Software Testing in the SDLC: A Structured Review of Testing Levels, Principles, and Lifecycle Practices
DOI:
https://doi.org/10.66108/mna.v5i02.99Keywords:
Software Testing, Software Development Life Cycle, Software Testing Life Cycle, Test Automation, Continuous testing, DevOpsAbstract
Software testing is one of the most crucial activities around Software Testing and is an essential activity in most of the stages of a Software Development Life Cycle (SDLC). Traditional presentations, however, tend to focus on testing as a last-line defect identification operation, and offer little insight into the interplay between various levels of testing, principles, phases of the testing life cycle, test tools, and current best practices. This paper has the intention of providing a structured narration review on software testing in SDLC. The review presents a synthesis of foundational literature, software-testing standards and latest research on testing levels, principles of testing, Software Testing Life Cycle (STLC), test automation, continuous testing, DevOps/CI/CD and testing assisted with AI. The findings indicate that the complementary activities at different levels of testing (unit, integration, system, and acceptance) should be planned and should be accompanied by a systematic quality assurance procedure throughout the life cycle of the testing. The deliverables of the review also highlight the potential for improving speed and repeatability of feedback via automation and continuous testing as well as the ability to take quality actions across the development and operational lifecycle using shift-left and shift-right. Then there is AI-driven exam generation, prioritization and maintenance which presents opportunities, but there's a caveat – they must be of high quality, context-appropriate and validated and require human oversight to be effective. In summary, the results align with an approach that is “botched” and “site” dependent whereby the selected test methods and tools are dependent on software characteristics, quality goals, development methodology, and risks. In addition to this analysis, the review points out research opportunities relating to automated testing, continuous testing, research into AI assisted testing and empirical comparison of testing across various SDLC environments.
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