highly usable, ‘too many unknowns’ for production – Computerworld

What’s your responsibility at Capgemini? “I look after software that’s in products. The software is so pervasive that you actually need different categories of software and differnent ways it’s developed. And, you can imagine that there’s a huge push right now in terms of moving software [out the door].”

How did your journey with AI-generated software begin? “Originally, we thought about generative AI with a big focus on sort of creative elements. So, a lot of people were talking about building software, writing stories, building websites, generating pictures and the creation of new things in general. If you can generate pictures, why can’t you generate code? If you can write stories, why not write user stories or requirements that go into building software. That’s the mindset of the shift going on, and I think the reality is it’s a combination of a market-driven dynamic. Everyone’s kind of moving toward wanting to build a digital business. You’re effectively now competing with a lot of tech companies to hire developers to build these new digital platforms.

“So, many companies are thinking, ‘I can’t hire against these large tech companies out here in the Bay Area, for example. So, what do I do?’ They turn to AI…to deal with the fact that [they] don’t have the talent pool or the resources to actually build these digital things. That’s why I think it’s just a perfect storm, right now. There’s a lack of resources, and people really want to build digital businesses, and suddenly the idea of using generative AI to produce code can actually compensate for [a] lack of talent. Therefore, [they] can push ahead on those projects. I think that’s why there’s so much emphasis on [genAI software augmentation] and wanting to build towards that.”

How have you been using AI to create efficiencies in software development and engineering? “I would break out the software development life cycle almost into stages. There is a pre-coding phase. This is the phase where you’re writing the requirements. You’re generating the user stories, and you create epics. Your team does a lot of the planning on what they’re going to build in this area. We can see generative AI having an additive benefit there just generating a story for you. You can generate requirements using it. So, it’s helping you write things, which is what generative AI is good at doing, right? You can give it some prompts of where you want to go and it can generate these stories for you.

“The second element is that [software] building phase, which is coding. This is the phase people are very nervous about it and for very good reason, because the code generation aspect of generative AI is still almost like a little bit of wizardry. We’re not quite sure how it gets generated. And then there’s a lot of concerns regarding security, like where did this get generated from? Because, as we know, AI is still learning from something else. And you have to ask [whether] my generated code is going to be used by somebody else? So there’s a lot of interest in using it, but then there’s a lot of hesitancy in actually doing the generation side of it.

“And then you have the post-coding phase, which is everything from deployment, and testing, and all that. For that phase, I think there’s a lot of opportunity for not just generative AI, but AI in general, which is all focused around intelligent testing. So, for instance, how do you generate the right test cases? How do you know that you’re testing against the right things? We often see from a lot of clients where effectively over the years they’ve just added more and more tests to that phase, and so it got bigger and bigger and bigger. But, nobody’s actually gone in and cleaned up that phase. So, you’re running a gazillion tests. Then you still have a bunch of defects because no one’s actually cleaned up the tests of defects they are trying to detect. So, a lot of this curates better with generative AI. Specifically, it can perform a lot of test prioritization. You can look at patterns of which tests are being used and not used. And, there’s less of a concern about something going wrong in with that. I think AI tools make a very big impact in that area.


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