Towards efficient and effective automatic program repair

• Published in: Proceedings of the 31st IEEE/ACM International Conference on Automated Software Engineering pp. 876 - 879
• Main Author: Le, Xuan-Bach D.
• Format: Conference Proceeding
• Language: English
• Published: ACM 01.09.2016
• Subjects: Automatic Program Repair; Computer bugs; Deductive Reasoning; Genetic Programming; History; Maintenance engineering; Mining Software Repository; Search problems; Semantics; Software; Syntactics
• DOI: 10.1145/2970276.2975934

Automatic Program Repair (APR) has recently been an emerging research area, addressing an important challenge in software engineering. APR techniques, if effective and efficient, can greatly help software debugging and maintenance. Recently proposed APR techniques can be generally classified into two families, namely search-based and semantics-based APR methods. To produce repairs, search based APR techniques generate huge populations of possible repairs, i.e., search space, and lazily search for the best one among the search space. Semantics-based APR techniques utilize constraint solving and program synthesis to make search space more tractable, and find those repairs that conform to semantics constraints extracted via symbolic execution. Despite recent advances in APR, search-based APR still suffers from search space explosion problem, while the semantics-based APR could be hindered by limited capability of constraint solving and program synthesis. Furthermore, both APR families may be subject to overfitting, in which generated repairs do not generalize to other test sets. This thesis works towards enhancing both effectiveness and efficiency in order for APR to be practically adopted in foreseeable future. To achieve this goal, other than using test cases as the primary criteria for traversing the search space, we designed a new feature used for a new search-based APR technique to effectively traverse the search space, wherein bug fix history is used to evaluate the quality of repair candidates. We also developed a deductive-reasoning-based repair technique that combines search-based and semantics-based approaches to enhance the repair capability, while ensuring the soundness of generated repairs. We also leveraged machine-learning techniques to build a predictive model that predicts whether an APR technique is effective in fixing particular bugs. In the future, we plan to synergize many existing APR techniques, improve our predictive model, and adopt the advances of other fields such as test case generation and program synthesis for APR.