The ManyBugs and IntroClass Benchmarks for Automated Repair of C Programs
By Claire Le Goues, Neal J. Holtschulte, Edward K. Smith, Yuriy Brun, Premkumar T. Devanbu, Stephanie Forrest, Westley Weimer. IEEE Transactions on Software Engineering, 2015
343 citations recorded by the source corpus.
Subfields: Information Systems, Software
View Paper: https://www.semanticscholar.org/paper/ea73e3c069f4593e916737ca8f59a3faf51d1f0c
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In Information Systems
- Michele Tufano
- Petros Maniatis
- Aditya Kanade
- Denys Poshyvanyk
- Gabriele Bavota
- Lin Tan
- Thibaud Lutellier
- Rishabh Singh
In Software
- Michele Tufano
- Gabriele Bavota
- Denys Poshyvanyk
- Rishabh Singh
- Lingming Zhang
- Lin Tan
- Thibaud Lutellier
- Abhik Roychoudhury
Citations in the corpus
- CoCoNuT: combining context-aware neural translation models using ensemble for program repair (2020, methodology): “On the ManyBugs benchmark, CoCoNuT fixes seven bugs, outperforming GenProg, Kali, and RSRepair but is outperformed by other techniques.”
- A syntax-guided edit decoder for neural program repair (2021, methodology): “The last one, IntroClassJava [11], consists of 297 buggy Java programs generated from the IntroClass [34] benchmark for C.”
- Context-Aware Patch Generation for Better Automated Program Repair (2018, background): “IntroClassJava is the Java version of the IntroClass benchmark originally proposed by Le Goues et al. [22].”
- An Empirical Study on Learning Bug-Fixing Patches in the Wild via Neural Machine Translation (2018, methodology): “o likely sources of imprecision in our analyses. To have enough training data, we mined bug-fixes in GitHub repositories rather than using curated bug-fix datasets such as Defects4j [34] or IntroClass[44], useful but very limited in size. To mitigate imprecisions in our datasets, we manually analyze”
- Neural Program Repair by Jointly Learning to Localize and Repair (2019, methodology): “The APR community has also designed some repair bechmarks, such as ManyBugs and IntroClass (Goues et al., 2015), and Defects4J (Just et al., 2014), for test-based program repair techniques.”
- Angelix: Scalable Multiline Program Patch Synthesis via Symbolic Analysis (2016, methodology): “However, we note that the GenProg benchmark we used for our experiments is one of the most extensive one available in the literature (the Many-Bugs benchmark [15] has also been released very recently as an extension of the GenProg benchmark).”
- Automated Vulnerability Detection in Source Code Using Deep Representation Learning (2018, methodology): “2 package from the ManyBugs dataset [17] took nearly a day of effort.”
- Automated Program Repair in the Era of Large Pre-trained Language Models (2022, neutral): “3) ManyBugs [64]: A C repair dataset consisting of 185 bugs gathered from 9 open-source projects with developer written tests.”