Write a Blog >>
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

Displayed time zone: (UTC) Coordinated Universal Time change

17:30 - 18:00
17:30
2m
Talk
API Method Recommendation via Explicit Matching of Functionality Verb Phrases
Research Papers
Wenkai Xie Fudan University, China, Xin Peng Fudan University, China, Mingwei Liu Fudan University, China, Christoph Treude University of Adelaide, Australia, Zhenchang Xing Australian National University, Australia, Xiaoxin Zhang Fudan University, China, Wenyun Zhao Fudan University, China
DOI
17:33
1m
Talk
Code Recommendation for Exception Handling
Research Papers
Tam Nguyen Auburn University, USA, Phong Vu Auburn University, USA, Tung Nguyen Auburn University, USA
DOI
17:35
1m
Talk
eQual: Informing Early Design Decisions
Research Papers
Arman Shahbazian Google, USA, Suhrid Karthik University of Southern California, USA, Yuriy Brun University of Massachusetts Amherst, Nenad Medvidović University of Southern California, USA
Link to publication DOI Pre-print Media Attached
17:37
1m
Talk
Recommending Stack Overflow Posts for Fixing Runtime Exceptions using Failure Scenario Matching
Research Papers
Sonal Mahajan Fujitsu Labs, USA, Negarsadat Abolhassani University of Southern California, USA, Mukul R. Prasad Fujitsu Labs, USA
DOI Pre-print Media Attached
17:39
1m
Talk
Recommender Systems: Metric Suggestion Mechanisms Applied to Adaptable Software Dashboards
Student Research Competition
Dragos Strugar Innopolis University, Russia
DOI
17:41
1m
Talk
Understanding the Impact of GitHub Suggested Changes on Recommendations between Developers
Research Papers
Chris Brown North Carolina State University, USA, Chris Parnin North Carolina State University, USA
DOI
17:43
17m
Talk
Conversations on Recommendation
Paper Presentations
Chris Brown North Carolina State University, USA, Dragos Strugar Innopolis University, Russia, Mingwei Liu Fudan University, China, Sonal Mahajan Fujitsu Labs, USA, Arman Shahbazian University of Southern California, M: Massimiliano Di Penta University of Sannio, Italy