Implementing AI into software engineering? Here’s everything you need to know

All that sounds good, right? But let’s bottom-line it: What is it about AI in software engineering that can give developers a few extra hours to watch Doctor Who, play with the dog, or — I know this sounds unreasonable — sleep? Let’s look at five areas that come to mind.

Automation of repetitive tasks

There are a ton of very dull, mundane tasks that require the skills of a developer, but are anything but fun. One example is helping to write repetitive code blocks.

Coders have long been able to set up their IDEs to fill out code blocks as if they were macros, but AI can learn the pattern and intent of code blocks and help generate them using either the style of the programmer or organization, and without necessarily pre-programming them. It could suggest, “I’ve seen you write code like that before. Would you like me to build it out for you?”

This can help developers focus on the more creative work, yes, but it can also introduce consistency across code without requiring the developer to try to fit code into a restrictive pattern.

Predictive analysis and improved software reliability

This is where AI can anticipate the behavior of code based on patterns and trends. AI tools can be used to predict system overloads, anticipate user behavior, possibly optimize the user experience, and perform preemptive maintenance on areas needing improvement. Another huge benefit would be updating code to meet minimum requirements as the platforms they run on change and update.

Beyond saving programming time, the software can be made to be more reliable and resilient almost entirely on its own.

Speeding up the development/test cycle 

Code always needs to be tested. The typical cycle involves a bit of coding, a bit of testing, a bit of fixing… rinse, wash, repeat. However, because AIs may be able to predict software behavior, they could flag errors before testing even needs to begin.

Development environments have long been able to flag syntax and type-conversion errors, which involve a knowledge of the language structure. But if development environments can start to flag logic errors, this could go a long way to speeding up code delivery and reducing the number of fixing stages necessary for a given project.

Reducing software maintenance costs 

Most software projects, once complete, require a near-constant level of maintenance to keep up with bugs discovered, changes in the platform, and performance issues. I wrote a piece of software that helps facilitate nonprofit donations, and I spend at least 80% of my time not responding to user requests or adding new capabilities, but simply rewriting the code to the constantly changing requirements of payment gateways. If I could reduce any of that thankless and tedious maintenance, a lot more value could be provided to users.

Maintaining the human element 

Despite the addition of AI in the development environment, all indications are that programmers — real, human programmers — will be creating most software for the foreseeable future.

Reducing the amount of time and tedium certainly can help developers stay sharp, but AIs can also monitor overall team performance, providing managers with notification if a team member has been over-utilized, over-extended, or heading to burnout. It can also help reallocate team assignments so that each member is working to their strengths, and help with planning and resource allocation.

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