Intent-Preserving Test Repair

Abstract

Repairing broken tests in evolving software systems is an expensive and challenging task. One of the main challenges for test repair, in particular, is preserving the intent of the original tests in the repaired ones. To address this challenge, we propose a technique for test repair that models and considers the intent of a test when repairing it. Our technique first uses a search-based approach to generate repair candidates for the broken test. It then computes, for each candidate, its likelihood of preserving the original test intent. To do so, the technique characterizes such intent using the path conditions generated during a dynamic symbolic execution of the tests. Finally, the technique reports the best candidates to the developer as repair recommendations. We implemented and evaluated our technique on a benchmark of 91 broken tests in 4 open-source programs. Our results are promising, in that the technique was able to generate intentpreserving repair candidates for over 79% of those broken tests and rank the intent-preserving candidates as the first choice of repair recommendations for almost 70% of the broken tests.