Trajectory-Aware Benchmark Subset Selection for Cost-Efficient Software Engineering Agent Regression Testing
This work proposes a trajectory-aware subset selection approach that replaces random sampling with deterministic selection based on trajectory embeddings, and shows that a 10% trajectory-aware subset keeps the median estimation error below 5% while cutting token cost by roughly 90%.
Mahmoud Ayyad, Ze-Hao Wang, Ji-Ho Shin et al.
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