一句话：利用形式不变式集合的差异与蕴含距离刻画自动生成补丁的语义相似性，并聚类以减轻人工审查负担。
自动生成补丁的部署受制于开发者信任，而理解补丁效果是建立信任的关键。本文提出用形式不变式集合的差异来刻画补丁差异，并用不变式空间中的蕴含距离度量补丁相似性：若一个补丁与另一个相似，它常包含相同修改及额外行为，这一模式可由逻辑蕴含捕捉。作者用定理证明器验证不同程序所蕴含不变式之间的蕴含关系，并发现字符串距离是高效启发式。基于补丁距离构建层次结构以突出相似补丁；在300多个补丁上评估，能正确将程序按语义相似性聚类，将需人工考虑语义不同补丁的数量减少50%以上，从而加速建立对自动修复的信任。
Developer trust is a major barrier to the deployment of automatically-generated patches. Understanding the effect of a patch is a key element of that trust. We find that differences in sets of formal invariants characterize patch differences and that implication-based distances in invariant space characterize patch similarities. When one patch is similar to another it often contains the same changes as well as additional behavior; this pattern is well-captured by logical implication. We can measure differences using a theorem prover to verify implications between invariants implied by separate programs. Although effective, theorem provers are computationally intensive; we find that string distance is an efficient heuristic for implication-based distance measurements. We propose to use distances between patches to construct a hierarchy highlighting patch similarities. We evaluated this approach on over 300 patches and found that it correctly categorizes programs into semantically similar clusters. Clustering programs reduces human effort by reducing the number of semantically distinct patches that must be considered by over 50%, thus reducing the time required to establish trust in automatically generated repairs.
DOI 原文 ·
@inproceedings{paperbot5278,
title = {Understanding Automatically-Generated Patches Through Symbolic Invariant Differences},
author = {Padraic Cashin and Carianne Martinez and Westley Weimer and Stephanie Forrest},
booktitle = {ASE},
year = {2019},
doi = {10.1109/ase.2019.00046}
}