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Faults in spreadsheets are not uncommon and they can have significant negative consequences in practice. Various approaches for fault localization were proposed in recent years, among them techniques that transferred ideas from spectrum-based fault localization (SFL) to the spreadsheet domain. Applying SFL to spreadsheets proved to be effective, but has certain limitations. Specifically, the constrained computational structures of spreadsheets may lead to large sets of cells that cannot be distinguished using SFL and thus have to be inspected manually. In this work, we propose to combine SFL with a fault prediction method based on spreadsheet metrics in a machine learning (ML) approach. In particular, we train supervised ML models using two orthogonal types of features: (i) variables that are used to compute similarity coefficients in SFL and (ii) spreadsheet product metrics that have shown to be good predictors for faulty formulas in previous work. Experiments with a widely-used corpus of faulty spreadsheets indicate that the combined model helps to significantly improve fault localization performance in terms of wasted effort and accuracy.

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