Seminar: Search-based Automated Program Repair (for apps)

Abstract

Automated program repair techniques were proposed to reduce the time and effort required to fix bugs. Search-based automated program repair techniques typically rely on a given test suite as correctness criteria. Despite recent advances in automated program repair, there remain challenges in the adaptation of repair techniques into real-world software development, including (1) generating poor quality patches that overfit to the given test suite, and (2) the lack of applications and deployment of repair techniques.
In this talk, I will present pragmatic solutions to address these challenges. Overfitting of patches can be alleviated by designing rules for prohibiting certain transformations or implicitly exploiting past program versions. Lack of applications can be alleviated by designing new repair systems for Android apps, or using systematic approaches towards collaborative programming. Such a collaborative approach can contribute to bug fixing as well as testing.

Bio

Shin Hwei Tan is an Assistant Professor at Southern University of Science and Technology in Shenzhen, China. She obtained her PhD degree from National University of Singapore and her B.S (Hons) and MSc degree from University of Illinois at Urbana-Champaign, USA. Her main research interests include automated program repair, software testing and search-based software engineering. She received the David J. Kuck Outstanding MSc Thesis Award and Google Anita Borg Memorial Scholarship. She has served as PCs for several top-ranked conferences (i.e., ICSE, ASE, FSE, ISSTA) and journals (i.e., TOSEM, TSE, EMSE). She also co-organized the 6th International Workshop on Genetic Improvement, and proposed the 1st International Workshop on Automated Program Repair. She also won several best reviewer awards, including the Distinguished Reviewer Award in FSE 2020, the Distinguished PC Member award in ASE 2020 and ICSE NIER 2022 Best Reviewer Award.