SECLLGJul 30

Change2Task: From Repository Changes to Executable Coding Agent Tasks and Environments

arXiv:2607.2859123.2
Predicted impact top 3% in SE · last 90 daysOriginality Incremental advance
AI Analysis

This provides a scalable method for generating executable training and evaluation data for coding agents, addressing a bottleneck in agent development.

Change2Task converts merged pull requests from repository history into executable coding agent tasks on modern code revisions, achieving 79.6% verified task construction success across five task families and recovering 29.2% more verified tasks than a baseline, with up to 98.0% outcome agreement and 10.8% cost reduction.

Scaling coding agents requires a continuing supply of executable data for training, benchmarking, and continuous evaluation. Each task must couple a realistic software state with a specification, development tools, and reliable verification. To expand this supply, we present Change2Task, a system grounded in repository history that converts merged pull requests into verified tasks on healthy modern revisions of the same repository. It aligns historical evidence with evolved code, reconstructs task states through Patch Reversal, Code Mapping, or Agent Reconstruction, and validates the lifecycle from a healthy base to a task state and a restored state. By deriving multiple tasks grounded in developer evidence from maintained environments, Change2Task provides executable data for coding agent training and evaluation while reducing repeated environment setup, storage, and task construction effort. We evaluate the system through five common and widely adopted coding agent task families: Bug Fix, Feature Addition, Test Generation, Application Programming Interface Migration, and Security Repair. Starting from 1,130 source changes eligible for construction, Change2Task achieves 79.6% verified task construction success across these task families. On a matched candidate set, it recovers 29.2% more verified tasks than a construction baseline based on pull requests. Historical and reconstructed cases achieve up to 98.0% matched outcome agreement under agent evaluation, while reuse of modern bases reduces measured expenditure across the complete pipeline by 10.8%.

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