Diffblue: How does 'AI-assisted' software development work?

This is a guest post for the Computer Weekly Developer Network written by Mathew Lodge in his capacity as CEO of Diffblue — the company is known for its work as it aims to ‘revolutionizse’ software engineering through Artificial Intelligence (AI) and offer what it calls an #AIforCode experience.

Diffblue’s Lodge reminds us that, in the last decade, automation of software development pipelines has rapidly taken off as more teams have adopted DevOps practices and cloud-native architecture.

Automated software pipelines have led to The Rise of The Bots: robot assistants within the continuous integration loop that automate tedious and repetitive tasks like updating project dependencies to avoid security flaws.

Lodge says that today, bots can generate ‘pull requests’ (a pull request is a method of submitting contributions to an open or other development project) to update dependencies and those requests are reviewed by other bots and, if they pass the tests, automatically merged.

Lodge writes as follows…

The crucial part that makes all of this [AI coding] work is tests.

Without tests to quickly validate commits, automated pipelines will risk automatically promoting junk – which is much harder and slower to fix later in the software delivery process.

In his canonical 2006 article on Continuous Integration, Martin Fowler pithily notes that: “Imperfect tests, run frequently, are much better than perfect tests that are never written at all.”

AI for code: developing at scale

Writing tests is like eating healthily and drinking enough water: everyone aspires to do it, but life tends to get in the way.

It’s often the least enjoyable part of development and it takes time and attention away from the more interesting stuff. So automation seems like it would be a great fit – except that the rules-based automation that works well for dependency-checking bots, does not work well for automating test generation; it’s a much harder problem to solve.

AI-based test-writing approaches that apply new algorithms to the problem have emerged in the last few years. Machine learning-based tools can look at browser-based UI testing code, compare it to the Document Object Model… and suggest how to fix failing tests based on training data from analysing millions of UI tests.

But much more code will have to be written by AI to move the needle.

Gartner has estimated that by 2021, demand for application development will grow five times faster than tech teams can deliver. So we’re now seeing the emergence of AI that writes full unit test code, by analysing the program to understand what it does and connecting inputs to outputs.

While the tests aren’t perfect, as no one has solved the halting problem and other well-known challenges in program analysis, the tests are good enough – and infinitely better than perfect tests that were never written.

Benefits beyond automation

AI for code can do more than simply increase the speed at which developers work: it can actually improve the quality of the finished software product, and reduce the amount of required debugging. It can quickly complete repetitive tasks, without losing interest or making mistakes as humans sometimes do. Automating the boring (but necessary) parts of the job can also prevent burnout and increase job satisfaction at a time when companies have to compete for the best talent.

Diffblue CEO Lodge: AI for code can help shoulder the (work) load.

With AI for code, the developers of tomorrow will have more freedom to innovate in the way only people can – benefits that go beyond what’s possible with automation alone. Expect the future of software development to be increasingly AI-assisted.

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