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Wed 11 Nov 2020 17:33 - 17:34 at Virtual room 1 - Recommendation

Exception handling is an effective mechanism to avoid unexpected runtime errors. However, novice programmers might fail to handle exceptions properly, causing serious errors like system crashing or resource leaking. In this paper, we introduce FuzzyCatch, a code recommendation tool for handling exceptions. Based on fuzzy logic, FuzzyCatch can predict if a runtime exception would occur in a given code snippet and recommend code to handle that exception. FuzzyCatch is implemented as a plugin for Android Studio. The empirical evaluation suggests that FuzzyCatch is highly effective. For example, it has top-1 accuracy of 77% on recommending what exception to catch in a try catch block and of 70% on recommending what method should be called when such an exception occurs. FuzzyCatch also achieves a high level of accuracy and outperforms baselines significantly on detecting and fixing real exception bugs.

Wed 11 Nov
Times are displayed in time zone: (UTC) Coordinated Universal Time change

17:30 - 17:32
Talk
API Method Recommendation via Explicit Matching of Functionality Verb Phrases
Research Papers
Wenkai XieFudan University, China, Xin PengFudan University, China, Mingwei LiuFudan University, China, Christoph TreudeUniversity of Adelaide, Australia, Zhenchang XingAustralian National University, Australia, Xiaoxin ZhangFudan University, China, Wenyun ZhaoFudan University, China
DOI
17:33 - 17:34
Talk
Code Recommendation for Exception Handling
Research Papers
Tam NguyenAuburn University, USA, Phong VuAuburn University, USA, Tung NguyenAuburn University, USA
DOI
17:35 - 17:36
Talk
eQual: Informing Early Design Decisions
Research Papers
Arman ShahbazianGoogle, USA, Suhrid KarthikUniversity of Southern California, USA, Yuriy BrunUniversity of Massachusetts Amherst, Nenad MedvidovićUniversity of Southern California, USA
Link to publication DOI Pre-print Media Attached
17:37 - 17:38
Talk
Recommending Stack Overflow Posts for Fixing Runtime Exceptions using Failure Scenario Matching
Research Papers
Sonal MahajanFujitsu Labs, USA, Negarsadat AbolhassaniUniversity of Southern California, USA, Mukul R. PrasadFujitsu Labs, USA
DOI Pre-print Media Attached
17:39 - 17:40
Talk
Recommender Systems: Metric Suggestion Mechanisms Applied to Adaptable Software Dashboards
Student Research Competition
Dragos StrugarInnopolis University, Russia
DOI
17:41 - 17:42
Talk
Understanding the Impact of GitHub Suggested Changes on Recommendations between Developers
Research Papers
Chris BrownNorth Carolina State University, USA, Chris ParninNorth Carolina State University, USA
DOI
17:43 - 18:00
Talk
Conversations on Recommendation
Paper Presentations
Chris BrownNorth Carolina State University, USA, Dragos StrugarInnopolis University, Russia, Mingwei LiuFudan University, China, Sonal MahajanFujitsu Labs, USA, Arman ShahbazianUniversity of Southern California, M: Massimiliano Di PentaUniversity of Sannio, Italy