TY - GEN
T1 - Reduce before You Localize
T2 - 29th IEEE International Symposium on Software Reliability Engineering Workshops, ISSREW 2018
AU - Christi, Arpit
AU - Olson, Matthew Lyle
AU - Alipour, Mohammad Amin
AU - Groce, Alex
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/11/16
Y1 - 2018/11/16
N2 - Spectrum-based fault localization (SBFL) is one of the most popular and studied methods for automated debugging. Many formulas have been proposed to improve the accuracy of SBFL scores. Many of these improvements are either marginal or context-dependent. This paper proposes that, independent of the scoring method used, the effectiveness of spectrum-based localization can usually be dramatically improved by, when possible, delta-debugging failing test cases and basing localization only on the reduced test cases. We show that for programs and faults taken from the standard localization literature, a large case study of Mozilla's JavaScript engine using 10 real faults, and mutants of various open-source projects, localizing only after reduction often produces much better rankings for faults than localization without reduction, independent of the localization formula used, and the improvement is often even greater than that provided by changing from the worst to the best localization formula for a subject.
AB - Spectrum-based fault localization (SBFL) is one of the most popular and studied methods for automated debugging. Many formulas have been proposed to improve the accuracy of SBFL scores. Many of these improvements are either marginal or context-dependent. This paper proposes that, independent of the scoring method used, the effectiveness of spectrum-based localization can usually be dramatically improved by, when possible, delta-debugging failing test cases and basing localization only on the reduced test cases. We show that for programs and faults taken from the standard localization literature, a large case study of Mozilla's JavaScript engine using 10 real faults, and mutants of various open-source projects, localizing only after reduction often produces much better rankings for faults than localization without reduction, independent of the localization formula used, and the improvement is often even greater than that provided by changing from the worst to the best localization formula for a subject.
KW - automated debugging
KW - fault localization
KW - test case reduction
UR - http://www.scopus.com/inward/record.url?scp=85059868475&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85059868475&partnerID=8YFLogxK
U2 - 10.1109/ISSREW.2018.00005
DO - 10.1109/ISSREW.2018.00005
M3 - Conference contribution
AN - SCOPUS:85059868475
T3 - Proceedings - 29th IEEE International Symposium on Software Reliability Engineering Workshops, ISSREW 2018
SP - 184
EP - 191
BT - Proceedings - 29th IEEE International Symposium on Software Reliability Engineering Workshops, ISSREW 2018
A2 - Natella, Roberto
A2 - Ghosh, Sudipto
A2 - Laranjeiro, Nuno
A2 - Poston, Robin
A2 - Cukic, Bojan
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 15 October 2018 through 18 October 2018
ER -