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Failures that are not related to a specific fault can reduce the effectiveness of fault localization in multi-fault scenarios. To tackle this challenge, researchers and practitioners typically cluster failures (e.g., failed test cases) into several disjoint groups, with those caused by the same fault grouped together. In such a fault isolation process that requires input in a mathematical form, ranking-based failure proximity (R-proximity) is widely used to model failed test cases. In R-proximity, each of failed test cases is represented as a suspiciousness ranking list of program statements through a finger- printing function (i.e., a risk evaluation formula, REF). Although many off-the-shelf REFs have been integrated into R-proximity, they were designed for single-fault localization originally. To the best of our knowledge, no REF has been developed to serve as a fingerprinting function of R-proximity in multi-fault scenarios. For better clustering failures in fault isolation, in this paper, we present a genetic programming-based framework along with a sophisticated fitness function, for evolving REFs with the goal of more properly representing failures in multi-fault scenarios. By using a small set of programs for training, we get a collection of REFs that can obtain good results applicable in a larger and more general scale of scenarios. The best one of them outperforms the state-of-the-art by 50.72% and 47.41% in faults number estimation and clustering effectiveness, respectively. Our framework is highly configurable for further use, and the evolved formulas can be directly applied in future failure representation tasks without any retraining.

Wed 12 Oct

Displayed time zone: Eastern Time (US & Canada) change

13:30 - 15:30
Technical Session 14 - Bug Prediction and LocalizationJournal-first Papers / Research Papers / NIER Track / Industry Showcase at Banquet A
Chair(s): David Lo Singapore Management University
Research paper
How Useful is Code Change Information for Fault Localization in Continuous Integration?
Research Papers
An Ran Chen Concordia University, Tse-Hsun (Peter) Chen Concordia University, Junjie Chen Tianjin University
Industry talk
Code Understanding Linter to Detect Variable Misuse
Industry Showcase
Yeonhee Ryou Samsung Research, Samsung Electronics, Sangwoo Joh Samsung Research, Samsung Electronics, Joonmo Yang Samsung Research, Samsung Electronics, Sujin Kim Samsung Research, Samsung Electronics, Youil Kim Samsung Research, Samsung Electronics
Static Data-Flow Analysis for Software Product Lines in C
Journal-first Papers
Philipp Dominik Schubert Heinz Nixdorf Institut, Paderborn University, Paul Gazzillo University of Central Florida, Zachary Patterson University of Texas at Dallas, Julian Braha University of Central Florida, Fabian Schiebel Fraunhofer IEM, Ben Hermann Technical University Dortmund, Shiyi Wei University of Texas at Dallas, Eric Bodden University of Paderborn; Fraunhofer IEM
Vision and Emerging Results
Boosting Spectrum-Based Fault Localization for Spreadsheets with Product Metrics in a Learning ApproachVirtual
NIER Track
Adil mukhtar Graz University of Technology, Birgit Hofer Graz University of Technology, Dietmar Jannach University of Klagenfurt, Franz Wotawa Graz University of Technology, Konstantin Schekotihin University of Klagenfurt
Research paper
Evolving Ranking-Based Failure Proximities for Better Clustering in Fault IsolationVirtual
Research Papers
Yi Song School of Computer Science, Wuhan University, Xiaoyuan Xie School of Computer Science, Wuhan University, China, Xihao Zhang School of Computer Science, Wuhan University, Quanming Liu School of Computer Science, Wuhan University, Ruizhi Gao Sonos Inc.
Leveraging structural properties of source code graphs for just-in-time bug predictionVirtual
Journal-first Papers
Md Nadim University of Saskatchewan, Debajyoti Mondal University of Saskatchewan, Chanchal K. Roy University of Saskatchewan