Efficient SWE Agent Benchmarking via Trajectory-Aware Evaluation

Abstract:Evaluating software engineering agents on realistic benchmarks is costly, since each task may require multi-step code exploration, modification, and test execution. Existing efficient evaluation methods select representative subsets to estimate full-benchmark performance, but are largely result-only: they fit historical pass/fail response matrices or static task semantics, discarding how agents solve problems. We propose PTA-IRT, a Privileged Trajectory-Aware Item Response Theory framework that fuses process and outcome signals. Historical execution trajectories supply process-level evidence beyond pass/fail, such as explored context, attempted edits, and solving paths, which PTA-IRT uses as privileged information for calibration subset selection and ability estimation. Under low calibration budgets, PTA-IRT consistently outperforms prior IRT baselines on score and ranking recovery across four SWE benchmarks. Code and data are publicly available at this https URL.

Software Engineering (cs.SE); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)

arXiv:2609.01603 [cs.SE]

(or arXiv:2609.01603v1 [cs.SE] for this version)

https://doi.org/10.48550/arXiv.2609.01603 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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