一句话：提出用变体执行编码搜索空间中的备选方案，为自动程序修复和高阶变异测试的搜索启发式提供洞见。
生成-验证式程序修复和高阶变异测试常依赖搜索技术，在巨大空间中寻找最优或足够好的解；当解需要多个变更交互时，搜索会变得困难。论文提出将搜索空间的备选方案编码为变体，利用变体执行作为黑盒技术穷举探索变体并识别交互，从而为现有搜索启发式提供信息。作者在两个场景上初步验证了该思路，并给出适合采用变体执行的问题判据，以帮助发现更多应用。
Generate-and-validate automatic program repair and higher order mutation testing often use search-based techniques to find optimal or good enough solutions in huge search spaces. As search spaces continue to grow, finding solutions that require interactions of multiple changes can become challenging. To tackle the huge search space, we propose to use variational execution. Variational execution has been shown to be effective in exhaustively exploring variations and identifying interactions in a huge but often finite configuration space. The key idea is to encode alternatives in the search space as variations and use variational execution as a black-box technique to generate useful insights so that existing search heuristics can be informed. We show that this idea is promising and identify criteria for problems in which variational execution is a promising tool, which may be useful to identify further applications.
@inproceedings{paperbot4606,
title = {Beyond testing configurable systems: applying variational execution to automatic program repair and higher order mutation testing},
author = {Chu-Pan Wong and Jens Meinicke and Christian Kästner},
booktitle = {FSE},
year = {2018},
doi = {10.1145/3236024.3264837}
}