Human-In-The-Loop Automatic Program Repair

Abstract

We introduce LEARN2FIX, the first human-in-theloop, 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-revealing 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.1 We present a randomized variant of the algorithm presented by Holub et al. [10]. We do not need to construct two classifiers for each unlabeled data point in a fixed-size sample. Instead, our sample is continuously generated.

FDG: A Precise Measurement of Fault Diagnosability Gain of Test Cases

“…QFID differs from ENLIGHTEN in that it does not require a full test suite. Recently, Böhme et al proposed Learn2Fix [42], a human-inthe-loop program repair technique. Learn2Fix generates test cases by mutating the failing test and allowing the user label them in the order of their likelihood of failing.…”