This blog was originally posted on: https://www.spiceworks.com/tech/devops/guest-article/software-testing-ai-qa/
AI adoption in QA (quality assurance) is a silent revolution that’s helping solve hardware constraints and test cycle bloat for industries, including automotive. AI’s biggest successful adoption in QA is analyzing code changes to run only critical tests, discusses co-CEO and founder of Launchable, Harpreet Singh.
While there is a feeding frenzy around AI chatbots and even AI in crypto, the software development life cycle (SDLC) has been a seedbed over the last few years for incorporating AI into development.
The maturation of CI/CD (continuous integration and continuous delivery) has evolved the software testing arena to be primed for progress. While the overall buzz of AI is reaching industries far and wide, it is the end-user demand for quality and faster throughputs that drives the adoption of AI in software testing.
SDLC processes are only evolving to be more complex. Data-driven pipelines are the only way for teams to combat complexity while speeding up test cycles. While the new wave of QA testing and AI came in like a lamb, the adoption of AI in software testing is spreading and slowly feeling more like a lion.
AI in Software Testing: Unlocking New Possibilities for Quality Control
In the SDLC, testing remains the lowest prioritized phase in terms of tech stack advancement, yet it remains a large friction point in most release cycles. Software testing all too frequently becomes the roadblock preventing developers from achieving quicker, more reliable release cycles because of large test suites, problematic tests, and a general reliance on manual testing.
Testing is where artificial intelligence is making the most impact. By its nature, AI can add value by utilizing data to automate repetitive processes. Software testing is producing a tsunami of data that is being ignored. Using this data intelligently can reduce the bloat felt by engineering teams. With AI-assisted testing, developer friction can be greatly reduced, leading to improved developer experience and workplace efficiency.
Two common scenarios for AI adoption by developers and QA teams for better software testing processes: having too few or too many tests.
Having enough tests is essential for rapidly and accurately identifying errors. With too few tests, developers risk identifying bugs before being sent downstream. As a result, mistakes compound and are more difficult to correct. AI test-generating tools like Mesmer have gained popularity, allowing teams to develop more high-quality tests for their testing program.
The converse dilemma is that organizations frequently deal with testing pipelines that are bloated as a result of having too many tests. This makes testing a critical bottleneck and liability. 75% of DevOps professionals surveyed in 2022Opens a new window reported losing more than 25% productivity to testing, a strong signal that over-testing continues to be harmful to team throughputs.
Running unnecessary tests for every commit can take a long time and risk producing false positives and negatives. Some teams are employing AI to enable large-scale testing as a solution to this problem. This can involve using ML models to collect test suite insights to better identify the critical tests to run, and optimizing test selection.
While there are many approaches to analyzing code changes to assess the impact on tests, Predictive test selection is the next-gen AI approach to its manual counterpart of test impact analysis. It determines which tests to run for a given code change using a machine learning model that has been trained on historical test results. With AI, teams can scale test impact analysis to every project, allowing them to have faster testing cycles while also increasing confidence in their code. The growing adoption of AI in software testing is allowing developers to focus in tandem more on innovation while also speeding up release cycles.
See More: How AI Will Affect Developer Workflows
Emerging AI Victories in Software Testing: Turbocharging the Automotive SDLC
We all see the potential of AI; by using AI to perform more mundane tasks, we free up our experts to perform more complex testing and innovation. While startups and SMBs look to AI for expanding test generation, in long-established industries, including automotive, bloated test suites and long development cycle times result in delivery delays and strain on developer experience.
Infamously, automotive manufacturers battle long development cycle times. The burden of testing complex codebases and embedded components results in delayed failure identification, causing repeat delays.
Coupled with hardware resource constraints from expensive devices at capacity – causing developers to wait for days to get their commits tested – delivery cycles are directly impacted. Teams often rely on parallel testing, but adding new hardware capacity implies going through yearly planning and budgeting exercises which means developers must survive through inefficient processes. All of this challenges automotive manufacturers in the face of steep competition.
The value of AI in automotive software testing cycles is solving the inherent productivity loss due to hardware restraints and test cycle bloat that parallel testing can’t do on its own.
With AI-driven tooling like Predictive Test Selection, these teams are now able to tackle these issues without the heavy lift of generating a custom AI option. A German car company evaded testing bottlenecks with AIOpens a new window resulting in a 90% reduction in test execution times, a 40% reduction in build times, and a 25% reduction in hardware costs.
Data-driven Pipelines for the Win
Instead of months-long DevOps transformations, AI is helping teams see resource savings and faster, data-driven pipelines in weeks – working with existing resources.
While the rest of the world is riding the wave of AI emerging into new avenues, software development and testing have already found a strong home for AI within their pipelines, enabling them to advance their existing pipelines without disruption.
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