clear formatting – Most machines are unable to read data accurately if the data is heavily formatted. You may be dealing with different formats especially if you have gotten your data from various sources. Clear formatting and the data will be read smoothly.
  • Carefully remove errors from the data – This should be done to get a reliable dataset. Key findings might become hard to see if you do not clean the errors immediately.

Machine Learning and Deep Learning Frameworks

Machine learning is continuously growing and it can provide the smart solutions that businesses from different industries need. The frameworks can be understood better because of the libraries, interfaces, and tools that are available for people to view and study.

TensorFlow

This is created by Google’s Brain Team and it can be used for Python. It uses dataflow graphs to create and process data. This is preferred by those who do AI development because the learning models are easy to build. It can also be used for powerful research and experimentation.

PyTorch

This is a framework created by Facebook’s AI Research Lab also known as FAIR. This can be used for different libraries such as Python and C++. The framework is designed to be scaled and improved so that it can become more flexible depending on the project that you are making. This is best for people who are already familiar with C and C++ as there are some similarities.

Scikit-Learn

This is an open-source data analysis library which is usually one of the first choices when people want to do machine learning for Python. This can be helpful for data that needs to be segmented depending on the algorithm. It will also have the ability to recognize data based on the patterns that it shows. 

Natural Language Processing Tools

NLP tools and techniques are very helpful for AI as they can make AI more accurate. The process can also be done in a faster time as compared to not using the right NLP tools. NLP allows applications to do more every day. People can also gain more every day because of this. The more that technology improves, the more sophisticated the algorithms that become available.

Essential NLP Libraries and Tools

People who are searching for IT jobs in Germany usually try to increase the number of skills that they have. Still, they cannot just rely on their skills. They need to make an effort to learn more about the libraries and tools that they can use.

  • Natural Language Toolkit (NLTK) – This is a library that supports various tasks from text segmentation to semantic reasoning in Python. This is the main tool that professionals use for NLP and machine learning.
  • TextBlob – This is the tool that most beginners use when they want to make better experiences while still exploring Python and NLTK. This can help design people’s prototypes.
  • Core NLP – This is one of the tools that can be used when you are using Java. It is required that you have Java installed on your device before you can use this for different processes like sentiment analysis and part-of-speech tagger.

Model Evaluation and Deployment

How sure are you that your machine is providing the type of data that you are looking for? You need AI development services from a trusted company or professionals. They should know the different techniques to check the accuracy of the AI model that has been created.

Accuracy

This is the most widely used metric for model evaluation. This will show you the ratio between the corrected values and the data that you have placed on the machine. This will also show you if the classes that you are trying to analyze are imbalanced.

Precision

This will provide the percentage of the predicted positive instances. This will let you know if the model is giving you information about how right the machine is when it says that it can accurately read the data.

Specificity

The percentage of the negative instances are being measured against the actual total of the negative instances. This can be the most effective if you want to measure the actual number of people who have indicated negatives in the data set vs what the machine says is the percentage of negative instances.

Machine learning is not something new. An artificial intelligence development company can offer professionals who can use the right tools to deploy AI and machine learning easily. People have already learned a lot of details about it and it is expected to become more accurate in the years to come. 

Different machine learning tools might become steeper for beginners especially if they do not know anything about the processes. The sheer determination of people can weed out those who can become good at it.

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News and Resources

On the role of female coders in software development

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Since the beginning of computing, women have consistently played a pivotal role in software development that has frequently been overlooked; from Ada Lovelace developing the first algorithms for modern computers to Margaret Hamilton’s crucial role in the development of on-board guidance software for NASA’s Apollo program.

However, despite women’s exceptional contributions to the field, they have often received less credit than their male counterparts, and their place in the field is questioned.

Today, efforts are being made across the software development ecosystem to address these historical biases. While efforts have been made to promote women to get involved in the historically male-dominated field, there is still considerable work to be done. 

Data and technology are not free from bias. Past applications and software development projects have demonstrated the need for input from diverse groups2.

In this chapter, we specifically explore the involvement of women in software development. According to our latest global developer survey (Q1 2023), nearly a quarter of all developers (22%) self-identify as females, the highest proportion since we began asking respondents about their gender.

This is a small increase from two years ago, since Q1 2021, when female coders accounted for 19% of all developers. 

This slight increase in the proportion of developers self-identifying as females can be partially attributed to the rise in the representation of women among early-to-mid-career developers. Women currently make up a quarter (25%) of developers between the ages of 25 and 34, the highest proportion of all age groups, up from less than 20% in Q1 2021.

This is followed closely by 23% of developers between the ages of 18 and 24. The highest proportion of women falling within the 25-34 age bracket indicates the possible beginning of a positive trend for the future of women in the tech industry. This is the age when people begin to settle into their careers and is a point where people are likely to develop additional skills that allow them to cross-train and enter industries of their choosing.

Further to this, we are also seeing an increasing presence of women in certain regions that are leading to an increase in the proportion of women in technology overall.

Specific highlights include the Middle East and Africa, where the proportion of women in technology in this region has gone from 10% in Q1 2021 to over 20% currently. Similarly, women made up 15% of developers in East Asia in Q1 2021 and now makeup almost 30% of developers. 

Overall, a higher representation of women in the software development ecosystem is a great development. Not only do they bring critical perspectives and approaches to the work being undertaken, but diversity in the workforce offers fresh experiences that can help businesses address underserved needs.

It also enhances efforts to make spaces that are less hostile to women in both overt and subtle ways, allowing even more women to follow their interests in the technology space.

The proportion of women among developers varies substantially depending on the types of projects they are involved in. Virtual reality (VR) and augmented reality (AR) projects have the highest proportion of women, at 33% and 28% respectively, followed by games (28%). 

On the contrary, backend services and web application projects have the lowest concentration of female coders, at just 13% and 16%, respectively.

With these sectors selecting those with formal degrees at higher rates than other areas, and a 10 percentage point difference between men and women having such a degree, this may be one factor in the lower presence of women.

Undergraduate degrees in computer science or equivalent are held by 45% of backend developers and 43% of web developers, compared to 37% of all developers.

Further, the lower proportion of women working in backend services and web application development may, in part, be attributed to the historically male-dominated culture within these sectors. Addressing cultural differences3 and fostering a more inclusive atmosphere can contribute to balancing representation and mentorship opportunities within these sectors.

Further, there may be potential unconscious biases in hiring practices derived from existing workplace culture, which may prevent certain development areas from harnessing the full spectrum of talent, and benefit from the input of individuals with diverse backgrounds.

Examining the sizes of organisations that female developers work for throughout various stages of their life and career could indicate that company characteristics have an influence on women’s decisions in the technology sector.

Like young men, young women are more likely to work as freelancers relative to other age groups and only return to similar proportions among developers aged 55 and above. Additionally, younger female developers (18-24) tend to work for smaller companies, whereas older female developers (45+) are more inclined to work for larger organisations with over 10,000 employees.

Examining a particular age group, women between the age of 35 and 44, may offer an insight into issues women have with progressing through their careers. Previous research into women’s careers in the software development sector has highlighted that women are promoted at a lower rate than men4.

However, when looking at the roles women self-identify with, we find that at mid-market companies (251-1,000 employees) and enterprises (1,001-10,000 employees) the percentage of women in management positions (20% and 29%) is significantly higher than at other organisation sizes (13% on average).

These organisations could offer better opportunities for career growth, decision-making, and leadership. In larger companies, management roles might be more hierarchical and bureaucratic, leading to less autonomy and slower career progression.

In smaller companies, limited opportunities due to their size might result in fewer leadership positions being available overall, and with women being a minority in software development, there are fewer women in leadership positions.

Further, there is an underrepresentation of women in certain leadership roles. 11% of men list their role as CIO, CTO, or IT manager, and 14% identify as technical team leads, compared to just 9% and 8% of women. This could create a cycle whereby there may be fewer mentorship opportunities for other women.

When there are fewer female leaders, it has been found in a range of fields5 that it can be harder for women to progress in their careers, and it can be more challenging for aspiring women to find mentors who can guide them, provide valuable insights, and help them navigate their career paths. 

However, while still a minority of those in such roles, 25% of those in CEO or management positions are women, compared to their position as 22% of the developer population.

While only a small percentage difference, given their underrepresentation in other leadership roles, this represents an area where women are getting leadership positions. Among the previously discussed issues women may face, women are also less likely to apply for leadership positions where they do not fulfil all of the requirements than men6.

This may be leading women to also self-select towards management positions that are not solely dependent on technical skills. 

The observation that women hold a higher proportion of CEO/management roles compared to men (7% against 5%, respectively), particularly in companies with more than 250 employees (8% of women to 4% of men), could indicate a positive shift in gender representation and diversity in leadership positions.

This trend might be driven by a changing corporate culture that is increasingly recognising the importance of gender diversity in leadership, leading companies to seek out and promote women into these roles6 proactively.

Embracing diverse perspectives at the decision-making level can result in better organisational performance and decision-making.

Another factor that may contribute to this observation is the growing appreciation for women’s leadership styles, which tend to be more collaborative, participative, and relationship-oriented. These qualities are often valued in today’s business environment and might make women particularly well-suited for CEO/management roles.

Moreover, women, through their skills and abilities, are likely actively contributing to this positive trend, demonstrating that they are well-equipped for leadership roles. Despite women remaining a minority in leadership this growing representation in CEO/management roles is a step in the right direction, highlighting the benefits of diverse and inclusive leadership.

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Community

Introducing Developer Nation forums

developer nation forums

It’s been a while (read 3 months) since we launched the Official Developer Nation forum along with our revamped website. But we have yet to make a formal announcement about it. This blog will serve as an official bulletin illustrating our rationale behind launching a self-hosted forum for Developer Nation.

Being a global community, we are aware that developers worldwide depend on us and each other for support, sharing ideas, collaboration, and diverse perspectives to make informed decisions in their programming journey. However, we didn’t want to hastily create yet another Discord server or Slack workspace without careful consideration, which could result in unanswered queries and inactivity.

How we support our global developer community 

We have been providing extensive assistance to our community members via email for a considerable period. Our support ranges from answering their questions, and connecting them with relevant individuals in the community, to sharing developer market research reports upon request from our vast data repository. We also extend it to help them with job hunting, among other things. However, we noticed all this happening behind closed doors, isolated from the rest of the community, for no reason but a lack of an open platform. Even if another developer had a similar query or request, they could not benefit from previous conversation flows with other members. Therefore, we decided to take this to the next level by providing the community with a platform to collaborate openly and benefit from the conversations other members are having.

Announcing Developer Nation Forums

Developer Nation forums are our discourse server which can be accessed at: https://forum.developernation.net/, now without actually telling you what you can do here, I would highly encourage you to check it out yourself and consider this our community playground where nothing is wrong, and everything posted is regarded as a healthy flow of conversations within the community. We’ve created the categories we see fit (for now), but this is ever-evolving as we receive community feedback. 

One of the key goals of creating our own forums is to help our community self-serve themselves; that means once the forums have significant conversations, the chances of you finding an answer to your query increase by many folds. Thus, new members can better navigate our community and surveys and get support without needing to reach out to us personally.

Apart from creating your own threads and participating in conversations started by other community members, you can customise the look and feel from dark to light mode. Feel free to explore more and share your feedback with me on how we can make it better and more inclusive for everyone. I believe our community members will generously help each other on the forums and make it a sustainable healthy hangout place for all the members.

P.S: Since every member of the Developer Nation community team, including me, spends time on forums every day, the chances of your query being addressed are relatively high there.

I’ve created this short video as a quick crash course on using the forums for the first time; check it out, and I look forward to welcoming you there. Cheers!

Join forums here

– Ayan

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News and Resources

AI-Powered Predictive Analytics: A New Era in Project Estimation and Planning for Software Development

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Software development projects are like puzzles with countless pieces that must come together seamlessly. But one of the biggest challenges in this process is estimating and planning the project accurately. It’s a bit like trying to predict the future-a daunting task. However, with the introduction of AI-powered predictive analytics and the emergence of AI-based project management tools, a new era of software development project estimation and planning has begun.

Understand the terms

Project estimation and planning

Project estimation and planning in software development involves predicting the project’s duration, effort, and resource requirements. Project managers and teams break down the project into smaller tasks, estimate the time and effort for each task, and create a timeline. They consider factors like team size, skills, and available resources. The goal is to set realistic expectations and use resources wisely. Good estimation and planning prevent surprises, delays, and extra costs. It’s about understanding what needs to be done, how long it will take, and what resources are necessary for success.

Most of the time, the estimation process would cost the company significant money and time at the start of developing a brand-new website, app, or software.

AI-powered predictive analytics

AI algorithms can predict future events or behaviours by analyzing large amounts of data and identifying patterns. AI-powered predictive analytics in software development can estimate project timelines, identify risks, and optimize resource allocation. It enables us to make data-driven decisions and adjust plans as necessary. It’s all about using AI to predict and plan for the future based on insights from past data.

Project Estimation and Planning Before AI

Before the introduction of AI-powered predictive analytics, project estimation and planning in software development relied heavily on human expertise and historical data. Project managers and teams would analyze previous projects with similar characteristics and use their experience to estimate the effort, time, and resources required for the new project. The following are some notable challenges of traditional project estimation and planning.

Limited data insights

The amount of historical data available for analysis limited traditional methods. Estimates were frequently based on a few previous projects, which may not accurately represent the complexities of new projects.

Biases and assumptions

Estimates may be influenced by human biases and assumptions, resulting in overestimation or underestimation of effort and timelines. These biases may result from previous experiences or personal perspectives, affecting the accuracy of estimations.

Identifying risks

Another challenge was anticipating potential risks and challenges early in the planning process. Due to the lack of comprehensive data analysis capabilities, project managers relied on their intuition and experience, which may have covered only some potential risks.

Adaptability and optimization

Traditional methods lacked the flexibility to adjust estimates and plans as the project progressed. Real-time data integration was limited, preventing optimal decision-making and resource allocation based on changing project needs.

Enter: AI-Powered Predictive Analytics

The implementation of AI-powered predictive analytics has changed the process radically. By leveraging machine learning and data analysis, AI can analyze vast amounts of historical project data to identify patterns, trends, and correlations that humans might miss. Here are some of the ways AI transforms project estimation and planning:

Uncovering hidden insights

AI algorithms examine massive amounts of historical project data, detecting patterns, trends, and correlations humans may overlook. AI uncovers hidden insights that enable more accurate predictions by analyzing project variables such as scope, complexity, team size, and resource allocation.

Data-driven decision-making

Project managers and stakeholders can make data-driven decisions from the start with AI-powered predictive analytics. They gain insight into potential bottlenecks, allowing them to allocate resources better. AI provides realistic timelines, enabling stakeholders to set appropriate expectations and avoid overpromising or underdelivering.

Effective risk management

AI identifies potential risks early on by analyzing historical project data. It identifies factors that have historically resulted in delays or cost overruns. With this information, project managers can proactively mitigate risks and develop contingency plans, resulting in more efficient project execution.

Continuous improvement

AI algorithms learn from real-time project data, adapting and refining their predictions. AI provides valuable insights as projects progress, allowing teams to course-correct, make data-driven decisions, and optimize resource allocation. Over time, this iterative learning process improves estimation accuracy.

Human-AI collaboration

It is critical to understand that AI-powered predictive analytics does not replace human expertise but supplements it. Project managers and stakeholders contribute valuable experience and domain knowledge. AI provides them with new insights, enhancing their decision-making abilities.

Final thoughts

AI-powered predictive analytics has transformed software development project estimation and planning. It enables project managers and teams to make more accurate predictions, optimize resource allocation, and manage risks more proactively. We can uncover hidden insights, make data-driven decisions, and adapt plans in real time by leveraging AI’s data analysis capabilities. 

This new era of project estimation and planning combines the best of human expertise with the power of artificial intelligence, resulting in more successful and efficient software development projects. We can expect even greater accuracy and efficiency in the future as AI technology advances, paving the way for continued innovation and growth in the software development industry.

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News and Resources

AI Spotlight: 63% of Developers Engage with AI-Assisted Development

63% developers engage with AI

You’re familiar with at least one AI-assisted development tool; That’s right, the ChatGPT. Its popularity has skyrocketed in the last few months and with good reason.

It is designed to assist users in generating human-like text but it’s been helpful to developers too, as they can leverage ChatGPT to automate certain tasks, generate code snippets, assist in writing documentation, or even prototype conversational interfaces. While ChatGPT is primarily a language model, it can be used in the development process to aid in various aspects of software development.

In SlashData’s  24th edition of State of Developer Nation, we asked developers if they use AI and how. This led to a dedicated chapter on all the new technologies that captivate developers’ imaginations.

The data from the survey suggest that 63% of developers engaged in some aspect of AI-assisted development, making it evident that this technology is rapidly maturing and transforming from a mere trend to a valuable tool.

AI-Assisted Development: A Growing Trend

While overall engagement has experienced a slight decline of 4% over the past year, the nature of developer involvement has undergone a fascinating shift.

More developers are actively working on or learning about AI-assisted development, showing a 6% increase in engagement. 

Simultaneously, the number of developers with latent interest has decreased by 6%.

This dynamic suggests that AI-assisted development is maturing and gaining practical applicability in the development landscape.

Generative AI: Unleashing Creative Possibilities

Alongside AI-assisted development, generative AI has emerged as a new and exciting technology. 

With 57% of developers actively involved or interested in generative AI, curiosity and excitement abound. While AI-assisted development still leads in adoption at 17%, generative AI projects attract 14% of engaged developers.

The Many Uses of Generative AI

Developers use generative AI in three main ways: 

  • as a helpful tool for their development process
  • by integrating it into projects through APIs
  • or even by creating the models themselves.

Ongoing investigations are exploring these usage patterns to uncover more insights into this groundbreaking technology.

Challenges and Opportunities

Although generative AI is gaining high engagement, there are factors that affect its adoption among developers.

Some developers may be hesitant to rely solely on generative models for critical or security-conscious tasks. 

However, there is a growing adoption of generative AI for visual assets in software development, which reduces the risks of errors and security vulnerabilities.

Overcoming Challenges

Developers who work on generative AI models face the challenge of needing a large amount of training data. 

However, certain tools offer the ability to fine-tune pre-trained models for specific tasks, making this challenge easier to overcome. As developers become more familiar with assistive and generative AI technologies, we can expect a surge in their adoption, leading to innovation and creativity.

Leadership’s Role

Interestingly, leaders in C-suite and other leadership positions show higher engagement rates with emerging technologies. 

About 49% and 50% of those who approve tool expenses or budgets are actively involved in AI-assisted development. 

This trend suggests that the revolution in AI-assisted development is driven by leaders who recognize its potential.

Looking Ahead: The Changing Landscape:

When we take a broader view, we see a cyclical pattern in the adoption and interest in emerging technologies. Developer interest has dropped by 5% overall, while adoption has increased by 4 percentage points. 

This contrast indicates a dynamic shift in developer preferences, marking a change from previous trends.

In summary, AI-assisted development is rapidly evolving and attracting developers’ attention. Generative AI opens up exciting possibilities, and leadership engagement plays a crucial role in driving its growth. Cryptocurrencies continue to be intriguing, and the landscape of emerging technologies is constantly shifting. 

Did you find this article interesting? Download the free report to learn about: 

  • The rest of the technologies that capture the developers’ imagination
  • The Role of female coders in software development
  • An update on language communities
  • How well-paid developers feel
  • What makes a high-quality API
  • An Overview of embedded software development
Categories
Community News and Resources

Shaping the future of Developer Space: Start here.

the future of Developer Space

It’s, no doubt, one of the fastest moving tech eras in the history of technology. From artificial intelligence and machine learning to blockchain and virtual reality, emerging technologies are transforming entire industries and redefining the way we interact with the world around us. 

For software developers , keeping up with the latest technologies has never been more crucial. By continually testing your knowledge and understanding of these technologies, you can utilize those capabilities to their greatest potential, making your life simpler, faster and more efficient. But where do you start?

Our brand-new Developer Nation survey is now open for developers who’d like to test their standing with the latest technologies and leave their mark in shaping the future of developer space. To help you get a better understanding of who we are, what we do and what it feels like to be a part of our developer community, we’ve also compared the Developer Nation Survey with the surveys offered by other developer communities, like Stack Overflow, across a variety of parameters to help you make the choice for yourself. 

Now, read on and unleash the incredible power of your voice!

Developer Nation Survey: Your Voice Matters

Developer Nation survey is the leading research programme that focuses on capturing and analyzing the trends in the developer ecosystem by inviting the participation of developers within the Web, Mobile, Desktop, Cloud, DevOps, Industrial IoT & Consumer Electronics, AR/VR, Apps/extensions for 3rd party ecosystems, Games, Machine Learning & AI, and Data Science fields. Some of the questions we ask revolve around your favourite tools and platforms, the projects you’re currently working on, your perspective on the software development cycle, and more. 

Why does your voice matter? Because it helps shed light on the challenges, trends, and opportunities within the developer community. With developers being the backbone of technological advancements and innovation, your opinion can directly influence the tools, programming languages, and industry standards of tomorrow. 

Many big tech companies trust our unique data insights in helping them understand developers better and shape their strategies. Here’s how Okta uses our data – your voice, to unlock more developer opportunities.

By participating in the Developer Nation Survey, you’ll be able to not only gain valuable insights and learn about the latest trends, but also have a chance to share your voice and ensure that your unique perspective is considered in shaping the future of software development. 

Comparing Prominent Developer Surveys

While these surveys focus on grasping the essence and behaviors of the developer community, they offer unique perspectives and insights across different dimensions, such as location, prizes, developer communities, loyalty programs, and average reach. 

Focus point

The Developer Nation Survey offers a global perspective, covering a wide range of topics and trends that impact developers worldwide. It emphasizes inclusivity and collaboration, ensuring that diverse voices and experiences are represented in shaping the future of software development. On the other hand, the Stack Overflow Developer Survey and Offerzen’s State of the European Software Developer Nation Survey have a narrower focus, and, therefore, offer localized insights and shed light on the challenges and opportunities within particular regions. 

To reach a wider and more diverse audience, we also translate our Developer Nation surveys in 10 languages  and make it available in 165+ countries, making it accessible and convenient for people who are not native English speakers. 

Loyalty program

With the mission of helping developers be their best selves, we place great importance on giving back to our community by sharing valuable insights and data, helping them set the right foundations for their careers, discover opportunities for professional growth and reward them for active participation with our loyalty program. Give us your feedback, participate in our survey production process or complete the survey to gather points, unlock special benefits and win prizes! 

Prizes

We understand that there is no ‘one size fits all’ approach. As a result, we try to bring in many exciting rewards which can be useful, practical and high-tech. Compared with other communities, we offer a wide range of different prizes and here’s what you can get your hands on by taking part in our Developer Nation survey:  cards and vouchers towards your desktop setup, a MacBook Pro13 M2, an Asus ZenBook13, annual or monthly licenses, courses credits to learn something new, and many more. Plus, everyone who completes the survey will get a free virtual goody bag with access to free resources. So, why not take your chance to get something you always wanted?

Giving back to the community

We make recurring donations to the charity of your choice. For each qualified survey response we donate USD $0.10 to different charities and organizations supported by our developer community. Our goal is to reach USD $1,700 in donations. Take the survey, pick a charity to support, and help us make a difference!

the future of Developer Space

What we do with the data 

We protect your privacy by anonymising all your answers. Those results are then available in the free State of the Developer Nation 25th Edition report, which you can be the first one to have access to by taking our survey! If you’re interested in the insights we offer in our reports, check out the previous editions here.

We exchange those insights regarding emerging trends among developers to help individuals responsible for developing tools and platforms in understanding the genuine needs of software creators. Our research remains independent, meaning that our surveys and data are not owned by any vendor, community, or other affiliated partner.

As for our survey methodology, we keep it transparent by making our sampling and analysis methods available in all our reports on developernation.net, free to download for all developers.

Ready to kick things off? Start the survey now!

Categories
APIs Community

What makes up a high-quality API

high-quality API

With third-party APIs, developers can leverage the power of external expertise to enhance the functionality of their applications. However, to ensure success, they must carefully evaluate the quality of APIs before incorporating them into their applications. This chapter aims to investigate the key characteristics that make third-party APIs high-quality, according to developers.

In recent years, application programming interfaces (APIs) have become a key part of modern software development. APIs act as intermediaries that facilitate communication between different applications through established protocols and definitions. By using APIs, developers can leverage the power of other applications without needing custom integrations. In turn, this allows them to focus more on building the core parts of their applications and less on recreating features that already exist or are not feasible.

With this in mind, it is unsurprising that almost all developers (89%) report using APIs in their projects. According to our data, 74% of developers use third-party APIs while 15% state that they only use private or internal APIs. Using private/internal APIs makes it easier for developers to link their in-house applications together and ensures that only authorised personnel can access their systems and internal information. On the other hand, using third-party offerings gives them access to external expertise but introduces additional dependencies that can affect their projects.

high-quality API

74% of developers use third-party APIs

With so many developers relying on third-party APIs to expand the scope of their applications, modern services are becoming increasingly more likely to offer public APIs. However, not all APIs are created equal. Just as high-quality APIs can enhance the capabilities of a given application, adopting a low-quality API can be detrimental to its success. Implementing low-quality solutions can create a wide range of issues such as poor performance, negative user experience, and security vulnerabilities. Therefore, developers must carefully evaluate the quality of APIs before incorporating them into their applications.

In the latest edition of our global developer survey, we asked developers who use third-party APIs to identify the most important characteristics of high-quality API offerings. Our results indicate that developers consider security, documentation and sample code, reliability, ease of use, and performance to be the most important characteristics of high-quality APIs. These five qualities separate themselves from the rest as the core pillars of strength developers look for when considering third-party APIs. In fact, 89% of those who use third-party APIs mention at least one of these characteristics in association with high-quality APIs.​

Security is the most important factor in evaluating the quality of third-party APIs, according to 42% of developers. Using third-party offerings opens up a line of communication with external services that can expose their users to unauthorised access to sensitive data and other security risks. To keep up with the rapidly evolving landscape of threats, developers and modern businesses must ensure that the APIs they use are secure to protect their assets.

Developers consider security to be the most important attribute of a high-quality API

Having access to clear documentation and sample code can make it substantially easier for developers to incorporate APIs into their applications. Our data suggest that 39% of developers consider documentation and sample code to be among the most important qualities in third-party APIs.

These features allow developers to quickly understand the capabilities and limitations that a given API brings and make it easier for them to get started. This goes hand in hand with ease of use, which is mentioned by 37% of developers who use third-party APIs.

On the other end of the spectrum, reliability (38%) and performance (36%) of third-party APIs can directly impact the success of a given project. If an API proves to be unreliable, it can lead to issues ranging from minor errors to system failures and data breaches.

On the other hand, reliable APIs help developers minimise the risk of something going wrong and ensure the highest chances of success in their projects. Similarly, applications can only perform as well as the APIs they use.

Therefore, it is essential for APIs to be fast and capable of handling high volumes of requests to be used in modern applications.

high-quality API

Those who are new to the field of software development tend to work on less challenging problems and can often turn to their peers and mentors for support. As such, they are the least likely (20%) to cite documentation and sample code as an important characteristic of a high-quality API and tend to prioritise other features.

However, as they gain expertise and take on more complex projects, developers begin to appreciate the benefits that clear documentation and sample code bring to the table. In fact, 65% of developers with 16+ years of experience mention documentation and sample code among the most important characteristics of high-quality third-party APIs, surpassing even security (51%).

Highly experienced developers value API documentation and sample code significantly more than beginners

With a greater reliance on self-guided learning, experienced developers become less likely to focus on the community when evaluating the quality of third-party APIs. However, technical issues can arise regardless of experience and may be difficult to resolve or diagnose without expert-level knowledge. In turn, technical support appears to retain its above-average importance for all but the most experienced developers.

high-quality API

With more years of experience, developers gain a deeper understanding of what is essential for their projects. For some, performance may be critical, while others may focus more on ease of use. By focusing on the right characteristics of third-party APIs, developers can enhance the functionality of their applications and deliver better products.

Would you like to contribute to similar findings?

Participate in our latest wave of the Developer Nation survey!

Complete the survey to access our amazing virtual Goody Bag filled with subscriptions, resources, and more!

Sign up for the chance to win prizes, earn loyalty points, and receive updates on survey results and future opportunities.

Take the survey anonymously here

Categories
Community

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. 

Categories
Community

IoT and MQTT for Software Developers

IoT and MQTT for Software Developers

“Developers who stop learning get left behind. However, the ones that grow and expand with trends always eat well.”

– Kudzai Manditereza

The technology landscape is evolving, with the Internet of Things (IoT) leading the charge. IoT is transforming the way we live and work, with billions of devices generating massive amounts of data. As a software developer, it is essential to stay on top of this trend and understand the opportunities and challenges presented by this new technology.

In this three-part series, we will breakdown IoT,  MQTT, which is the defacto standard protocol powering IoT, and wrap the series up with a practical step-by-step demonstration you can follow to try IoT yourself. 

If we do our job right, at the end of this first article you will be inspired to dive deeper into IoT, and see how you can add it to your arsenal of skills. At minimun, you will be able to define IoT fundamentals, understand its components and the impact this has on software development. Additionally, we will explore the potential benefits and the challenges you may face when working with this technology.

Demystifying IoT: A Simple Definition (for Software Developers)

IoT can be a complex and often confusing topic, but it doesn’t have to be. At its core, IoT refers to the network of physical objects that are connected to the internet and can exchange data with one another. These objects, or “things,” can be anything from household appliances and wearables to industrial machinery and smart city infrastructure.

To understand IoT, it’s important to break it down into its components. An IoT system typically consists of three main components: sensors, connectivity, and data analytics. 

Sensors 

Sensors are crucial in IoT systems; they serve as the primary data collection points. They bridge the gap between the physical and digital worlds by converting real-world information such as temperature, humidity, and motion into digital information. Sensors enable IoT systems to monitor, measure, and respond to various parameters by detecting changes in the environment or devices.

Connectivity 

As stated, the primary goal of data collection from sensor devices is to share the data with other devices and data analytics applications in the network. Connectivity is the communication infrastructure that allows these devices and applications to communicate and exchange information, e.g, Wi-Fi or Bluetooth. 

In addition, for devices to make sense of the shared information, they must use a standard communication protocol. Examples of IoT communication protocols include HTTP(S), which most software developers already use, AMQP, CoAP, and MQTT, which has become the defacto IoT standard protocol. We discuss MQTT in detail in Part 2 of this series.

Data analytics 

Data analytics platforms, which are usually cloud-hosted, enable the transformation of typically vast amounts of data collected from sensors/devices into valuable information and actionable insights. The capabilities of Data Analytics platforms range from simple visualization for remote monitoring to identifying patterns, trends, and correlations within the collected data, and advanced machine learning-based use cases. 

Application of Internet of Things (IoT)

To give you a sense of what IoT looks like in the real world, below is a list of the use cases in the Commercial, and Industrial sectors.

  1. Smart Retail: IoT can be used to create personalized shopping experiences through digital signage, targeted promotions, and smart shelves that detect low inventory levels.
  2. Building Automation: IoT can enable commercial buildings to become “smart,” with automated lighting, heating, ventilation, and air conditioning (HVAC) systems that respond to occupancy and environmental conditions, improving occupant comfort and reducing energy costs.
  3. Smart Agriculture: IoT can be used to monitor crop conditions, soil health, and weather data, enabling farmers to make data-driven decisions about irrigation, fertilization, and pest control, ultimately increasing crop yields and reducing resource waste.
  4. Wearable Payments: IoT devices like smartwatches and fitness trackers can be integrated with payment systems, enabling users to make contactless payments without the need for physical cards or cash.
  5. Predictive Maintenance in Manufacturing:  By collecting and analyzing industrial equipment data, potential issues can be detected early, helping to prevent unexpected equipment failure, reduce downtime, and extend the lifespan of machinery.

Hardware Platforms for IoT Application Development

To start building an IoT application, you will need a device that provides a physical interface to sensors, allows you to write and deploy code to acquire data from those sensors, establish connectivity to the internet, and publish the data for analytics in the cloud.

Device platforms commonly used to develop and prototype IoT applications include Raspberry Pi, Arduino, ESP32, BeagleBone, and others. The selection of an IoT device platform is mostly influenced by each platform’s capabilities for prototyping different features or products. For example, some devices only support WiFi connectivity requiring you to plugin additional hardware for cellular connectivity. Some platforms can host a full operation system for executing multiple general-purpose tasks, while others are built to execute one specific task. 

Developing Software for IoT Applications

To develop software for IoT devices, you need to have a good understanding of the hardware and networking technologies involved in IoT. As a developer, there is a good chance you are already familiar with programming languages used in IoT development, such as C, Java, and Python. IoT solutions can be programmed using a variety of languages, depending on the specific needs of the project. Programming languages commonly used in IoT development at different levels of the technology stack include C, Python, Java, JavaScript, NodeJs, and C#:

Similar to other projects, developers should choose a language that is suitable for the hardware and software components of the IoT system, as well as the data analysis and visualization requirements.

Using Software Libraries for IoT App Development

As with any software project, leveraging software libraries can make the process significantly easier and efficient. In IoT, libraries can help manage the following:

  • Data gathering from common sensors on the market
  • Controlling common actuators such as motors
  • Communication protocols, 
  • Data processing
  • Security features

To use these libraries, developers need to first identify the appropriate ones for their project requirements, such as the protocol stack (e.g., MQTT, CoAP, Zigbee), device platform (e.g., Arduino, ESP32, Raspberry Pi), and cloud services (e.g., AWS IoT, Azure IoT, Google Cloud IoT).

Here’s an example of code in Python that might be used in an IoT system using a Python library for an MQTT Client. The code shows how to connect an IoT device to an MQTT messaging server and subscribes to receive messages of interest that are being sent to the same server by other IoT devices. 

import paho.mqtt.client as mqtt
# Set up MQTT client
client = mqtt.Client()
client.connect("broker.hivemq.com", 1883, 60)
# Define callback function to handle incoming messages
def on_message(client, userdata, message):
    print("Message received: " + message.payload.decode())
# Subscribe to topic
client.subscribe("iot/devices/sensor1")
# Start the MQTT client loop
client.loop_start()
# Continuously listen for incoming messages
while True:
    pass

As previously mentioned, developers can create IoT applications able to interact with physical devices, gather data from sensors, and even automate tasks. IoT development involves a combination of software development, hardware design, and networking, so developers need to be well-versed in all these areas to create effective IoT applications.

IoT Software Development: Similar But Different

 The software for IoT differs from traditional software in several ways:

  1. Resource constraints: IoT devices often have limited computing power, memory, and storage compared to traditional computing devices. As a result, IoT software must be designed to use resources efficiently and effectively.
  2. Real-time requirements: Many IoT applications have real-time requirements, meaning that data must be processed and analyzed quickly, often in milliseconds or less. This requires a different approach to software design and development.
  3. Distributed nature: IoT systems often involve many interconnected devices that must work together to accomplish a task. This requires a distributed system architecture and a focus on communication and coordination between devices.
  4. Security: IoT devices connect the internet to the physical world, and security breaches can have life-threatening consequences. Therefore, IoT software must be designed with security in mind, including data encryption, access control, and secure communication protocols.
  5. Heterogeneous environment: IoT devices can run on a variety of hardware and software platforms, which can make software development and deployment more complex. Developers must be able to work with a wide range of platforms and technologies.

Understanding these differences, as well as IoT components, allows you to leverage each of their powers and create innovative and impactful applications that improve people’s lives and transform entire industries.

An example of a possible career path in IoT is IoT Engineer.  An IoT engineer is a professional who specializes in designing, developing, and maintaining Internet of Things (IoT) systems. They are responsible for creating the software and hardware components of IoT systems and ensuring that they work together seamlessly to achieve the desired results.

Conclusion

Overall, learning about IoT can be challenging, but it’s also gratifying. By following these steps and staying dedicated to your learning journey, you can develop the skills and knowledge you need to succeed in this exciting field.

We encourage software developers to explore IoT and take advantage of its new opportunities. By collaborating with other experts in the field, leveraging open-source technologies, and continuously learning new skills, you can make a significant impact in the world of IoT and contribute to building a more connected and sustainable future.

So what are you waiting for? Start exploring IoT today and unleash your potential as a software developer! 

Now that you know all about IoT, in part two of this series we are going to introduce you to MQTT, a technology that has become the defacto standard of data movement in IoT with applications spanning from Facebook Messenger, Connected Cars, Connected Factories, Wearables, and Home Automation etc.