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Regression Testing (RT) ensures that code modifications do not break existing functionality. However, running a full test suite is often costly and time-consuming, making optimization strategies essential. This Systematic Literature Review (SLR) analyzes 67 papers published between 2021–2025, focusing on Test Case (TC) Prioritization (TCP), Test Case Minimization (TCM), and Test Case Selection (TCS). Unlike prior reviews, this study places traceability at the center of the analysis, examining how its mechanisms influence these techniques in balancing execution time and fault detection effectiveness. By linking TCs and faults to code changes and requirements, traceability is identified as a key driver in enhancing test selection and prioritization. Additionally, a connectivity-based perspective supports maintainability by ensuring that tests evolve alongside changing software artifacts. The review is structured around four research questions, addressing the types of traceability data used, solution approaches, target systems, and validation practices. It offers practical insights and highlights research gaps. Key challenges such as automation, scalability, and integration with modern development workflows are also discussed.

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Regression Testing via Traceability: A Systematic Literature Review

  • 会议论文

  • 发表日期：

    2026年

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  • Moldovan Andrada-Mihaela-Nicoleta

摘要

Regression Testing (RT) ensures that code modifications do not break existing functionality. However, running a full test suite is often costly and time-consuming, making optimization strategies essential. This Systematic Literature Review (SLR) analyzes 67 papers published between 2021–2025, focusing on Test Case (TC) Prioritization (TCP), Test Case Minimization (TCM), and Test Case Selection (TCS). Unlike prior reviews, this study places traceability at the center of the analysis, examining how its mechanisms influence these techniques in balancing execution time and fault detection effectiveness. By linking TCs and faults to code changes and requirements, traceability is identified as a key driver in enhancing test selection and prioritization. Additionally, a connectivity-based perspective supports maintainability by ensuring that tests evolve alongside changing software artifacts. The review is structured around four research questions, addressing the types of traceability data used, solution approaches, target systems, and validation practices. It offers practical insights and highlights research gaps. Key challenges such as automation, scalability, and integration with modern development workflows are also discussed.