Towards differential fuzzing to reduce manual efforts to identify equivalent mutants: A preliminary study
Resumo
Mutation testing is a technique that assesses the effectiveness of a set of test cases by introducing changes to the source code and checking whether the test cases can detect them. However, mutation testing is costly, and many academic efforts have been directed to improve its effectiveness and reduce costs. One of the challenges related to mutation testing remains in the equivalent mutant problem. Fuzzing, as a search technique, can find test cases that the developers might not have addressed in unit testing, and it could be used to identify equivalent mutants. In this paper, we present a preliminary study that investigates the use of differential fuzzing to identify equivalent mutants. To identify equivalent mutants, one approach is to set a timeout period after which any surviving mutants are considered equivalent. In our experiment, a 3-minute timeout yielded an accuracy rate of 97%. In conclusion, differential fuzzing can be used to identify equivalent mutants accurately at a reasonable time, especially for projects that maintain a robust seed corpus for fuzzing.
Palavras-chave:
Mutation testing, fuzzing, differential fuzzing, equivalent mutant problem
Referências
Samuel Amorim, Leo Fernandes, Márcio Ribeiro, Rohit Gheyi, Marcio Delamaro, Marcio Guimarães, and André Santos. 2024. Reducing Manual Efforts in Equivalence Analysis in Mutation Testing. Journal of Software Engineering Research and Development 12, 1 (Mar. 2024), 3:1 – 3:17. DOI: 10.5753/jserd.2024.3588
Lukas Bernhard, Tobias Scharnowski, Moritz Schloegel, Tim Blazytko, and Thorsten Holz. 2022. JIT-Picking: Differential Fuzzing of JavaScript Engines. In Proceedings of the 2022 ACMSIGSAC Conference on Computer and Communications Security (Los Angeles, CA, USA) (CCS ’22). Association for Computing Machinery, New York, NY, USA, 351–364. DOI: 10.1145/3548606.3560624
Claudinei Brito, Vinicius H. S. Durelli, Rafael S. Durelli, Simone R. S. de Souza, Auri M. R. Vincenzi, and Marcio Eduardo Delamaro. 2020. A Preliminary Investigation into Using Machine Learning Algorithms to Identify Minimal and Equivalent Mutants. In 2020 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW). 304–313. DOI: 10.1109/ICSTW50294.2020.00056
Liang Cheng, Yang Zhang, Yi Zhang, ChenWu, Zhangtan Li, Yu Fu, and Haisheng Li. 2019. Optimizing Seed Inputs in Fuzzing with Machine Learning. In 2019 IEEE/ACM 41st International Conference on Software Engineering: Companion Proceedings (ICSE-Companion). 244–245. DOI: 10.1109/ICSE-Companion.2019.00096
Seungjoon Chung and Shin Yoo. 2022. Augmenting Equivalent Mutant Dataset Using Symbolic Execution. In 2022 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW). IEEE, 150–159.
Leo Fernandes, Márcio Ribeiro, Rohit Gheyi, Marcio Delamaro, Márcio Guimarães, and André Santos. 2022. Put Your Hands In The Air! Reducing Manual Effort in Mutation Testing. In Proceedings of the XXXVI Brazilian Symposium on Software Engineering (<conf-loc>, <city>Virtual Event</city>, <country>Brazil</country>, </conf-loc>) (SBES ’22). Association for Computing Machinery, New York, NY, USA, 198–207. DOI: 10.1145/3555228.3555233
Philipp Görz, Björn Mathis, Keno Hassler, Emre Güler, Thorsten Holz, Andreas Zeller, and Rahul Gopinath. 2023. Systematic Assessment of Fuzzers Using Mutation Analysis. In Proceedings of the 32nd USENIX Conference on Security Symposium (Anaheim, CA, USA) (SEC ’23). USENIX Association, USA, Article 254, 18 pages.
Alex Groce, Josie Holmes, Darko Marinov, August Shi, and Lingming Zhang. 2018. An Extensible, Regular-Expression-Based Tool for Multi-Language Mutant Generation. In Proceedings of the 40th International Conference on Software Engineering: Companion Proceeedings (Gothenburg, Sweden) (ICSE ’18). Association for Computing Machinery, New York, NY, USA, 25–28. DOI: 10.1145/3183440.3183485
Alex Groce, Kush Jain, Rijnard van Tonder, Goutamkumar Tulajappa Kalburgi, and Claire Le Goues. 2022. Looking for Lacunae in Bitcoin Core’s Fuzzing Efforts. In Proceedings of the 44th International Conference on Software Engineering: Software Engineering in Practice (Pittsburgh, Pennsylvania) (ICSESEIP ’22). Association for Computing Machinery, New York, NY, USA, 185–186. DOI: 10.1145/3510457.3513072
Alex Groce, Goutamkumar Tulajappa Kalburgi, Claire Le Goues, Kush Jain, and Rahul Gopinath. 2022. Registered report: First, fuzz the mutants. In International Fuzzing Workshop, ser. FUZZING, Vol. 22.
Bernhard J. M. Grün, David Schuler, and Andreas Zeller. 2009. The Impact of Equivalent Mutants. In 2009 International Conference on Software Testing, Verification, and Validation Workshops. 192–199. DOI: 10.1109/ICSTW.2009.37
Pieter Hartel and Richard Schumi. 2020. Mutation Testing of Smart Contracts at Scale. In Tests and Proofs,Wolfgang Ahrendt and HeikeWehrheim (Eds.). Springer International Publishing, Cham, 23–42.
Adrian Herrera, Hendra Gunadi, Shane Magrath, Michael Norrish, Mathias Payer, and Antony L. Hosking. 2021. Seed selection for successful fuzzing. In Proceedings of the 30th ACM SIGSOFT International Symposium on Software Testing and Analysis (Virtual, Denmark) (ISSTA 2021). Association for Computing Machinery, New York, NY, USA, 230–243. DOI: 10.1145/3460319.3464795
Qiang Hu, Lei Ma, Xiaofei Xie, Bing Yu, Yang Liu, and Jianjun Zhao. 2019. Deep-Mutation++: A Mutation Testing Framework for Deep Learning Systems. In 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE). 1158–1161. DOI: 10.1109/ASE.2019.00126
K. Jain, G. Kalburgi, C. Le Goues, and A. Groce. 2023. Mind the Gap: The Difference Between Coverage and Mutation Score Can Guide Testing Efforts. In 2023 IEEE 34th International Symposium on Software Reliability Engineering (ISSRE). IEEE Computer Society, Los Alamitos, CA, USA, 102–113. DOI: 10.1109/ISSRE59848.2023.00036
Thijs Klooster, Fatih Turkmen, Gerben Broenink, Ruben Ten Hove, and Marcel Böhme. 2023. Continuous Fuzzing: A Study of the Effectiveness and Scalability of Fuzzing in CI/CD Pipelines. In 2023 IEEE/ACM International Workshop on Search-Based and Fuzz Testing (SBFT). 25–32. DOI: 10.1109/SBFT59156.2023.0 0015
Lech Madeyski, Wojciech Orzeszyna, Richard Torkar, and Mariusz Józala. 2014. Overcoming the Equivalent Mutant Problem: A Systematic Literature Review and a Comparative Experiment of Second Order Mutation. IEEE Transactions on Software Engineering 40, 1 (2014), 23–42. DOI: 10.1109/TSE.2013.44
Muhammad Rashid Naeem, Tao Lin, Hamad Naeem, and Hailu Liu. 2020. A machine learning approach for classification of equivalent mutants. Journal of Software: Evolution and Process 32, 5 (2020), e2238.
Olivier Nourry, Yutaro Kashiwa, Bin Lin, Gabriele Bavota, Michele Lanza, and Yasutaka Kamei. 2023. The Human Side of Fuzzing: Challenges Faced by Developers during Fuzzing Activities. ACM Trans. Softw. Eng. Methodol. 33, 1, Article 14 (nov 2023), 26 pages. DOI: 10.1145/3611668
Samuel Peacock, Lin Deng, Josh Dehlinger, and Suranjan Chakraborty. 2021. Automatic Equivalent Mutants Classification Using Abstract Syntax Tree Neural Networks. In 2021 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW). 13–18. DOI: 10.1109/ICSTW52544.2021.00016
Goran Petrović, Marko Ivanković, Gordon Fraser, and René Just. 2022. Practical Mutation Testing at Scale: A view from Google. IEEE Transactions on Software Engineering 48, 10 (2022), 3900–3912. DOI: 10.1109/TSE.2021.3107634
Amol Saxena, Roheet Bhatnagar, and Devesh Kumar Srivastava. 2021. Improving Effectiveness of Spectrum-based Software Fault Localization using Mutation Testing. In 2021 2nd International Conference for Emerging Technology (INCET). 1–7. DOI: 10.1109/INCET51464.2021.9456109
Ari Takanen, Jared D Demott, Charles Miller, and Atte Kettunen. 2018. Fuzzing for software security testing and quality assurance. Artech House.
Thierry Titcheu Chekam, Mike Papadakis, Tegawendé F Bissyandé, Yves Le Traon, and Koushik Sen. 2020. Selecting fault revealing mutants. Empir. Softw. Eng. 25, 1 (Jan. 2020), 434–487.
Vasudev Vikram, Isabella Laybourn, Ao Li, Nicole Nair, Kelton OBrien, Rafaello Sanna, and Rohan Padhye. 2023. Guiding Greybox Fuzzing with Mutation Testing. In Proceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis (<conf-loc>, <city>Seattle</city>, <state>WA</state>, <country> USA</country>, </conf-loc>) (ISSTA 2023). Association for Computing Machinery, New York, NY, USA, 929–941. DOI: 10.1145/3597926.3598107
Youngseok Yang, Taesoo Kim, and Byung-Gon Chun. 2021. Finding Consensus Bugs in Ethereum via Multi-transaction Differential Fuzzing. In 15th USENIX Symposium on Operating Systems Design and Implementation (OSDI 21). USENIX Association, 349–365. [link]
Lukas Bernhard, Tobias Scharnowski, Moritz Schloegel, Tim Blazytko, and Thorsten Holz. 2022. JIT-Picking: Differential Fuzzing of JavaScript Engines. In Proceedings of the 2022 ACMSIGSAC Conference on Computer and Communications Security (Los Angeles, CA, USA) (CCS ’22). Association for Computing Machinery, New York, NY, USA, 351–364. DOI: 10.1145/3548606.3560624
Claudinei Brito, Vinicius H. S. Durelli, Rafael S. Durelli, Simone R. S. de Souza, Auri M. R. Vincenzi, and Marcio Eduardo Delamaro. 2020. A Preliminary Investigation into Using Machine Learning Algorithms to Identify Minimal and Equivalent Mutants. In 2020 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW). 304–313. DOI: 10.1109/ICSTW50294.2020.00056
Liang Cheng, Yang Zhang, Yi Zhang, ChenWu, Zhangtan Li, Yu Fu, and Haisheng Li. 2019. Optimizing Seed Inputs in Fuzzing with Machine Learning. In 2019 IEEE/ACM 41st International Conference on Software Engineering: Companion Proceedings (ICSE-Companion). 244–245. DOI: 10.1109/ICSE-Companion.2019.00096
Seungjoon Chung and Shin Yoo. 2022. Augmenting Equivalent Mutant Dataset Using Symbolic Execution. In 2022 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW). IEEE, 150–159.
Leo Fernandes, Márcio Ribeiro, Rohit Gheyi, Marcio Delamaro, Márcio Guimarães, and André Santos. 2022. Put Your Hands In The Air! Reducing Manual Effort in Mutation Testing. In Proceedings of the XXXVI Brazilian Symposium on Software Engineering (<conf-loc>, <city>Virtual Event</city>, <country>Brazil</country>, </conf-loc>) (SBES ’22). Association for Computing Machinery, New York, NY, USA, 198–207. DOI: 10.1145/3555228.3555233
Philipp Görz, Björn Mathis, Keno Hassler, Emre Güler, Thorsten Holz, Andreas Zeller, and Rahul Gopinath. 2023. Systematic Assessment of Fuzzers Using Mutation Analysis. In Proceedings of the 32nd USENIX Conference on Security Symposium (Anaheim, CA, USA) (SEC ’23). USENIX Association, USA, Article 254, 18 pages.
Alex Groce, Josie Holmes, Darko Marinov, August Shi, and Lingming Zhang. 2018. An Extensible, Regular-Expression-Based Tool for Multi-Language Mutant Generation. In Proceedings of the 40th International Conference on Software Engineering: Companion Proceeedings (Gothenburg, Sweden) (ICSE ’18). Association for Computing Machinery, New York, NY, USA, 25–28. DOI: 10.1145/3183440.3183485
Alex Groce, Kush Jain, Rijnard van Tonder, Goutamkumar Tulajappa Kalburgi, and Claire Le Goues. 2022. Looking for Lacunae in Bitcoin Core’s Fuzzing Efforts. In Proceedings of the 44th International Conference on Software Engineering: Software Engineering in Practice (Pittsburgh, Pennsylvania) (ICSESEIP ’22). Association for Computing Machinery, New York, NY, USA, 185–186. DOI: 10.1145/3510457.3513072
Alex Groce, Goutamkumar Tulajappa Kalburgi, Claire Le Goues, Kush Jain, and Rahul Gopinath. 2022. Registered report: First, fuzz the mutants. In International Fuzzing Workshop, ser. FUZZING, Vol. 22.
Bernhard J. M. Grün, David Schuler, and Andreas Zeller. 2009. The Impact of Equivalent Mutants. In 2009 International Conference on Software Testing, Verification, and Validation Workshops. 192–199. DOI: 10.1109/ICSTW.2009.37
Pieter Hartel and Richard Schumi. 2020. Mutation Testing of Smart Contracts at Scale. In Tests and Proofs,Wolfgang Ahrendt and HeikeWehrheim (Eds.). Springer International Publishing, Cham, 23–42.
Adrian Herrera, Hendra Gunadi, Shane Magrath, Michael Norrish, Mathias Payer, and Antony L. Hosking. 2021. Seed selection for successful fuzzing. In Proceedings of the 30th ACM SIGSOFT International Symposium on Software Testing and Analysis (Virtual, Denmark) (ISSTA 2021). Association for Computing Machinery, New York, NY, USA, 230–243. DOI: 10.1145/3460319.3464795
Qiang Hu, Lei Ma, Xiaofei Xie, Bing Yu, Yang Liu, and Jianjun Zhao. 2019. Deep-Mutation++: A Mutation Testing Framework for Deep Learning Systems. In 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE). 1158–1161. DOI: 10.1109/ASE.2019.00126
K. Jain, G. Kalburgi, C. Le Goues, and A. Groce. 2023. Mind the Gap: The Difference Between Coverage and Mutation Score Can Guide Testing Efforts. In 2023 IEEE 34th International Symposium on Software Reliability Engineering (ISSRE). IEEE Computer Society, Los Alamitos, CA, USA, 102–113. DOI: 10.1109/ISSRE59848.2023.00036
Thijs Klooster, Fatih Turkmen, Gerben Broenink, Ruben Ten Hove, and Marcel Böhme. 2023. Continuous Fuzzing: A Study of the Effectiveness and Scalability of Fuzzing in CI/CD Pipelines. In 2023 IEEE/ACM International Workshop on Search-Based and Fuzz Testing (SBFT). 25–32. DOI: 10.1109/SBFT59156.2023.0 0015
Lech Madeyski, Wojciech Orzeszyna, Richard Torkar, and Mariusz Józala. 2014. Overcoming the Equivalent Mutant Problem: A Systematic Literature Review and a Comparative Experiment of Second Order Mutation. IEEE Transactions on Software Engineering 40, 1 (2014), 23–42. DOI: 10.1109/TSE.2013.44
Muhammad Rashid Naeem, Tao Lin, Hamad Naeem, and Hailu Liu. 2020. A machine learning approach for classification of equivalent mutants. Journal of Software: Evolution and Process 32, 5 (2020), e2238.
Olivier Nourry, Yutaro Kashiwa, Bin Lin, Gabriele Bavota, Michele Lanza, and Yasutaka Kamei. 2023. The Human Side of Fuzzing: Challenges Faced by Developers during Fuzzing Activities. ACM Trans. Softw. Eng. Methodol. 33, 1, Article 14 (nov 2023), 26 pages. DOI: 10.1145/3611668
Samuel Peacock, Lin Deng, Josh Dehlinger, and Suranjan Chakraborty. 2021. Automatic Equivalent Mutants Classification Using Abstract Syntax Tree Neural Networks. In 2021 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW). 13–18. DOI: 10.1109/ICSTW52544.2021.00016
Goran Petrović, Marko Ivanković, Gordon Fraser, and René Just. 2022. Practical Mutation Testing at Scale: A view from Google. IEEE Transactions on Software Engineering 48, 10 (2022), 3900–3912. DOI: 10.1109/TSE.2021.3107634
Amol Saxena, Roheet Bhatnagar, and Devesh Kumar Srivastava. 2021. Improving Effectiveness of Spectrum-based Software Fault Localization using Mutation Testing. In 2021 2nd International Conference for Emerging Technology (INCET). 1–7. DOI: 10.1109/INCET51464.2021.9456109
Ari Takanen, Jared D Demott, Charles Miller, and Atte Kettunen. 2018. Fuzzing for software security testing and quality assurance. Artech House.
Thierry Titcheu Chekam, Mike Papadakis, Tegawendé F Bissyandé, Yves Le Traon, and Koushik Sen. 2020. Selecting fault revealing mutants. Empir. Softw. Eng. 25, 1 (Jan. 2020), 434–487.
Vasudev Vikram, Isabella Laybourn, Ao Li, Nicole Nair, Kelton OBrien, Rafaello Sanna, and Rohan Padhye. 2023. Guiding Greybox Fuzzing with Mutation Testing. In Proceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis (<conf-loc>, <city>Seattle</city>, <state>WA</state>, <country> USA</country>, </conf-loc>) (ISSTA 2023). Association for Computing Machinery, New York, NY, USA, 929–941. DOI: 10.1145/3597926.3598107
Youngseok Yang, Taesoo Kim, and Byung-Gon Chun. 2021. Finding Consensus Bugs in Ethereum via Multi-transaction Differential Fuzzing. In 15th USENIX Symposium on Operating Systems Design and Implementation (OSDI 21). USENIX Association, 349–365. [link]
Publicado
30/09/2024
Como Citar
GARCIA, Bruno E. R.; DELAMARO, Marcio E.; SOUZA, Simone R. S..
Towards differential fuzzing to reduce manual efforts to identify equivalent mutants: A preliminary study. In: SIMPÓSIO BRASILEIRO DE ENGENHARIA DE SOFTWARE (SBES), 38. , 2024, Curitiba/PR.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 568-573.
DOI: https://doi.org/10.5753/sbes.2024.3557.
