Author: Ilya Smirnov, Head of AI / ML Department at Usetech. Visiting lecturer at the Massachusetts Institute of Technology. Author of more than 50 scientific publications, speaker at international conferences and technology podcasts. Author of patented technology for trajectory analysis of vector 3D seismic fields.
Whether you have tried ChatGPT or use automated AI tools every day, the monumental rise in the adoption of Artificial Intelligence systems in all aspects of our lives is absolutely obvious. According to Grand View Study, the global Artificial Intelligence market is expected to grow by 37.3% from 2023 till 2030. Of all areas, AI in software development will be the most progressive and attractive for investments.
There is no denying that AI has already found its place in software development and has great prospects for the future. That’s why it is so important for IT market leaders to identify competitors as related to AI adoption in order to remain commercially viable. In this article, we are going to highlight opportunities and offers in the area of AI adoption in software development.
Will AI replace software developers?
We can say for sure: AI will not replace software developers in the immediate future. Even with customization, specific use cases and wishful thinking, AI has too many limitations. Nonetheless, AI will change software developers working practices, as 70% of developers admit that they routinely employ AI tools, which give them advantage in tasks completion and increase their performance. It is important to realize that AI will not replace all software developers and engineers. AI will only help developers achieve more and have much time to perform tasks that are more complicated than the applied algorithm code implementation.
How will AI influence developer’s experience?
AI for software development is already changing methods of software testing, debugging and documenting by expert teams. Developers use AI as a mediator in communicating with colleagues, analysts, customers, and clients. In particular, AI can speed up new features addition, bugs fixing and support requests.
All these changes can already be observed in the following development aspects:
- AI already helps analysts decompose large tasks and establish code and product acceptance requirements and criteria. Analysts can take this information into account, as decomposing performed by AI, is highly likely to correspond to community standards and approaches used in model learning.
- AI in design. In our opinion, this is one of the issues almost solved in practice. AI already helps with basic user interface design for MVP. This allows teams implementing the prototype to save on front-end development, since as a rule there is no need for design at that stage, and to leave complex interactive design elements for designers and front-end developers. Once AI has created the basis for app pages, designers can turn to user interface logic and mechanics.
- AI helps ensure the continuity of the development process. The development teams can use AI to write large amounts of code, but AI can also be employed to generate test coverage for a given piece of code. Thus, AI speeds up the development on the whole and increases the process continuity.
What areas definitely require generative AI?
Recurring, recurring and recurring tasks: AI is able to perform routine tasks with clearly defined requirements. This job is also important and allows developers to concentrate on more complex non-standard issues, which cannot be managed by AI.
- The first code prototypes: a software developer can quickly get the first draft prototype using AI. When developers are not sure where to start from, or they face challenges in task decomposition, the AI-generated code provides one of the skeleton options for further development.
- Minor updates of existing code: AI is perfect for minor code changes and updates. The main thing here is not to try too hard and state the request correctly, since there is a risk that you receive the brand-new code, and a minor update turns into full-scale development. However, developers can use AI to find bugs, improve previously written software and enter adjustments according to specific criteria.
- Analysis of plans and search for the best development path: AI provides an opportunity to analyze road maps and coordinate plans and development speed. This significant advantage is implemented in teams of 10 experts and more. AI tracks projects performance, improves predictions and finds the best way for tasks completion and assignment.
Despite all benefits of AI, developers are ahead of it in many processes.
So you still need a development team:
- Complex coding requirements: projects often impose on developers conflicting requirements, that are absolutely impossible to satisfy. Solving such issues is the task of communication between the product owner and the project manager and the business customer. AI is useless here, because such situations are non-routine and change dynamically, and there are no precedents within the learning sample.
- AI is unable to predict specific requirements for development in your company, so AI-generated code could not satisfy corporate safety and performance requirements. For this, among other things, such development standards, as PEP8, etc., are elaborated. This makes it possible to teach AI systems, and then generate code using the same standards.
- AI works best in a narrowly defined area. It can create the code which solves specific tasks, but such solutions will not be perfectly aligned with the overall product. We need a developer, who would be able to align all developed software in terms of style and function.
How to use AI in software development?
Knowing when and how to use AI is critical for making the most of available tools. Let us review the best options and practices to use AI in software development.
Code generalization and documents drafting
All modern approaches to development require quality assurance and management, which is expressed in automatic and manual testing. AI can automate the testing process. Despite all its weaknesses, AI combined with manual tests ensures the maximum code testing coverage. AI can also conduct A/B-testing of two software versions to identify the best solution. Once testing is complete, AI can draw up the documents.
Specific coding processes optimization
Automated tools can improve coding and speed-up projects implementation. Unlike common code generation, such tools are able to:
- Suggest new code lines, recognizing the code of the task being implemented;
- Introduce small corrections in real time, e.g., as related to syntax; automatically add line-end strokes, closing brackets, etc.;
- Supplement branching operators with all required commands. Thanks to these tools, developers can concentrate on the creative aspect of coding.
Such approach also allows them to try other code blocks combinations and other ways to solve tasks, while AI will generate code blocks for them.
Debugging
Automated debugging routines are one of the most wide-spread artificial intelligence tools in software development. Developers can identify problems manually, but AI is able to improve the process due to revealing and eliminating bugs in the blink of an eye. Some tools are even able to predict future bugs based on your data.
AI can not always identify complex errors, but in simple testing it saves many hours spent for debugging.
It is worth mentioning, that systems for project planning and resource allocation are already being developed:
- Successful projects involve thorough planning and assessment, as well as budget compliance. AI can analyze previous projects, collect data on resources allocated, and recommend the assessment for similar new projects. Such assessments help the company make a decision on the advisability of participation in a project with existing resources.
- Once resources are allocated, AI helps predict the results. Namely, automated project tracking platforms use these data to manage:
- Project timing and stages;
- Budget and resource planning;
- Scope of project;
- Workflows.
Mitigation of entry barriers for non-developers
Software development requires deep expertise, that’s why there are skill gaps and barriers for such team members as testers, analysts, and project manager. Generation of code with AI allows less experienced team members to employ a tool, which can be useful to check new ideas and approaches to solve a major business task. It is also important, that this additional tool allows developers to perform more complex tasks and improve their skills.
Conclusion
It is safe to say that AI represents the present and the future of mankind. AI is already successfully integrating both into our personal and professional lives. This, in turn, means a growing amount of work for AI developers. Sooner rather than later, we will start to successfully adopt AI-assistants to develop AI-assistants. The circle is closing. Get the popcorn!
