BENCHMARK AI CODING BY TIME-TO-RESOLUTION AND COST
A community discussion calls for SWE benchmarks that track end-to-end time-to-resolution, resolve rate, and cost—not just accuracy. For AI in the SDLC, these me...
A community discussion calls for SWE benchmarks that track end-to-end time-to-resolution, resolve rate, and cost—not just accuracy. For AI in the SDLC, these metrics better reflect throughput, latency, and actual ROI than pass/fail scores.
Latency and cycle time directly impact developer throughput and delivery predictability.
Cost-per-resolution and resolve rate expose real productivity gains versus raw model accuracy.
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terminal
Instrument tasks to record start-to-merge time, resolve rate, human-in-the-loop minutes, and cost per task across models/agents.
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terminal
Run A/B across AI setups with the same task pool and track median/95p cycle time, rework counts, and rollback rates.
Legacy codebase integration strategies...
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Add telemetry in issue trackers and CI to capture timestamps, AI usage, and costs; baseline current SLAs before rollout.
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Pilot on one repo and gate expansion on improved median and 95p time-to-resolution without increasing defects.
Fresh architecture paradigms...
- 01.
Define tasks with clear exit criteria and log prompts, responses, timings, and cost from day one.
- 02.
Choose tools that expose latency, token usage, and cost via APIs to enable standardized benchmarking.