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ICSE 2021
Mon 17 May - Sat 5 June 2021
Thu 27 May 2021 20:50 - 21:10 at Blended Sessions Room 3 - 3.6.3. Fault Localization #2 Chair(s): Davide Falessi
Fri 28 May 2021 08:50 - 09:10 at Blended Sessions Room 3 - 3.6.3. Fault Localization #2

In this paper, we propose DEEPRL4FL, a deep learning fault localization (FL) approach that locates the buggy code at the statement and method levels by treating FL as an image pattern recognition problem. DEEPRL4FL does so via novel code coverage representation learning (RL) and data dependencies RL for program statements. Those two types of RL on the dynamic information in a code coverage matrix are also combined with the code representation learning on the static information of the usual suspicious source code. This combination is inspired by crime scene investigation in which investigators analyze the crime scene (failed test cases and statements) and related persons (statements with dependencies), and at the same time, examine the usual suspects who have committed a similar crime in the past (similar buggy code in the training data). For the code coverage information, DEEPRL4FL first orders the test cases and marks error-exhibiting code statements, expecting that a model can recognize patterns discriminating between faulty and non-faulty statements/methods easily. For dependencies among statements, the suspiciousness of a statement is seen taking into account the data dependencies to other statements in execution and data flows, in addition to the statement by itself. Finally, the vector representations for code coverage matrix, data dependencies among statements, and source code are combined and used as the input of a classifier built from a Convolution Neural Network to detect buggy statements/methods. Our empirical evaluation shows that DEEPRL4FL outperforms the baseline models and localizes 245 bugs from Defects4J. It improves the top-1 results of baselines from 15.0%–206.3%.

Conference Day
Thu 27 May

Displayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change

20:50 - 21:50
3.6.3. Fault Localization #2SEIP - Software Engineering in Practice / Technical Track / Journal-First Papers at Blended Sessions Room 3 +12h
Chair(s): Davide FalessiCalifornia Polytechnic State University
20:50
20m
Paper
Fault Localization with Code Coverage Representation LearningTechnical Track
Technical Track
Yi LiNew Jersey Institute of Technology, Shaohua WangNew Jersey Institute of Technology, Tien N. NguyenUniversity of Texas at Dallas
Pre-print Media Attached
21:10
20m
Paper
PerfJIT: Test-level Just-in-time Prediction for Performance Regression Introducing CommitsJournal-First
Journal-First Papers
Jinfu ChenCentre for Software Excellence, Huawei, Canada, Weiyi ShangConcordia University, Emad ShihabConcordia University
Link to publication Pre-print Media Attached
21:30
20m
Paper
Scalable Statistical Root Cause Analysis on App TelemetrySEIP
SEIP - Software Engineering in Practice
Vijayaraghavan MuraliFacebook, Inc., Edward YaoFacebook, Umang MathurUniversity of Illinois at Urbana-Champaign, Satish ChandraFacebook, USA
Pre-print

Conference Day
Fri 28 May

Displayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change

08:50 - 09:50
08:50
20m
Paper
Fault Localization with Code Coverage Representation LearningTechnical Track
Technical Track
Yi LiNew Jersey Institute of Technology, Shaohua WangNew Jersey Institute of Technology, Tien N. NguyenUniversity of Texas at Dallas
Pre-print Media Attached
09:10
20m
Paper
PerfJIT: Test-level Just-in-time Prediction for Performance Regression Introducing CommitsJournal-First
Journal-First Papers
Jinfu ChenCentre for Software Excellence, Huawei, Canada, Weiyi ShangConcordia University, Emad ShihabConcordia University
Link to publication Pre-print Media Attached
09:30
20m
Paper
Scalable Statistical Root Cause Analysis on App TelemetrySEIP
SEIP - Software Engineering in Practice
Vijayaraghavan MuraliFacebook, Inc., Edward YaoFacebook, Umang MathurUniversity of Illinois at Urbana-Champaign, Satish ChandraFacebook, USA
Pre-print