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Efficient Time-Aware Prioritization with Knapsack Solvers ABSTRACT
| Content Provider | CiteSeerX |
|---|---|
| Author | Alspaugh, Sara Walcott, Kristen R. Belanich, Michael Kapfhammer, Gregory M. Soffa, Mary Lou |
| Abstract | Regression testing is frequently performed in a time constrained environment. This paper explains how 0/1 knapsack solvers (e.g., greedy, dynamic programming, and the core algorithm) can identify a test suite reordering that rapidly covers the test requirements and always terminates within a specified testing time limit. We conducted experiments that reveal fundamental trade-offs in the (i) time and space costs that are associated with creating a reordered test suite and (ii) quality of the resulting prioritization. We find knapsack-based prioritizers that ignore the overlap in test case coverage incur a low time overhead and a moderate to high space overhead while creating prioritizations exhibiting a minor to modest decrease in effectiveness. We also find that the most sophisticated 0/1 knapsack solvers do not always identify the most effective prioritization, suggesting that overlap-aware prioritizers with a higher time overhead are useful in certain testing contexts. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Knapsack Solver Abstract Efficient Time-aware Prioritization Knapsack Solver Specified Testing Time Limit Time Overhead Dynamic Programming Effective Prioritization Knapsack-based Prioritizers Core Algorithm Test Case Coverage Test Suite Space Cost Regression Testing Low Time Overhead Test Requirement High Space Modest Decrease Overlap-aware Prioritizers Reordered Test Suite Fundamental Trade-off |
| Content Type | Text |
| Resource Type | Article |
