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

Imagine you're a developer trying to train an AI coding assistant. You need a steady stream of realistic coding tasks that not only reflect real-world scenarios but also come with a reliable way to verify their correctness. Currently, many systems rely on manually crafted tasks or outdated benchmarks, which can lead to issues like irrelevant tasks or environments that don't match current code states — this is what's called a lack of alignment with real-world coding practices. This misalignment can result in wasted time and resources, as the AI struggles to adapt to the actual coding environment it will face. To address this, Change2Task offers a solution that leverages the history of code changes in repositories to automatically generate verified coding tasks. By analyzing merged pull requests, it reconstructs the necessary context and state for each task, ensuring that the tasks are grounded in real developer activity. The system employs techniques like Patch Reversal and Code Mapping to validate the tasks, making them more relevant and executable. Compared to previous methods, Change2Task not only increases the number of verified tasks generated but also reduces the overhead associated with setting up these tasks. This means that for developers building AI coding agents, there's now a more efficient way to create and evaluate training data that closely mirrors the complexities of real-world software development.

Change2Task introduces a novel method for generating executable tasks from historical code changes.

The evaluation shows a significant improvement in task construction success compared to existing baselines.