Agile technologies such as DevOps and continuous integration, continuous delivery (CI/CD) practices have brought about positive changes for software developers over the past decade. From faster time to market and greater collaboration between development and operations teams to fewer end-user issues and improved testing, DevOps has changed the way developers work. In a similar vein, artificial intelligence (AI) is poised to change how organizations and their engineering teams approach software development.
AI’s promising potential
Software engineers are tasked with creating solutions for the myriad problems, challenges, and everyday organizational tasks in most every industry imaginable. Ironically, they even develop the tools that make their development processes easier. AI is well-suited to helping software engineers develop these intelligent software tools because it can learn from and replicate human behaviors. Because of this, AI and machine learning algorithms can impact nearly all areas of software development.
Best uses for AI in software development
AI and machine learning have already made big impacts in software development. Here are three of the most important ways it is changing the development landscape and the evolving role of software engineers.
- Estimating delivery schedules — When development teams work together for long periods of time, they become fairly adept at estimating delivery times, although they may still encounter challenges due to a variety of influencing factors, including flawed code and changing user demands. AI can help development teams make more accurate estimates, even with the numerous and diverse factors that come into play. And as the AI programs gather more data and learn from other development projects, the accuracy of those estimates is likely to continue to improve.
- Project management — AI systems can take over daily project management tasks without the need for human input, according to The Next Web article. Over time, they can understand project performance and use that knowledge to form insights, complete complex tasks, and help human project managers make improved decisions.
- Testing and quality assurance — Developers are creating tools that use AI to detect flaws in code and automatically fix them, according to the Forbes article. This is the logical next step after testing automation and will likely lead to higher-quality software and improved time to market. Software engineers could have less involvement in testing mechanics but would shift their roles to approving and acting on test findings. In other words, AI could streamline software testing by providing the right data to the human engineers who can then make better decisions.
Best practices and AI’s future
Based on these changes alone, it seems AI and machine-based learning are primed to disrupt the software development field. What does that mean for development company leaders, software engineers, and software development in general?
Overall, AI will likely help software development become better, faster, and less expensive. However, for this to happen, engineers would have to learn a different skill set so they could build AI into their development toolboxes. They’d need more data science skills and a better understanding of deep learning principles to reap the full benefits of machine-based learning. Also, instead of turning to logging and debugging tools to find and fix bugs, engineers would need tools that allow them to question the AI to find out how and why it reached a particular conclusion. In addition, AI could allow more tasks to run autonomously and require fewer daily management tasks. Finally, developers could use AI for routine tasks so humans can focus on what makes them human: thinking creatively according to the problem’s context, something that AI has not yet mastered.
Will AI eventually replace the human element in software engineering? Not likely, but it certainly has the potential to make development faster, more efficient, more effective, and less costly all while letting engineers and other development personnel focus on honing their skills to make better use of AI in their processes.