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It is essential to detect functional differences between programs in various software engineering tasks, such as automated program repair, mutation testing, and code refactoring.
Differential testing for software
William M McKeeman. 1998 · 1998
Earlier work this paper cites.
Automated testing of refactoring engines. In Proceedings of the the 6th joint meeting of the European software engineering conference and the ACM SIGSOFT symposium on The foundations of software engineering . 185–194
Brett Daniel, Danny Dig, Kely Garcia, and Darko Marinov. 2007 · 2007
Earlier work this paper cites.
Differential testing: a new approach to change detection. In The 6th Joint Meeting on European software engineering conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering: Companion Papers . 549–552
Robert B Evans and Alberto Savoia. 2007 · 2007
Earlier work this paper cites.
Randoop: feedback-directed random testing for Java. In Companion to the 22nd ACM SIGPLAN conference on Object-oriented programming systems and applications companion . 815–816
Carlos Pacheco and Michael D Ernst. 2007 · 2007
Earlier work this paper cites.
DiffGen: Automated Regression Unit-Test Generation. In Proceedings of the 23rd IEEE/ACM International Conference on Automated Software Engineering (ASE ’08) . IEEE Computer Society, USA, 407–410
K. Taneja and Tao Xie. 2008a · 2008
Earlier work this paper cites.
DiffGen: Automated regression unit-test generation. In 2008 23rd IEEE/ACM International Conference on Automated Software Engineering . IEEE, 407–410
Kunal Taneja and Tao Xie. 2008b · 2008
Earlier work this paper cites.
Evosuite: automatic test suite generation for object-oriented software. In Proceedings of the 19th ACM SIGSOFT symposium and the 13th European conference on Foundations of software engineering . 416–419
Gordon Fraser and Andrea Arcuri. 2011 · 2011
Earlier work this paper cites.
QuixBugs: A multi-lingual program repair benchmark set based on the Quixey Challenge. In Proceedings Companion of the 2017 ACM SIGPLAN international conference on systems, programming, languages, and applications: software for humanity . 55–56
Derrick Lin, James Koppel, Angela Chen, and Armando Solar-Lezama. 2017 · 2017
Earlier work this paper cites.
Automated test case generation as a many-objective optimisation problem with dynamic selection of the targets
Annibale Panichella, Fitsum Meshesha Kifetew, and Paolo Tonella. 2017 · 2017
Earlier work this paper cites.
Search-based detection of deviation failures in the migration of legacy spreadsheet applications. In Proceedings of the 27th ACM SIGSOFT International Symposium on Software Testing and Analysis . 266–275
M Moein Almasi, Hadi Hemmati, Gordon Fraser, Phil McMinn, and Janis Benefelds. 2018 · 2018
Earlier work this paper cites.
Dlfuzz: Differential fuzzing testing of deep learning systems. In Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering . 739–743
Jianmin Guo, Yu Jiang, Yue Zhao, Quan Chen, and Jiaguang Sun. 2018 · 2018
Earlier work this paper cites.
Test generation for higher-order functions in dynamic languages
Marija Selakovic, Michael Pradel, Rezwana Karim, and Frank Tip. 2018 · 2018
Earlier work this paper cites.
Perception and practices of differential testing. In 2019 IEEE/ACM 41st International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP) . IEEE, 71–80
Muhammad Ali Gulzar, Yongkang Zhu, and Xiaofeng Han. 2019 · 2019
Earlier work this paper cites.
Code4Bench: A multidimensional benchmark of Codeforces data for different program analysis techniques
Amirabbas Majd, Mojtaba Vahidi-Asl, Alireza Khalilian, Ahmad Baraani-Dastjerdi, and Bahman Zamani. 2019 · 2019
Earlier work this paper cites.
Diffuzz: differential fuzzing for side-channel analysis. In 2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE) . IEEE, 176–187
Shirin Nilizadeh, Yannic Noller, and Corina S Pasareanu. 2019 · 2019
Earlier work this paper cites.
A systematic literature review of techniques and metrics to reduce the cost of mutation testing
Alessandro Viola Pizzoleto, Fabiano Cutigi Ferrari, Jeff Offutt, Leo Fernandes, and Márcio Ribeiro. 2019 · 2019
Earlier work this paper cites.
Detecting semantic conflicts via automated behavior change detection. In 2020 IEEE International Conference on Software Maintenance and Evolution (ICSME) . IEEE, 174–184
Leuson Da Silva, Paulo Borba, Wardah Mahmood, Thorsten Berger, and João Moisakis. 2020 · 2020
Earlier work this paper cites.
Codebert: A pre-trained model for programming and natural languages
Zhangyin Feng, Daya Guo, Duyu Tang, Nan Duan, Xiaocheng Feng, Ming Gong, Linjun Shou, Bing Qin, Ting Liu, Daxin Jiang, et al · 2020
Earlier work this paper cites.
Automated unit test generation for python. In Search-Based Software Engineering: 12th International Symposium, SSBSE 2020, Bari, Italy, October 7–8, 2020, Proceedings 12 . Springer, 9–24
Stephan Lukasczyk, Florian Kroiß, and Gordon Fraser. 2020 · 2020
Earlier work this paper cites.
Unit test case generation with transformers and focal context
Michele Tufano, Dawn Drain, Alexey Svyatkovskiy, Shao Kun Deng, and Neel Sundaresan. 2020 · 2020
Earlier work this paper cites.
TestMC: testing model counters using differential and metamorphic testing. In Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering . 709–721
Muhammad Usman, Wenxi Wang, and Sarfraz Khurshid. 2020 · 2020
Earlier work this paper cites.
On learning meaningful assert statements for unit test cases. In Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering . 1398–1409
Cody Watson, Michele Tufano, Kevin Moran, Gabriele Bavota, and Denys Poshyvanyk. 2020 · 2020
Earlier work this paper cites.
Evaluating large language models trained on code
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al · 2021
Earlier work this paper cites.
Measuring coding challenge competence with apps
Dan Hendrycks, Steven Basart, Saurav Kadavath, Mantas Mazeika, Akul Arora, Ethan Guo, Collin Burns, Samir Puranik, Horace He, Dawn Song, et al · 2021
Earlier work this paper cites.
SEMEO: A Semantic Equivalence Analysis Framework for Obfuscated Android Applications. In Mobile and Ubiquitous Systems: Computing, Networking and Services: 18th EAI International Conference, MobiQuitous 2021, Virtual Event, November 8-11, 2021, Proceedings , Vol. 419. Springer Nature, 322
Bruno VieiraB, Gregg Resende Rothermel, E Silva, and Hamid Jackson Bagheri. 2022 · 2021
Cited alongside, same era.
Duo: Differential fuzzing for deep learning operators
Xufan Zhang, Jiawei Liu, Ning Sun, Chunrong Fang, Jia Liu, Jiang Wang, Dong Chai, and Zhenyu Chen. 2021 · 2021
Cited alongside, same era.
Explaining the behaviour of game agents using differential comparison. In Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering . 1–8
Ezequiel Castellano, Xiao-Yi Zhang, Paolo Arcaini, Toru Takisaka, Fuyuki Ishikawa, Nozomu Ikehata, and Kosuke Iwakura. 2022 · 2022
Cited alongside, same era.
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Bei Chen, Fengji Zhang, Anh Nguyen, Daoguang Zan, Zeqi Lin, Jian-Guang Lou, and Weizhu Chen. 2022 · 2022
Reinforcement Learning from Automatic Feedback for High-Quality Unit Test Generation
Benjamin Steenhoek, Michele Tufano, Neel Sundaresan, and Alexey Svyatkovskiy. 2023 · 2023
Later among the works it cites.
Can large language models write good property-based tests?
Vasudev Vikram, Caroline Lemieux, and Rohan Padhye. 2023 · 2023
Later among the works it cites.
ChatUniTest: a ChatGPT-based automated unit test generation tool
Zhuokui Xie, Yinghao Chen, Chen Zhi, Shuiguang Deng, and Jianwei Yin. 2023 · 2023
Later among the works it cites.
A Generative and Mutational Approach for Synthesizing Bug-Exposing Test Cases to Guide Compiler Fuzzing. In Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering . 1127–1139
Guixin Ye, Tianmin Hu, Zhanyong Tang, Zhenye Fan, Shin Hwei Tan, Bo Zhang, Wenxiang Qian, and Zheng Wang. 2023 · 2023
Later among the works it cites.
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Elizabeth Dinella, Gabriel Ryan, Todd Mytkowicz, and Shuvendu K Lahiri. 2022 · 2022
Cited alongside, same era.
Interactive code generation via test-driven user-intent formalization
Shuvendu K Lahiri, Aaditya Naik, Georgios Sakkas, Piali Choudhury, Curtis von Veh, Madanlal Musuvathi, Jeevana Priya Inala, Chenglong Wang, and Jianfeng Gao. 2022 · 2022
Cited alongside, same era.
Pynguin: Automated unit test generation for python. In Proceedings of the ACM/IEEE 44th International Conference on Software Engineering: Companion Proceedings . 168–172
Stephan Lukasczyk and Gordon Fraser. 2022 · 2022
Cited alongside, same era.
Automating Differential Testing with Overapproximate Symbolic Execution. In 2022 IEEE Conference on Software Testing, Verification and Validation (ICST) . IEEE, 256–266
Richard Rutledge and Alessandro Orso. 2022 · 2022
Cited alongside, same era.
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Ebert Schoofs, Mehrdad Abdi, and Serge Demeyer. 2022 · 2022
Cited alongside, same era.
Generating accurate assert statements for unit test cases using pretrained transformers. In Proceedings of the 3rd ACM/IEEE International Conference on Automation of Software Test . 54–64
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Cited alongside, same era.
SBDT: Search-Based Differential Testing of Certificate Parsers in SSL/TLS Implementations. In Proceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis . 967–979
Chu Chen, Pinghong Ren, Zhenhua Duan, Cong Tian, Xu Lu, and Bin Yu. 2023 · 2023
Cited alongside, same era.
Large language models are zero-shot fuzzers: Fuzzing deep-learning libraries via large language models. In Proceedings of the 32nd ACM SIGSOFT international symposium on software testing and analysis . 423–435
Yinlin Deng, Chunqiu Steven Xia, Haoran Peng, Chenyuan Yang, and Lingming Zhang. 2023 · 2023
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