Test Suite Minimization (TSM) reduces the size of test suites while preserving their fault detection capability. In black-box TSM, reduction is performed without relying on production-code instrumentation. While several black-box TSM approaches have explored metrics like test logs or test similarity, these often suffer from scalability and efficiency issues. Recently, change history has been explored as a lightweight and scalable indicator for guiding black-box TSM. However, existing approaches treat historical modifications uniformly, ignoring the temporal dynamics of software evolution where recently modified code tends to be more fault-prone. To address this limitation, we introduce temporal modeling into black-box TSM and propose Temporal Risk-driven Test Suite Minimization (TRTM). TRTM extracts modification history from version-control metadata and applies exponential temporal attenuation to weight changes based on recency, producing time-weighted class-level risk scores that reflect fault-proneness. Next, it determines dependencies between test cases and production classes by constructing static call graphs derived solely from test code, preserving the black-box setting. The risk scores of the classes exercised by each test case are then aggregated using statistical measures such as Average and Geometric Mean to compute a risk score for the test case. Finally, test cases with the highest risk scores are selected to construct the reduced suite. Evaluation on a large dataset containing 14 projects with 631 versions shows that TRTM consistently outperforms the state-of-the-art baseline, achieving a mean Accuracy of 0.72 (vs. 0.66) and Fault Detection Rate (FDR) of 0.75 (vs. 0.69), while also reducing execution time.

Black-box Test Suite Minimization (TSM) aims to remove redundant test cases from a test suite while retaining its fault detection capability without analyzing the production code. This makes it particularly efficient to be used in industry projects. These techniques utilize metrics such as test history, commit complexity or test code diversity (or similarity) to guide test case selection. Similarly, change-proneness (CP) can also guide to successful test case selection as it indicates the likelihood of having faults. We propose CP as a metric for TSM and implement into an approach, Change-proneness based Test suite Minimization (CTM), where relationships between test cases and their depending classes are measured using CP values. CTM first calculates class-level CP, followed by identifying the dependency between the test cases and classes. Then the association between test cases and their related classes are calculated using various statistical measures such as geometric mean etc. Finally test cases with the highest association values are selected. To demonstrate the effectiveness and efficiency of CP as a TSM metric, we compared CTM with state of the art, AST-based Test case Minimizer (ATM) on 15 Java projects with 617 versions. The experimental results demonstrate that CTM achieves the same average fault detection accuracy as ATM (0.67) while reducing execution time by 114.5 folds.