Comparative Analysis of Test Coverage Metrics in Agile vs. Traditional Software Development

Test coverage metrics are broadly used in software engineering to assess the thoroughness of automated testing, up till now little empirical work has directly compared their behavior across Agile and Traditional development methodologies. This research conducts a mixed-method analysis combining industrial case studies, mining of public repositories, controlled experiments, and machine learning modeling to study statement, branch, and path coverage patterns in 30 projects spanning multiple domains and languages. Statistical analysis has x-rayed that Agile projects achieved significantly higher and more stable statement and branch coverage than Traditional projects, with smaller but consistent differences in path coverage. Continuous Integration (CI) occurrence was robustly connected with coverage stability in Agile settings but yields mixed benefits in Traditional contexts. Coverage volatility appeared as a robust predictor of methodology, enabling a Random Forest classifier to differentiate Agile from Traditional projects with 87% accuracy. Contextual features such as project size, domain, and language also moderate coverage results, with embedded systems exhibiting systematically lower path coverage due to hardware and regulatory constraints. These investigation revealed actionable guidance for test managers, including the use of coverage volatility and CI frequency as process health indicators, and highlight the trade-offs between earlier, incremental coverage growth in Agile and concentrated, late-phase coverage in Traditional methods.