SGS: Mutant Reduction for Higher-order Mutation-based Fault Localization

Luxi Fan; Zheng Li; Hengyuan Liu; Doyle Paul; Haifeng Wang; Xiang Chen; Yong Liu

doi:10.1109/COMPSAC57700.2023.00116

SGS: Mutant Reduction for Higher-order Mutation-based Fault Localization

Luxi Fan, Zheng Li, Hengyuan Liu, Doyle Paul, Haifeng Wang, Xiang Chen, Yong Liu

School of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

MBFL (Mutation-Based Fault Localization) is one of the most commonly studied fault localization techniques due to its promising fault localization effectiveness. However, MBFL incurs a high execution cost as it needs to execute the test suite on a large number of mutants. While previous studies have proposed mutant reduction methods for FOMs (First-Order Mutants) to help alleviate the cost of MBFL, the reduction of HOMs (Higher-Order Mutants) has not been thoroughly investigated. In this study, we propose SGS (Statement Granularity Sampling), a method which conducts HOMs reduction for HMBFL (Higher-Order Mutation-Based Fault Localization). Considering the relationship between HOMs and statements, we sample HOMs at the statement level to ensure each statement has corresponding HOMs. We empirically evaluate the fault localization effectiveness of HMBFL using SGS on 237 multiple-fault programs taken from the SIR and Codeflaws benchmarks. The experimental results show that (1) The best sampling ratio for HMBFL with SGS is 20%, which preserves the performance and reduces execution costs by 80% ; (2) The fault localization accuracy of HMBFL with SGS outperforms the state-of-the-art SBFL (Spectrum-Based Fault Localization) and MBFL techniques by 20%.

Original language	English
Title of host publication	Proceedings - 2023 IEEE 47th Annual Computers, Software, and Applications Conference, COMPSAC 2023
Editors	Hossain Shahriar, Yuuichi Teranishi, Alfredo Cuzzocrea, Moushumi Sharmin, Dave Towey, AKM Jahangir Alam Majumder, Hiroki Kashiwazaki, Ji-Jiang Yang, Michiharu Takemoto, Nazmus Sakib, Ryohei Banno, Sheikh Iqbal Ahamed
Publisher	IEEE Computer Society
Pages	870-875
Number of pages	6
ISBN (Electronic)	9798350326970
DOIs	https://doi.org/10.1109/COMPSAC57700.2023.00116
Publication status	Published - 2023
Event	47th IEEE Annual Computers, Software, and Applications Conference, COMPSAC 2023 - Hybrid, Torino, Italy Duration: 26 Jun 2023 → 30 Jun 2023

Publication series

Name	Proceedings - International Computer Software and Applications Conference
Volume	2023-June
ISSN (Print)	0730-3157

Conference

Conference	47th IEEE Annual Computers, Software, and Applications Conference, COMPSAC 2023
Country/Territory	Italy
City	Hybrid, Torino
Period	26/06/23 → 30/06/23

Keywords

Higher-order-mutants
Multiple faults
Mutant reduction
Mutation-based fault localization

Access to Document

10.1109/COMPSAC57700.2023.00116

https://arrow.tudublin.ie/ascnetcon/9Licence: CC BY-NC-SA

Cite this

Fan, L., Li, Z., Liu, H., Paul, D., Wang, H., Chen, X., & Liu, Y. (2023). SGS: Mutant Reduction for Higher-order Mutation-based Fault Localization. In H. Shahriar, Y. Teranishi, A. Cuzzocrea, M. Sharmin, D. Towey, AKM. J. A. Majumder, H. Kashiwazaki, J.-J. Yang, M. Takemoto, N. Sakib, R. Banno, & S. I. Ahamed (Eds.), Proceedings - 2023 IEEE 47th Annual Computers, Software, and Applications Conference, COMPSAC 2023 (pp. 870-875). (Proceedings - International Computer Software and Applications Conference; Vol. 2023-June). IEEE Computer Society. https://doi.org/10.1109/COMPSAC57700.2023.00116

Fan, Luxi ; Li, Zheng ; Liu, Hengyuan et al. / SGS : Mutant Reduction for Higher-order Mutation-based Fault Localization. Proceedings - 2023 IEEE 47th Annual Computers, Software, and Applications Conference, COMPSAC 2023. editor / Hossain Shahriar ; Yuuichi Teranishi ; Alfredo Cuzzocrea ; Moushumi Sharmin ; Dave Towey ; AKM Jahangir Alam Majumder ; Hiroki Kashiwazaki ; Ji-Jiang Yang ; Michiharu Takemoto ; Nazmus Sakib ; Ryohei Banno ; Sheikh Iqbal Ahamed. IEEE Computer Society, 2023. pp. 870-875 (Proceedings - International Computer Software and Applications Conference).

@inproceedings{b0f8bd4081194f138636d56d9a677598,

title = "SGS: Mutant Reduction for Higher-order Mutation-based Fault Localization",

abstract = "MBFL (Mutation-Based Fault Localization) is one of the most commonly studied fault localization techniques due to its promising fault localization effectiveness. However, MBFL incurs a high execution cost as it needs to execute the test suite on a large number of mutants. While previous studies have proposed mutant reduction methods for FOMs (First-Order Mutants) to help alleviate the cost of MBFL, the reduction of HOMs (Higher-Order Mutants) has not been thoroughly investigated. In this study, we propose SGS (Statement Granularity Sampling), a method which conducts HOMs reduction for HMBFL (Higher-Order Mutation-Based Fault Localization). Considering the relationship between HOMs and statements, we sample HOMs at the statement level to ensure each statement has corresponding HOMs. We empirically evaluate the fault localization effectiveness of HMBFL using SGS on 237 multiple-fault programs taken from the SIR and Codeflaws benchmarks. The experimental results show that (1) The best sampling ratio for HMBFL with SGS is 20%, which preserves the performance and reduces execution costs by 80% ; (2) The fault localization accuracy of HMBFL with SGS outperforms the state-of-the-art SBFL (Spectrum-Based Fault Localization) and MBFL techniques by 20%.",

keywords = "Higher-order-mutants, Multiple faults, Mutant reduction, Mutation-based fault localization",

author = "Luxi Fan and Zheng Li and Hengyuan Liu and Doyle Paul and Haifeng Wang and Xiang Chen and Yong Liu",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 47th IEEE Annual Computers, Software, and Applications Conference, COMPSAC 2023 ; Conference date: 26-06-2023 Through 30-06-2023",

year = "2023",

doi = "10.1109/COMPSAC57700.2023.00116",

language = "English",

series = "Proceedings - International Computer Software and Applications Conference",

publisher = "IEEE Computer Society",

pages = "870--875",

editor = "Hossain Shahriar and Yuuichi Teranishi and Alfredo Cuzzocrea and Moushumi Sharmin and Dave Towey and Majumder, {AKM Jahangir Alam} and Hiroki Kashiwazaki and Ji-Jiang Yang and Michiharu Takemoto and Nazmus Sakib and Ryohei Banno and Ahamed, {Sheikh Iqbal}",

booktitle = "Proceedings - 2023 IEEE 47th Annual Computers, Software, and Applications Conference, COMPSAC 2023",

address = "United States",

}

Fan, L, Li, Z, Liu, H, Paul, D, Wang, H, Chen, X & Liu, Y 2023, SGS: Mutant Reduction for Higher-order Mutation-based Fault Localization. in H Shahriar, Y Teranishi, A Cuzzocrea, M Sharmin, D Towey, AKMJA Majumder, H Kashiwazaki, J-J Yang, M Takemoto, N Sakib, R Banno & SI Ahamed (eds), Proceedings - 2023 IEEE 47th Annual Computers, Software, and Applications Conference, COMPSAC 2023. Proceedings - International Computer Software and Applications Conference, vol. 2023-June, IEEE Computer Society, pp. 870-875, 47th IEEE Annual Computers, Software, and Applications Conference, COMPSAC 2023, Hybrid, Torino, Italy, 26/06/23. https://doi.org/10.1109/COMPSAC57700.2023.00116

SGS: Mutant Reduction for Higher-order Mutation-based Fault Localization. / Fan, Luxi; Li, Zheng; Liu, Hengyuan et al.
Proceedings - 2023 IEEE 47th Annual Computers, Software, and Applications Conference, COMPSAC 2023. ed. / Hossain Shahriar; Yuuichi Teranishi; Alfredo Cuzzocrea; Moushumi Sharmin; Dave Towey; AKM Jahangir Alam Majumder; Hiroki Kashiwazaki; Ji-Jiang Yang; Michiharu Takemoto; Nazmus Sakib; Ryohei Banno; Sheikh Iqbal Ahamed. IEEE Computer Society, 2023. p. 870-875 (Proceedings - International Computer Software and Applications Conference; Vol. 2023-June).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - MBFL (Mutation-Based Fault Localization) is one of the most commonly studied fault localization techniques due to its promising fault localization effectiveness. However, MBFL incurs a high execution cost as it needs to execute the test suite on a large number of mutants. While previous studies have proposed mutant reduction methods for FOMs (First-Order Mutants) to help alleviate the cost of MBFL, the reduction of HOMs (Higher-Order Mutants) has not been thoroughly investigated. In this study, we propose SGS (Statement Granularity Sampling), a method which conducts HOMs reduction for HMBFL (Higher-Order Mutation-Based Fault Localization). Considering the relationship between HOMs and statements, we sample HOMs at the statement level to ensure each statement has corresponding HOMs. We empirically evaluate the fault localization effectiveness of HMBFL using SGS on 237 multiple-fault programs taken from the SIR and Codeflaws benchmarks. The experimental results show that (1) The best sampling ratio for HMBFL with SGS is 20%, which preserves the performance and reduces execution costs by 80% ; (2) The fault localization accuracy of HMBFL with SGS outperforms the state-of-the-art SBFL (Spectrum-Based Fault Localization) and MBFL techniques by 20%.

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Fan L, Li Z, Liu H, Paul D, Wang H, Chen X et al. SGS: Mutant Reduction for Higher-order Mutation-based Fault Localization. In Shahriar H, Teranishi Y, Cuzzocrea A, Sharmin M, Towey D, Majumder AKMJA, Kashiwazaki H, Yang JJ, Takemoto M, Sakib N, Banno R, Ahamed SI, editors, Proceedings - 2023 IEEE 47th Annual Computers, Software, and Applications Conference, COMPSAC 2023. IEEE Computer Society. 2023. p. 870-875. (Proceedings - International Computer Software and Applications Conference). doi: 10.1109/COMPSAC57700.2023.00116

SGS: Mutant Reduction for Higher-order Mutation-based Fault Localization

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Keywords

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