Boosting Automated Program Repair for Adoption By Practitioners

Automated program repair (APR) attracts a huge interest from research and industry as the ultimate target in automation of software maintenance. Towards realizing this automation promise, the research community has explored various ideas and techniques, which are increasingly demonstrating that APR is no longer fictional. Although literature techniques constantly set new records in fixing a significant fraction of defects within well-established benchmarks, we are not aware of large-scale adoption of APR in practice. Meanwhile, open-source and commercial organizations have started to reflect on the potential of integrating some automated steps in the software development cycle. Actually, the current practice has several development settings that use a number of tools to automate and systematize various tasks such as code style checking, bug detection, and systematic patching.

Our work is motivated by this fact. We advocate that systematic and empirical exploration of the current practice that leverage tools to automate debugging tasks would provide valuable insights for rethinking and boosting the APR agenda towards its acceptability by developer communities. We have identified three investigation axes in this dissertation. First, mining software repositories towards understanding code change properties that could be valuable to guide program repair. Second, analyzing communication channels in software development in order to assess to what extent they could be relevant in a real-world program repair scenario. Third, exploring generic concepts of patching in the literature for establishing a common foundation for program repair pipelines that can be integrated with industrial settings.

This dissertation makes the following contributions to the community:

• An empirical study of tool support in a real development setting providing concrete insights on the acceptance, stability and the nature of bugs being fixed by manually-craft patches vs tool-supported patches and manifests opportunities for improving automated repair techniques.

• A novel information retrieval based bug localization approach that learns how to compute the similarity scores of various types of features.

• An automated mining strategy to infer fix pattern that can be integrated to automated program repair pipelines.

• A practical bug report driven program repair pipeline.