The Accelerator

AI Driven QA

An artificial intelligence (AI) driven QA leverages machine learning (ML) and Natural language processing (NLP) algorithms and techniques to build meaningful insights into the product quality. The AI induces this much needed intelligence into the continuous testing pipelines by forecasting possible defects, impacts, gaps, failures and test coverage and help the DevOps teams take informed decisions on run time. No wonder thus that AI is rapidly proving to be the modern day accelerator for testing in DevOps that significantly optimizes the overall testing effort and cycle time.

With AI, QA teams can trigger unattended test cycles, where defects are identified and remedial measures are triggered in run time, based on insights gleaned from historical data sets and past events. This way, the AI engine will ensure that only a robust code progresses from one stage to the next, orchestrating quality across the DevOps pipeline.

An AI engine assumes the task of checking-off code at quality gates, rendering testing autonomous. By analyzing the results of automated tests, an ML algorithm can pass or fail code progression, creating a fully automated workflow. By orchestrating QA processes with AI, QA teams can:

  • Automate quality gates: As ML algorithms determine if the code is a “go” (or) “no go” based on historical data, QA teams can entrust the AI engine to promote the code (or) shut down features with high probability of causing application outage (or) production defects.

  • Predict root causes: Triaging (or) identifying the root cause of a defect is one of the reasons for delays in releasing new features. With patterns and correlations, ML algorithms can trace defects to root causes, with the AI triggering remedial tests before the code progresses. As AI takes these judgment calls, the margin of error is significantly reduced.

  • Leverage precognitive monitoring: ML algorithms scout for symptoms in coding errors that were previously overlooked. The algorithm can then flag these symptoms, such as a high memory usage, as a potential threat that could result in an outage. As corrective steps, the AI engine can automatically spin-up a parallel process to optimize the server-resource consumption.