SWE-Future: Forecast-Conditioned Data Synthesis for Future-Oriented Software Engineering Agents
Realistic coding-agent benchmarks often replay public GitHub issues and pull requests, making them vulnerable to overlap with model pretraining, fine-tuning, synthetic-data generation, or benchmark-driven model selection. Fully synthetic tasks avoid direct historical replay, but can drift away from real repository needs. We propose SWE-Future, a forecast-conditioned data synthesis method for future-oriented coding tasks. Given a forecast snapshot at time $T_0$, the method uses only pre-$T_0$ repository evidence to forecast future feature implementation/enhancement, bugfix, and refactor task families. We first validate this forecasting step retrospectively: after forecasts are fixed, later...
| Authors | Qiao Zhao, JianYing Qu, Jun Zhang et al. |
|---|---|
| Primary category | cs.SE |
| Published | 17 June 2026 |
| Last updated at source | 17 June 2026 |
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