Is Self-Healing Code the Future of Software Development?

We already have automated processes that detect bugs, test solutions, and generate documentation, notes a new post on Stack Overflow's blog. But beyond that, several developers "have written in the past on the idea of self-healing code. Head over to Stack Overflow's CI/CD Collective and you'll find numerous examples of technologists putting this ideas into practice." Their blog post argues that self-healing code "is the future of software development." When code fails, it often gives an error message. If your software is any good, that error message will say exactly what was wrong and point you in the direction of a fix. Previous self-healing code programs are clever automations that reduce errors, allow for graceful fallbacks, and manage alerts. Maybe you want to add a little disk space or delete some files when you get a warning that utilization is at 90% percent. Or hey, have you tried turning it off and then back on again? Developers love automating solutions to their problems, and with the rise of generative AI, this concept is likely to be applied to both the creation, maintenance, and the improvement of code at an entirely new level... "People have talked about technical debt for a long time, and now we have a brand new credit card here that is going to allow us to accumulate technical debt in ways we were never able to do before," said Armando Solar-Lezama, a professor at the Massachusetts Institute of Technology's Computer Science & Artificial Intelligence Laboratory, in an interview with the Wall Street Journal. "I think there is a risk of accumulating lots of very shoddy code written by a machine," he said, adding that companies will have to rethink methodologies around how they can work in tandem with the new tools' capabilities to avoid that. Despite the occasional "hallucination" of non-existent information, Stack Overflow's blog acknowledges that large-language models improve when asked to review their response, identify errors, or show its work. And they point out the project manager in charge of generative models at Google "believes that some of the work of checking the code over for accuracy, security, and speed will eventually fall to AI." Google is already using this technology to help speed up the process of resolving code review comments. The authors of a recent paper on this approach write that, "As of today, code-change authors at Google address a substantial amount of reviewer comments by applying an ML-suggested edit. We expect that to reduce time spent on code reviews by hundreds of thousands of hours annually at Google scale. Unsolicited, very positive feedback highlights that the impact of ML-suggested code edits increases Googlers' productivity and allows them to focus on more creative and complex tasks...." Recently, we've seen some intriguing experiments that apply this review capability to code you're trying to deploy. Say a code push triggers an alert on a build failure in your CI pipeline. A plugin triggers a GitHub action that automatically send the code to a sandbox where an AI can review the code and the error, then commit a fix. That new code is run through the pipeline again, and if it passes the test, is moved to deploy... Right now his work happens in the CI/CD pipeline, but [Calvin Hoenes, the plugin's creator] dreams of a world where these kind of agents can help fix errors that arise from code that's already live in the world. "What's very fascinating is when you actually have in production code running and producing an error, could it heal itself on the fly?" asks Hoenes... For now, says Hoenes, we need humans in the loop. Will there come a time when computer programs are expected to autonomously heal themselves as they are crafted and grown? "I mean, if you have great test coverage, right, if you have a hundred percent test coverage, you have a very clean, clean codebase, I can see that happening. For the medium, foreseeable future, we probably better off with the humans in the loop." Last month Stack Overflow themselves tried an AI experiment that helped users to craft a good title for their question.

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