Comprehensive Review on Innovation in Software Testing: Advancing Methods with State-of-the-Art Intelligent Platforms - Vamsi Krishna Talasila (International Journal of Intelligent Systems and Applications in Engineering, 2025)
Software engineering and DevOps keep changing faster than most teams can catch their breath, and that shift has pushed testing into new territory. What used to be a fairly manual, rule-based practice is now leaning hard on AI to make sense of growing complexity. This paper takes a close look at how intelligent testing frameworks those that use machine learning, natural language processing, and other AI techniques fit into modern CI/CD pipelines. It walks through how testing evolved from scripted checks to systems that can, at least in theory, adapt and even repair themselves. The review digs into the architectures and algorithms behind these systems and how they’re actually used in industry. Some approaches, like reinforcement learning or NLP-based test generation, appear to raise fault detection rates and keep pipelines running with fewer interruptions. That said, the story isn’t all progress. These systems depend heavily on data quality, and bias or noise can easily skew predictions. Scalability sounds great in principle, but real-world environments often complicate it. The paper argues that explainability and ongoing validation aren’t just nice-to-haves they’re what make autonomous testing trustworthy. From a broader view, intelligent testing seems to shift quality assurance from catching errors after the fact to something more proactive and self-correcting. Whether that transformation truly holds up across teams and toolchains, though, still depends on how well the AI can align with human judgment. The work here nudges the field closer to testing ecosystems that are resilient, transparent, and hopefully just a bit more human-aware.