Efficient SWE Agent Benchmarking via Trajectory-Aware Evaluation
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…
We haven't written up this one. arXiv cs.AI has the full story — the link below goes straight to it.
