The impact of AI on software development
Software has changed and so has software development. We are witnessing how software is enveloping the world. As we become increasingly software-driven, software development organizations have had to give their development methodologies a makeover. We witnessed the rise of Agile and DevOps that helped the development process become faster and more fool-proof. I feel, today, software development may be at the cusp of yet another overhaul – one that is powered by AI. I’ve written previously about what I think about AI and the applications. This note is what I feel the AI impact on software development will be. I should also hastily add that this is not a note about how AI will replace the developer -more an exploration of how an AI-led transformation could come about.
Smart development
Software applications are changing. They demand increased safety and security measures. Technologies such as IoT and AR are also impacting the very nature of the applications. The demand for interactive applications is increasing. This creates a greater load on the developer and drives a greater focus on testing and automation. The developer also has to cross the chasm of developing a great idea into an even greater collection of code. Developers now need to identify why something is not working and they have to do so fast. In a nutshell, they have to be hyper-focused on developing stronger code than yesterday.
I feel AI is going to complement the Agile and DevOps methodology in the new age of software that we are entering. AI technologies such as Machine Learning can help developers improve how they deal with daily tasks (more on that here). AI can help developers by deploying expert systems that suggest possible changes in code and provide suggestions on how to deploy them. With AI, developers can implement strong text recognition in software models to create stronger code. AI also brings to the table the ability to recognize previous errors to improve the overall project outcomes.
The impact on project management
AI may well help level the playing field for the project managers. Identifying the precise time needed to generate functioning code from requirements is a tall order. Project managers have to expend a lot of effort to derive a reliable project estimation. They need a well-known context and reasonably comprehensive knowledge of the technology and the teams' skill levels with it to arrive at the right conclusion. However, with AI and Machine Learning, project managers can leverage analytics taken from a huge amount of data collected over an extended period of time from many projects. They can bake in parameters such as user story descriptions, historical team estimates, and actions, activities related to user stories etc. and use the correlations and statistics to get more precise estimates of time and effort.
The production line impact
DevOps teams spend a lot of time and effort analyzing and identifying why and where “in operation” bugs happen. They also have to identify who would be the best person to fix those. With AI in the picture, software development teams could get detailed insights into who wrote the code and identify developers with similar skills who have worked on similar projects and have the available time. The past project lifecycle data can also be leveraged to make the production line more free-flowing by giving development teams the insights they need to predict what might go wrong with an existing project and help them be proactive in avoiding such problems in the future.
The testing impact
The applications of today interact with each other using numerous API’s. Some applications still use legacy systems. This can further increase testing complexity. AI tools can be used here proactively to improve software testing and ensure better information authenticity, test creation, and improved test management. Repetitive manual tasks can not only be removed but automated tests can become more refined and intuitive using AI. AI gives testing teams the power to automate tests like GUI testing as the AI algorithm can test whether the visual-focused code is functioning optimally or not.
AI can also reduce regression testing load by identifying incorrect or unused requirements. AI can give testing teams the benefit of applying business rules and production data to assess how the application is being used. This would not reduce the load on testing teams but also help testers and developers alike prioritize their efforts based on the business risks.
With AI as a part of the test automation armory, developers can code better. The QA experts can test more effectively and expansively in lesser time. Testing teams can use bots powered by AI to identify bugs. AI testing tools assist in not only finding flaws but could also fix the code automatically thereafter.
The impact on management and maintenance
Software applications are becoming more complex and carry multiple dependencies and integrations. Along with this, there are multiple layers of functionalities and interfaces to take care of. Presently, much of this sustenance function has to be done manually. This effort can be time-consuming and effort-intensive. With AI, Machine Learning models can be easily employed to extrapolate important features and patterns in data. Machine Learning methods such as backpropagation and stochastic gradient descent can update the software development model with new data that will change the way in which the model will perform with each change.
While traditional software development is unlikely to disappear and I don’t claim that we will ever replace the human developer, AI could well give traditional development the new age boost that it needs – to become more agile, accurate, and accelerated.