Human-In-The-Loop Automatic Program Repair

TL;DR

Learn2fix is a human-in-the-loop semi-automatic program repair method that efficiently trains an automatic bug oracle through systematic user queries, enabling high-quality repairs with minimal user effort.

Contribution

It introduces a novel approach for bug repair that combines user interaction with automated testing, reducing manual effort and improving patch quality.

Findings

Learn2fix requires fewer than 20 user queries to train an effective bug oracle.

The approach produces higher-quality patches compared to manual test suites.

Systematic queries improve the accuracy of bug detection and repair.

Abstract

We introduce Learn2fix, the first human-in-the-loop, semi-automatic repair technique when no bug oracle--except for the user who is reporting the bug--is available. Our approach negotiates with the user the condition under which the bug is observed. Only when a budget of queries to the user is exhausted, it attempts to repair the bug. A query can be thought of as the following question: "When executing this alternative test input, the program produces the following output; is the bug observed"? Through systematic queries, Learn2fix trains an automatic bug oracle that becomes increasingly more accurate in predicting the user's response. Our key challenge is to maximize the oracle's accuracy in predicting which tests are bug-exposing given a small budget of queries. From the alternative tests that were labeled by the user, test-driven automatic repair produces the patch. Our experiments demonstrate that Learn2fix learns a sufficiently accurate automatic oracle with a reasonably low labeling effort (lt. 20 queries). Given Learn2fix's test suite, the GenProg test-driven repair tool produces a higher-quality patch (i.e., passing a larger proportion of validation tests) than using manual test suites provided with the repair benchmark.