TY - GEN
T1 - Hey APR! Integrate Our Fault Localization Skill
T2 - 46th IEEE Annual Computers, Software, and Applications Conference, COMPSAC 2022
AU - Yamate, Kyosuke
AU - Kondo, Masanari
AU - Kashiwa, Yutaro
AU - Kamei, Yasutaka
AU - Ubayashi, Naoyasu
N1 - Funding Information:
In this paper, the developer’s skill was quantified as the number of inspected statements before localizing the faulty statement from a method, but other skills may also be involved in the execution cost of APR with MFL. Hence, we would like to clarify the feasibility of MFL in an actual software development when considering the skills of actual developers. The replication package can be found here: https://www.dropbox. com/s/fotxbb7qh0fdxy2/compsac 2022 APR.zip?dl=0 ACKNOWLEDGMENTS This research was partially supported by JSPS KAKENHI Japan (Grant Numbers: JP18H04097, 21H04877, 21K17725) and JSPS International Joint Research Program with SNSF (Project “SENSOR”: JPJSJRP20191502).
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Background: Prior studies lack the perspective of using developer's skills to augment the performance of automated program repair (APR). APR has a phase referred to as fault localization (FL), which automatically finds the faulty statement that causes faults. To achieve a well-performed FL phase, we study developers' FL skills, which allow developers to find faulty statements. We suppose that such FL skills can add additional information to fault localization to augment the accuracy of fault localization and reduce the execution cost of APR. Aims: We aim at revealing a criterion that distinguishes whether using the FL skill reduces the execution cost of the state-of-the-art APR, TBar, depending on the accuracy of the FL skill. Method: We conduct a simulation case study in the Defects4J dataset, which is the most popular dataset. We compare the numbers of candidate patches generated by TBar using the FL skill or using spectrum-based fault localization (SBFL). Results: Our case study revealed that, if developers localized the faulty statements before inspecting 40 % of the statements in the target program, the execution cost of TBar reduces for over half of the studied faults. The 40 % value is a requirement for developers using the FL skill to augment the performance of APR. Conclusion: If developers can localize the faulty statement before inspecting 40 % of the statements, integrating the FL skill with SBFL makes TBar faster compared to when SBFL is used.
AB - Background: Prior studies lack the perspective of using developer's skills to augment the performance of automated program repair (APR). APR has a phase referred to as fault localization (FL), which automatically finds the faulty statement that causes faults. To achieve a well-performed FL phase, we study developers' FL skills, which allow developers to find faulty statements. We suppose that such FL skills can add additional information to fault localization to augment the accuracy of fault localization and reduce the execution cost of APR. Aims: We aim at revealing a criterion that distinguishes whether using the FL skill reduces the execution cost of the state-of-the-art APR, TBar, depending on the accuracy of the FL skill. Method: We conduct a simulation case study in the Defects4J dataset, which is the most popular dataset. We compare the numbers of candidate patches generated by TBar using the FL skill or using spectrum-based fault localization (SBFL). Results: Our case study revealed that, if developers localized the faulty statements before inspecting 40 % of the statements in the target program, the execution cost of TBar reduces for over half of the studied faults. The 40 % value is a requirement for developers using the FL skill to augment the performance of APR. Conclusion: If developers can localize the faulty statement before inspecting 40 % of the statements, integrating the FL skill with SBFL makes TBar faster compared to when SBFL is used.
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U2 - 10.1109/COMPSAC54236.2022.00101
DO - 10.1109/COMPSAC54236.2022.00101
M3 - Conference contribution
AN - SCOPUS:85136969474
T3 - Proceedings - 2022 IEEE 46th Annual Computers, Software, and Applications Conference, COMPSAC 2022
SP - 563
EP - 568
BT - Proceedings - 2022 IEEE 46th Annual Computers, Software, and Applications Conference, COMPSAC 2022
A2 - Va Leong, Hong
A2 - Sarvestani, Sahra Sedigh
A2 - Teranishi, Yuuichi
A2 - Cuzzocrea, Alfredo
A2 - Kashiwazaki, Hiroki
A2 - Towey, Dave
A2 - Yang, Ji-Jiang
A2 - Shahriar, Hossain
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 27 June 2022 through 1 July 2022
ER -