Developer Nation Blog

When you are involved and excited by an emerging technology, it is a common instinct to overestimate its impact and promise. Media enthusiasm builds in intensity and stokes interest, and when a new technology is promoted at the proof-of-concept stage, the publicity encourages developers to investigate it. Early adopters dive in, development proceeds, and success stories add to the anticipation of great things to come.But what about mainstream adoption?

While the intensity of interest may appear strong, it is equally likely that there are many, unreported, abandoned projects. Developers may initially be enthusiastic about a technology but then sometimes find their expectations are not met for a number of reasons, particularly if the hardware promoted is unavailable, consumers are not interested, or the necessary tools are difficult to get to grips with. To avoid disappointment, those developers wishing to be successful in a new field will need to work hand-in-hand with vendors providing the products or platforms. It is only through refinement that an immature technology can become sufficiently compelling to encourage mainstream uptake and continuing media attention rather than be written off as over-hyped.

We gauged interest in certain technologies by asking developers about the areas they are actively working on, learning about or simply interested in. The resulting answers fall into four quadrants when divided around the median values of the responses, indicative of the technologies that have already matured and been widely adopted, those that are triggering interest, and those that are still nascent or have hit a plateau.

MAINSTREAM ADOPTION

DevOps is one of the best-established, mainstream technologies of those areas we asked about. Used across a range of industry sectors, it is a set of tools and practices that allow development and operations teams to collaborate in the development and rollout of their software. DevOps automates infrastructure, testing, and performance management, allowing code to be released into production more regularly and with fewer defects.

DevOps is one of the areas that ranked highest in the survey in terms of interest, learning, and adoption. It is the most popular by some margin for developer adoption (17%) and learning (also 17%), and over half of the developers that expressed interest in the topic are working on DevOps projects.

We also find embedded development, which includes IoT, to have entered the mainstream adoption. While embedded development attracts similar levels of interest to drones and robotics, it shows significantly higher levels of developer adoption. This may well be because the field has had time to establish itself. IoT, although still an emerging and somewhat nebulous area, has reached a point where the early hype has died down and the possibilities are better understood by developers and consumers alike.

Mini apps are a relatively new phenomenon. Running inside a mobile framework, they are isolated within a specific ecosystem, such as the popular WeChat app. They are written using HTML5 and other web technologies. Developers reported a high level of interest, and 10% adoption, placing them in the mainstream quadrant. Unsurprisingly, we found mobile developers to be particularly keen on this technology, with 22% adoption; the second highest technology of interest for mobile developers after robotics. We also found this to be the one area more highly adopted by women developers than by men.

WANING INTEREST

Enterprise Resource Planning (ERP) and Customer Relationship Management (CRM) are used to increase the overall profitability of a business. CRM software is used to organise sales, marketing and customer services, while ERP is used to improve the efficiency of internal business processes. Fewer developers are active in this area than in DevOps, although it is the second most adopted technology area. However, the number of developers saying they simply were not interested in this area was the second highest, and the numbers of developers working in the area have dropped significantly since our last survey. ERP/CRM seems to be an area where interest is tailing off.

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AI is a powerful and disruptive technology altering the landscape of application development and the wider world as we know it. The adoption of AI is increasing at a fast pace. While AI helps developers in every area of society to create solutions, implement change, and drive progress, it also forces us to think more deeply about our relationship with technology and the ethics of AI.

Indeed, adoption and availability of tools to build AI have caught up with the promises of the field and what once seemed unachievable is now within reach. As a result, many people are concerned and are actively discussing the implications of AI and to what standard we must hold ourselves in order to ensure that AI is aligned with our widely shared human values.

WHERE DO DEVELOPERS STAND ON ETHICS IN AI?

Their views are surely of the utmost importance because they are, after all, on the front line of building and implementing the algorithms that underlie AI products. In the 16th edition of our Developer Economics survey, we asked developers to what degree they agree or disagree with issues such as AI’s unintended consequences, algorithm bias, and jobs replacement, as well as their views about data collection and protection.

WE GOT THE BASICS RIGHT

It should give us peace of mind to know that the vast majority of developers take user rights very seriously. Developers agree that they should not only ask for user consent to collect data and follow security and data protection laws but that they should also go above and beyond legal requirements – 72% of developers told us so. Scandals such as the Facebook/Cambridge Analytica one have indicated that regulations are lagging behind and it is very encouraging that developers are aware of their ethical responsibility while regulators are still trying to catch up.

When it comes to AI specifically, however, developers have diverging opinions on a range of topics.

CAN AI BE TAUGHT TO BEHAVE AS THOUGH MORAL & HUMAN-FRIENDLY?

No topic divides developers more than the unintended consequences of AI. When asked whether AI can be taught to behave as though moral and human-friendly, developers’ responses split almost equally among those who agree (33%), those who neither agree nor disagree (40%), and those who disagree (27%). While such distribution of opinion could be expected from the general population, one might expect developers to have a more unified view as they possess a better technical understanding of what ML/AI can and cannot achieve.

Looking at the breakdown of developers’ opinions by age group we find that individuals who are under 25 years old have a much more positive outlook (45% agree) than those who are over 35 years old (28%). Where developers live is another differentiator: Europeans are more neutral (42% neither agree nor disagree) whereas South-Asia has the highest percentage of developers who agree that AI can be taught to behave as moral and human- friendly (49%). These differences may be the result of the type of involvement in ML/AI as developers in South-Asia are more likely to be using ML for medical diagnosis and prognosis, object recognition/image classification and NLP (Natural Language Processing), whereas Europeans are more likely to be working in more ‘traditional’ ML fields such as fraud detection.

Responses of ML/AI developers and data scientists also differ when considering their types of involvement (as professionals, hobbyists or students) and their use cases. Half of developers who teach AI, ML or data science have favourable views towards the ability of AI to behave in a moral and human- friendly way – in fact, teachers are twice as likely to strongly agree compared to all developers involved in ML/AI. On the other hand, developers who build machine learning frameworks are more likely to strongly disagree (12% vs. 8% for all developers).

Another very interesting insight is that more than half (56%) of ML developers who work in bioengineering and/or bioinformatics agree that AI can be taught to behave morally and be human-friendly. This is worth noting as these developers develop ML/AI that applies engineering principles of design and analysis to biological systems and therefore are likely to have a deeper understanding of the feasibility of such a lofty goal.

A burning question is “Will AI steal your job?”

Discover the answer and more details on the Ethics in AI, on our State of the Developer Nation 16th Edition report.

It’s free and full of insights.

The technology industry often takes credit for the changing world of work. One example is the model of remote employees working as digital nomads in their favourite coffee shop, connected via Slack and collaborating via the cloud to create products and services for consumption over the internet or on smartphones and tablets. But what about work within the technology industry itself? We take a look at the profile of women in tech and compare it with the profile of their male counterparts.

If we exclude those who preferred not to share their gender with us, and those who skipped this optional question, female developers responding to our survey were outnumbered by males by a ratio of 1 to 10 (9% women and 91% men). This suggests a global population of 1.7 million women developers and 17 million men. The technology industry is dominated by men and the imbalance in numbers is such that we cannot make numerical comparisons between men and women. Instead, in the rest of this chapter, we will look at relative differences in terms of experience, age and roles adopted, and the most common company sectors and development areas for men and women.

What are their ages?

Looking at the comparative ages of male and female developers, we find a higher percentage of women are under the age of 35. The 25-34 age group accounts for the largest number of developers of both genders (36% of women, 33% of men), yet male developers are more likely to be older: we found 37% of male developers are over 35 years, compared to 29% of women developers.

There are (at least) two different ways of interpreting this observation. One is to say that women are being increasingly drawn to software development; the comparatively young profile of women compared to men illustrates recent gains made in attracting girls and young women into technology. Analysis of college data for entrants to computer science courses, in North America at least, suggests that this is indeed a plausible explanation, as women are increasingly studying courses in the subjects that lead to a career in technology.

An alternative, or additional, explanation is that women may have always been involved, but tend to leave software development as they get older, either by choice or necessity.

And here’s a preview of the roles they undertake:

Women in tech & their educational background?

When we looked into the education levels of the genders, we noted that women developers are equally likely to have been educated to degree level in computing/software engineering when compared to men. Likewise for other classroom training that doesn’t lead to specific degrees, and for attendance at developer bootcamps.

Women are slightly more likely than men to have learned their craft using online course materials and slightly less likely to have learned on-the-job. Women are significantly less likely to be self-taught (57% of women compared to 75% of men) but it is still the most popular way of learning about development for both genders. The relatively older profile of men probably explains why more have become self-taught: they have engaged in continuous education throughout their longer career because of the rapidly changing nature of the industry. As women in tech and women developers particularly mature, we would expect the level of “self-taught” women to rise as they also teach themselves new skills to advance their career.

For more details on the Gender Wars, you can download our State of the Developer Nation 16th Edition report.

It’s free and full of insights.