{"id":55324,"date":"2024-08-01T14:26:56","date_gmt":"2024-08-01T18:26:56","guid":{"rendered":"https:\/\/sdtimes.com\/?p=55324"},"modified":"2024-08-02T09:06:20","modified_gmt":"2024-08-02T13:06:20","slug":"generative-ai-development-requires-a-different-approach-to-testing","status":"publish","type":"post","link":"https:\/\/sdtimes.com\/test\/generative-ai-development-requires-a-different-approach-to-testing\/","title":{"rendered":"Generative AI development requires a different approach to testing"},"content":{"rendered":"

Generative AI has the potential to have a positive impact on software development and productivity, but with that increased productivity comes increased pressure on software testing.\u00a0<\/span><\/p>\n

If you can generate five or even 10 times the amount of code you previously could, that’s also five to 10\u00a0 times more code that needs to be tested.\u00a0<\/span><\/p>\n

\u201cMany CFOs right now are looking at $30 per month per developer to go get them a GitHub Copilot or similar product,\u201d said Jim Scheibmeir, senior director analyst at Gartner<\/a>. \u201cAnd I feel like we’ve kind of forgotten that frequently a bottleneck in software development is not the writing of code, but the testing of code. We’re gonna make developers so much more productive, which includes making them more productive at writing defects.\u201d<\/span><\/p>\n

Unlike AI-assisted dev tools where developers want to write more code, the goal with AI-assisted testing tools is to enable less testing. For instance, according to Scheibmeir, things like test impact analysis tools can create a testing strategy that is properly sized for the actual code change that is being pushed, so that only the tests that need to be run are run, rather than just running every test you have for every change.\u00a0<\/span><\/p>\n

\u201cThese tools provide focus for testers,\u201d he said. \u201cAnd it’s so very difficult to give testers focus today. There’s this feeling like we must go test all of the things and yet we’re always crunched on time.\u201d<\/span><\/p>\n

Arthur Hicken, chief evangelist at Parasoft<\/a>, agrees that we\u2019ve already reached a point where test suites are taking hours, or even days, to complete, and using generative AI to help optimize test coverage can help with that.\u00a0 \u201cYou can put together with AI these days a pretty good estimation of what you need to do to validate a change,\u201d he said.<\/span><\/p>\n

Generative AI helping with test generation, management, and more<\/b><\/h5>\n

Beyond helping testers test less, AI is creeping into other aspects of the process to make it more efficient end to end. For instance, Madhup Mishra, SVP at SmartBear<\/a>, says that generative AI can now be used to create the tests themselves. \u201cThe tester can actually express their software test in simple English, and AI can actually create the automated test on their behalf,\u201d he said.\u00a0<\/span><\/p>\n

\u201cBehind the scenes, GenAI should be understanding the context of the test, understanding what’s happening on the screen, and they can actually come up with a recommended test that actually solves the user\u2019s problem without the user having to do a lot more,\u201d he said.<\/span><\/p>\n

Scheibmeir explained that the idea of making test generation easier had already been explored by low-code and no-code tools with their intuitive drag-and-drop interfaces, and generative AI is now taking it to that next level.\u00a0<\/span><\/p>\n

And according to Eli Lopian, CEO of Typemock<\/a>, AI is really good at exploring edge cases and may come up with scenarios that a developer might have missed. He believes that it can understand complex interactions in the codebase that the tester might not see, which can result in better coverage.\u00a0<\/span><\/p>\n

AI can also help with generation of test data, such as usernames, addresses, PIN codes, phone numbers, etc. According to Mishra, generating test data can often be a lengthy, time-consuming process because testers have to think up all the possible variations, such as the characters that can go in a name or the country codes that come before phone numbers.\u00a0<\/span><\/p>\n

\u201cGenerative AI can create all the different combinations of test data that you can ultimately use to be able to test all the corner cases,\u201d Mishra explained.\u00a0<\/span><\/p>\n

Another potential opportunity is using AI in test management. Companies often have a repository of all the different tests they have created, and AI can sort through all that and make suggestions on which to use. This allows testers to utilize what they\u2019ve already created and free up more of their time to create new tests they need, explained Mishra.\u00a0<\/span><\/p>\n

Parasoft\u2019s Hicken added that AI could sort through older tests and validate if they are still going to work. For instance, if a test is capturing today\u2019s date, then that test won\u2019t work tomorrow.\u00a0<\/span><\/p>\n

AI might make testing more accessible, but won\u2019t eliminate need for it<\/b><\/h5>\n

Together, all of these AI enhancements are helping organizations take more responsibility for software quality themselves, where in the past they might have outsourced testing, Scheibmeir said.\u00a0<\/span><\/p>\n

Similar to the citizen developer movement, the capabilities for testing that are now available make it easier for anyone to run a test, so it doesn\u2019t require such specialized skills like it once did.\u00a0<\/span><\/p>\n

\u201cThe hype and capabilities that generative AI are offering have brought some of these organizations back to the table of should we own more of that testing ourselves, more of that test automation ourselves,\u201d Scheibmeir said.\u00a0<\/span><\/p>\n

However, it\u2019s still important to keep in mind that AI does have its drawbacks. According to Lopian, one of the biggest downsides is that AI doesn\u2019t understand the emotion that software is supposed to give you.\u00a0<\/span><\/p>\n

\u201cAI is going to find it difficult to understand when you’re testing something and you want to see, is the button in the right place so that the flow is good? I don’t think that AI would be as good as humans in that kind of area,\u201d he said.<\/span><\/p>\n

It\u2019s also important to remember that AI won\u2019t replace testers, and testers will still need to keep an eye on it for now to ensure all the right coverage and the right tests are happening. Lopian likened it to a \u201cclever intern\u201d that you still need to keep an eye on to make sure they\u2019re doing things correctly.\u00a0<\/span><\/p>\n

AI\u2019s impact on development skills will drive need for quality to shift further left<\/b><\/h5>\n

Another important consideration is the potential that if developers rely too heavily on generative AI, their development skills might atrophy, Mishra cautioned.\u00a0<\/span><\/p>\n

\u201cHow many times have you gotten an Uber and realized the Uber driver knows nothing about where you’re going, they\u2019re just blindly following the direction of the GPS, right? So that’s going to happen to development, and QA needs to sort of come up to speed on making sure that quality is embedded right from the design phase, all the way to how that application code will behave in production and observing it,\u201d he said.\u00a0\u00a0<\/span><\/p>\n

Hicken agrees, likening it to how no one memorizes phone numbers anymore because our phones can store it all.\u00a0<\/span><\/p>\n

\u201cIf I was a young person wanting to have a good long-term career, I would be careful not to lean on this crutch too much,\u201d he said.<\/span><\/p>\n

This isn\u2019t to say that developers will totally forget how to do their jobs and that in 20, 30 years no one will know how to create software without the help of AI, but rather that there will emerge a new class of \u201ccasual developers,\u201d which will be different from citizen developers.<\/span><\/p>\n

Hicken believes this will lead to a more stratified developer community where you\u2019ve got the \u201cOG coders\u201d who know how the computer works and how to talk to it, and also casual developers who know how to ask the computer questions \u2014 prompt engineers.\u00a0<\/span><\/p>\n

\u201cI think we are going to have to better define the people that are creating and managing our software, with roles and titles that help us understand what they’re capable of,\u201d he said. \u201cBecause if you just say software engineer, that person needs to actually understand the computer. And if you say developer, it might be that they don’t need to understand the computer.\u201d<\/span><\/p>\n

\n

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Generative AI has the potential to have a positive impact on software development and productivity, but with that increased productivity comes increased pressure on software testing.\u00a0 If you can generate five or even 10 times the amount of code you previously could, that’s also five to 10\u00a0 times more code that needs to be tested.\u00a0 … continue reading<\/a><\/p>\n","protected":false},"author":752,"featured_media":55325,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"om_disable_all_campaigns":false,"cybocfi_hide_featured_image":"","footnotes":"","_links_to":"","_links_to_target":""},"categories":[1],"tags":[16528,1141,4],"coauthors":[11687],"acf":[],"yoast_head":"\nGenerative AI development requires a different approach to testing - SD Times<\/title>\n<meta name=\"description\" content=\"Generative AI has the potential to have a positive impact on software development and productivity and increased pressure on software testing.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/sdtimes.com\/test\/generative-ai-development-requires-a-different-approach-to-testing\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Generative AI development requires a different approach to testing - 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