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Fri 13 Nov 2020 08:37 - 08:38 at Virtual room 1 - Testing 4

Finding the root cause of a bug requires a significant effort from developers. Automated fault localization techniques seek to reduce this cost by computing the suspiciousness scores (i.e., the likelihood of program entities being faulty). Existing techniques have been developed by utilizing input features of specific types for the computation of suspiciousness scores, such as program spectrum or mutation analysis results. This article presents a novel learn-to-rank fault localization technique called PRecise machINe-learning-based fault loCalization tEchnique (PRINCE). PRINCE uses genetic programming (GP) to combine multiple sets of localization input features that have been studied separately until now. For dynamic features, PRINCE encompasses both Spectrum Based Fault Localization (SBFL) and Mutation Based Fault Localization (MBFL) techniques. It also uses static features, such as dependency information and structural complexity of program entities. All such information is used by GP to train a ranking model for fault localization. The empirical evaluation on 65 real-world faults from CoREBench, 84 artificial faults from SIR, and 310 real-world faults from Defects4J shows that PRINCE outperforms the state-of-the-art SBFL, MBFL, and learn-to-rank techniques significantly. PRINCE localizes a fault after reviewing 2.4% of the executed statements on average (4.2 and 3.0 times more precise than the best of the compared SBFL and MBFL techniques, respectively). Also, PRINCE ranks 52.9% of the target faults within the top ten suspicious statements.

Fri 13 Nov

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08:30 - 09:00
08:30
2m
Talk
A Taxonomy to Assess and Tailor Risk-based Testing in Recent Testing Standards
Journal First
Juergen Grossmann Fraunhofer, Michael Felderer University of Innsbruck, Johannes Viehmann Fraunhofer FOKUS, Germany, Ina Schieferdecker Fraunhofer FOKUS & TU Berlin, Germany
08:33
1m
Talk
Detecting Optimization Bugs in Database Engines via Non-optimizing Reference Engine Construction
Research Papers
Manuel Rigger ETH Zurich, Zhendong Su ETH Zurich
DOI Pre-print Media Attached
08:35
1m
Talk
Evolutionary Improvement of Assertion Oracles
Research Papers
Valerio Terragni USI Lugano, Switzerland, Gunel Jahangirova USI Lugano, Switzerland, Paolo Tonella USI Lugano, Switzerland, Mauro Pezze USI Lugano, Switzerland
DOI
08:37
1m
Talk
Precise Learn-to-Rank Fault Localization Using Dynamic and Static Features of Target Programs
Journal First
Yunho Kim KAIST, Seokhyeon Moon KAIST, Shin Yoo Korea Advanced Institute of Science and Technology, Moonzoo Kim KAIST / VPlusLab Inc.
08:39
1m
Talk
When Does My Program Do This? Learning Circumstances of Software Behavior
Research Papers
Alexander Kampmann CISPA, Germany, Nikolas Havrikov CISPA, Germany, Ezekiel O. Soremekun CISPA, Germany, Andreas Zeller CISPA, Germany
DOI
08:41
19m
Talk
Conversations on Testing 4
Paper Presentations
Manuel Rigger ETH Zurich, Valerio Terragni USI Lugano, Switzerland, Gunel Jahangirova USI Lugano, Switzerland, Alexander Kampmann CISPA, Germany, M: Marcel Böhme Monash University, Australia