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 NovDisplayed time zone: (UTC) Coordinated Universal Time change
17:30 - 18:00 | RecommendationStudent Research Competition / Research Papers / Paper Presentations at Virtual room 1 | ||
17:30 2mTalk | 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 1mTalk | 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 1mTalk | 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 1mTalk | 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 Prasad Fujitsu Labs, USA DOI Pre-print Media Attached | ||
17:39 1mTalk | Recommender Systems: Metric Suggestion Mechanisms Applied to Adaptable Software Dashboards Student Research Competition Dragos Strugar Innopolis University, Russia DOI | ||
17:41 1mTalk | Understanding the Impact of GitHub Suggested Changes on Recommendations between Developers Research Papers DOI | ||
17:43 17mTalk | 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 | ||