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Thu 12 Nov 2020 01:35 - 01:36 at Virtual room 2 - Testing 2

Automated program repair holds the potential to significantly reduce software maintenance effort and cost. However, recent studies have shown that it often produces low-quality patches that repair some but break other functionality. We hypothesize that producing patches by replacing likely faulty regions of code with semantically-similar code fragments, and doing so at a higher level of granularity than prior approaches can better capture abstraction and the intended specification, and can improve repair quality. We create SOSRepair, an automated program repair technique that uses semantic code search to replace candidate buggy code regions with behaviorally-similar (but not identical) code written by humans. SOSRepair is the first such technique to scale to real-world defects in real-world systems. On a subset of the ManyBugs benchmark of such defects, SOSRepair produces patches for 22 (34%) of the 65 defects, including 3, 5, and 6 defects for which previous state-of-the-art techniques Angelix, Prophet, and GenProg do not, respectively. On these 22 defects, SOSRepair produces more patches (9, 41%) that pass all independent tests than the prior techniques. We demonstrate a relationship between patch granularity and the ability to produce patches that pass all independent tests. We then show that fault localization precision is a key factor in SOSRepair’s success. Manually improving fault localization allows SOSRepair to patch 23 (35%) defects, of which 16 (70%) pass all independent tests. We conclude that (1) higher-granularity, semantic-based patches can improve patch quality, (2) semantic search is promising for producing high-quality real-world defect repairs, (3) research in fault localization can significantly improve the quality of program repair techniques, and (4) semi-automated approaches in which developers suggest fix locations may produce high-quality patches.

Thu 12 Nov
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01:30 - 02:00
Clustering Test Steps in Natural Language toward Automating Test Automation
Industry Papers
Linyi LiUniversity of Illinois at Urbana-Champaign, Zhenwen LiPeking University, China, Weijie ZhangTencent, China, Jun ZhouTencent, China, Pengcheng WangTencent, China, Jing WuTencent, China, Guanghua HeTencent, China, Xia ZengTencent, China, Yuetang DengTencent, Inc., Tao XiePeking University
PRF: A Framework for Building Automatic Program Repair Prototypes for JVM-Based Languages
Tool Demos
Ali GhanbariThe University of Texas at Dallas, Andrian MarcusUniversity of Texas at Dallas
DOI Pre-print
SOSRepair: Expressive Semantic Search for Real-World Program Repair
Journal First
Afsoon AfzalCarnegie Mellon University, Manish MotwaniUniversity of Massachusetts, Amherst, Kathryn StoleeNorth Carolina State University, Yuriy BrunUniversity of Massachusetts Amherst, Claire Le GouesCarnegie Mellon University
Link to publication DOI Pre-print Media Attached
tsDetect: An Open Source Test Smells Detection Tool
Tool Demos
Anthony PerumaRochester Institute of Technology, Khalid AlmalkiRochester Institute of Technology, USA, Christian D. NewmanRochester Institute of Technology, Mohamed Wiem MkaouerRochester Institute of Technology, Ali OuniETS Montreal, University of Quebec, Fabio PalombaUniversity of Salerno
DOI Pre-print Media Attached
Understanding and Automatically Detecting Conflicting Interactions between Smart Home IoT Applications
Research Papers
Rahmadi TrimanandaUniversity of California at Irvine, USA, Seyed Amir Hossein AqajariUniversity of California at Irvine, USA, Jason ChuangUniversity of California at Irvine, USA, Brian DemskyUniversity of California at Irvine, Guoqing Harry XuUniversity of California at Los Angeles, Shan LuUniversity of Chicago, USA
DOI Pre-print Media Attached File Attached
Conversations on Testing 2
Paper Presentations
Afsoon AfzalCarnegie Mellon University, Anthony PerumaRochester Institute of Technology, Linyi LiUniversity of Illinois at Urbana-Champaign, Rahmadi TrimanandaUniversity of California at Irvine, USA, M: Corina S PasareanuCarnegie Mellon University Silicon Valley, NASA Ames Research Center