SEApr 19, 2020

Interactive Patch Filtering as Debugging Aid

arXiv:2004.08746v225 citations
AI Analysis

This addresses the challenge of making low-precision repair tools useful for developers by leveraging incorrect patches as debugging aids, though it is incremental as it builds on existing repair tool concepts.

The paper tackles the problem of low precision in program repair tools by proposing an interactive filtering approach that asks developers questions to filter out incorrect patches, showing that it improves repair performance with 62.5% more successfully repaired bugs and 25.3% less debugging time on average.

It is widely recognized that program repair tools need to have a high precision to be useful, i.e., the generated patches need to have a high probability to be correct. However, it is fundamentally difficult to ensure the correctness of the patches, and many tools compromise other aspects of repair performance such as recall for an acceptable precision. In this paper we ask a question: can a repair tool with a low precision be still useful? To explore this question, we propose an interactive filtering approach to patch review, which filters out incorrect patches by asking questions to the developers. Our intuition is that incorrect patches can still help understand the bug. With proper tool support, the benefit outweighs the cost even if there are many incorrect patches. We implemented the approach as an Eclipse plugin tool, InPaFer, and evaluated it with a simulated experiment and a user study with 30 developers. The results show that our approach improve the repair performance of developers, with 62.5% more successfully repaired bugs and 25.3% less debugging time in average. In particular, even if the generated patches are all incorrect, the performance of the developers would not be significantly reduced, and could be improved when some patches provide useful information for repairing, such as the faulty location and a partial fix.

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