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ESEC/FSE 2022
Mon 14 - Fri 18 November 2022 Singapore
Wed 16 Nov 2022 14:00 - 14:15 at SRC LT 53 - Program Repair/Synthesis Chair(s): Saikat Chakraborty

We present PyTER, an automated program repair (APR) technique for Python type errors. Python developers struggle with type error exceptions that are prevalent and difficult to fix. Despite the importance, however, automatically repairing type errors in dynamically typed languages such as Python has received little attention in the APR community and no existing techniques are readily available for practical use. PyTER is the first technique that is carefully designed to fix diverse type errors in real-world Python applications. To this end, we present a novel APR approach that uses dynamic and static analyses to infer correct and incorrect types of program variables, and leverage their difference to effectively identify faulty locations and patch candidates. We evaluated PyTER on 93 type errors collected from open-source projects. The result shows that PyTER is able to fix 48.4% of them with a precision of 77.6%.

Wed 16 Nov

Displayed time zone: Beijing, Chongqing, Hong Kong, Urumqi change

14:00 - 15:30
Program Repair/SynthesisResearch Papers / Industry Paper at SRC LT 53
Chair(s): Saikat Chakraborty Microsoft Research
14:00
15m
Talk
PyTER: Effective Program Repair for Python Type Errors
Research Papers
Wonseok Oh Korea University, Hakjoo Oh Korea University
DOI
14:15
15m
Talk
VulRepair: A T5-Based Automated Software Vulnerability Repair
Research Papers
Micheal Fu Monash University, Kla Tantithamthavorn Monash University, Trung Le Monash University, Australia, Van Nguyen Monash University, Australia, Dinh Phung Monash University, Australia
DOI
14:30
15m
Talk
An Empirical Study of Deep Transfer Learning-Based Program Repair for Kotlin Projects
Industry Paper
Misoo Kim Sungkyunkwan University, Youngkyoung Kim Sungkyunkwan University, Hohyeon Jeong Sungkyunkwan University, Jinseok Heo Sungkyunkwan University, Sungoh Kim Samsung Electronics, Hyunhee Chung Samsung Electronics, Eunseok Lee Sungkyunkwan University
DOI
14:45
15m
Talk
DeepDev-PERF: A Deep Learning-Based Approach for Improving Software Performance
Research Papers
Spandan Garg Microsoft, Roshanak Zilouchian Moghaddam Microsoft, Colin Clement Microsoft, Neel Sundaresan Microsoft, Chen Wu Microsoft
DOI
15:00
15m
Talk
Less Training, More Repairing Please: Revisiting Automated Program Repair via Zero-Shot Learning
Research Papers
Chunqiu Steven Xia University of Illinois at Urbana-Champaign, Lingming Zhang University of Illinois at Urbana-Champaign
DOI