Automated model repair techniques enable machines to synthesise patches that ensure models meet given requirements. B-repair, which is an existing model repair approach, assists users in repairing erroneous models in the B formal method, but repairing large models is inefficient due to successive applications of repair. In this work, we improve the performance of B-repair using simultaneous modifications, repair refactoring and better classifiers. The simultaneous modifications can eliminate multiple invariant violations at a time so that the average time to repair each fault can be reduced. Further, the modifications can be refactored to reduce the length of repair. The purpose of using better classifiers is to perform more accurate and general repairs and avoid inefficient brute-force searches. We conducted an empirical study to demonstrate that the improved implementation leads to the entire model process achieving higher accuracy, generality and efficiency.

自动模型修复技术使机器能够合成补丁，确保模型满足给定的要求。B-repair是现有的模型修复方法，在B形式化方法中辅助用户修复错误模型，但修复大型模型由于连续应用修复而效率低下。在这项工作中，我们使用同时​​修改、修复重构和更好的分类器来提高 B 修复的性能。同时修改可以一次消除多个不变量违规，从而可以减少修复每个故障的平均时间。此外，可以重构修改以减少修复时间。使用更好的分类器的目的是执行更准确和更一般的修复，并避免低效的蛮力搜索。