{"id":53174,"date":"2023-11-30T15:21:40","date_gmt":"2023-11-30T20:21:40","guid":{"rendered":"https:\/\/sdtimes.com\/?p=53174"},"modified":"2023-12-06T20:43:53","modified_gmt":"2023-12-07T01:43:53","slug":"the-promise-of-generative-ai-in-low-code-testing","status":"publish","type":"post","link":"https:\/\/sdtimes.com\/ai\/the-promise-of-generative-ai-in-low-code-testing\/","title":{"rendered":"premium<\/span> The promise of generative AI in low-code, testing"},"content":{"rendered":"

Over the past year, software companies have worked hard to incorporate generative AI into their products, doing whatever it takes to incorporate the latest technology and stay competitive.\u00a0<\/span><\/p>\n

One software category that is particularly well-suited to being boosted by AI is low code, as that is already a market that has a goal of making things easier on developers.\u00a0<\/span><\/p>\n

Just as low code lowered the bar to entry for development, generative AI will have a similar impact because of such things as code completion and workflow automation. But Kyle Davis, VP analyst at Gartner,\u00a0 believes that the two technologies will interact in more of a collaborative effort than a competitive way, at least for citizen developers. \u201cEven though you could use generative AI to generate code, if you don’t understand what the code is doing, there’s no way to validate that it’s correct,\u201d he said. \u201cUsing low code, it’s declarative, so you can look at what’s there on the screen and say, \u2018does that make sense?\u2019\u201d<\/span><\/p>\n

RELATED CONTENT: A guide to low-code vendors that incorporate generative AI capabilities<\/a><\/strong><\/p>\n

However, Davis also says it\u2019s really too new of a market to make any real predictions. \u201cWe’ve seen a lot of failure, we’ve seen a lot of success, because it’s so early days that, at best, you’re kind of experimenting with this now. But the hope is that it can offer a lot of potential,\u201d he explained.\u00a0<\/span><\/p>\n

According to Davis, there are three main ways AI is being incorporated into low-code platforms.\u00a0<\/span><\/p>\n

First, there are generative AI capabilities that are designed to improve the developer experience.<\/span><\/p>\n

Second, there are generative AI capabilities targeting the end users of the application created using low code. \u201cSo embedding like a Copilot or ChatGPT type control within the application. That way the user of the application can ask questions about the app\u2019s data, as an example,\u201d Davis said.\u00a0<\/span><\/p>\n

Third, there are features related to process improvement. \u201cWhen you’re creating workflows or automation, there’s usually a lot of steps that are very human-centric, when it comes to generating data or categorizing data or whatnot,\u201d Davis said. \u201cAnd so we’ve seen a lot of those steps being not displaced by a generative AI step, but rather kind of preceded by a generative AI step.\u201d<\/span><\/p>\n

He gave the example of a workflow that is designed to help hiring managers create requirements for a job position. Usually the hiring manager has to go in and manually add information, like the name of the position, the description, and other requirements. But, Davis said, \u201cIf generative AI were to step in first and do a draft of that, it allows the hiring manager to come in and just make refinements.\u201d\u00a0<\/span><\/p>\n

Davis believes that a major challenge experienced by these low-code vendors is the added work placed on them to enable this integration to work. Low code is very declarative and abstracted away, and the constructs that make up a low-code application are proprietary to the platform it belongs to, which requires the vendors to either have their own LLM or be able to take user prompts and create all the constructs within their platform to represent what was asked.\u00a0<\/span><\/p>\n

\u201cThere’s a lot they can leverage from existing LLMs and, and generative AI vendors, but there’s still pieces that they have to do themselves,\u201d he said.\u00a0<\/span><\/p>\n

Using generative AI in testing is another promising area<\/b><\/p>\n

Combining generative AI and testing is also a promising mashup, according to Arthur Hicken, chief evangelist at testing software company Parasoft<\/a>. \u201cWe’re still at a relatively early stage, so it’ll be interesting to see how much of it is real and how much of it pans out,\u201d he said. \u201cIt certainly shows a lot of promise in the ability to generate code, but perhaps more so in the ability to generate tests \u2026 I don’t believe we\u2019re there yet, but we are seeing some pretty interesting capabilities that, you know, didn’t exist a year or two ago.\u201d<\/span><\/p>\n