GenProg: A Generic Method for Automatic Software Repair

By Claire Le Goues, Thanhvu Nguyen, Stephanie Forrest, Westley Weimer. 2012

1,227 citations recorded by the source corpus.

Subfields: Information Systems, Software, Signal Processing

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Citations in the corpus

Program Synthesis with Large Language Models (2021, neutral): “Program repair is the problem of automatically fixing bugs in programs, often based on a test suite [Le Goues et al., 2012, Long and Rinard, 2016].”
Automatically Evading Classifiers: A Case Study on PDF Malware Classifiers (2016, methodology): “tasks including fixing legacy software bugs [17], software reverse engineering [13], and software re-engineering [23].”
An Empirical Study on Learning Bug-Fixing Patches in the Wild via Neural Machine Translation (2018, methodology): “acceptable one according to a specification [54]. Behavioral repair techniques in particular change the behavior of a program under repair by changing its source or binary code [54]. These techniques [20, 45, 73] rely on a critical assumption, the redundancy assumption, that claims large programs co”
Neural Program Repair by Jointly Learning to Localize and Repair (2019, background): “The hypothesis that a program that contains a bug at a location likely contains ingredients of the repair elsewhere in the program (Engler et al., 2001) has been used quite effectively in practice (Le Goues et al., 2012).”
Learning to Fix Build Errors with Graph2Diff Neural Networks (2019, background): “Essentially, the copy mechanism is a way to encode into a neural network the insight from prior research on program repair [6, 18] that often code contains the seeds of its own repair.”
CoCoNuT: combining context-aware neural translation models using ensemble for program repair (2020, methodology): “One of the main approaches for automatic program repair is the G&V method [17, 38, 52, 69, 88, 89].”
Ask the Mutants: Mutating Faulty Programs for Fault Localization (2014, methodology): “We applied Jaccard, Ochiai, Op2, and MUSE, to GenProg-FL in order to ﬁx look utx 4.3 , which is one of the subject programs recently used by Le Goues et al. [7].”
Graph-Based Mining of In-the-Wild, Fine-Grained, Semantic Code Change Patterns (2019, methodology): “Other researchers detect the patterns of changes for program auto-repairing [19], [15] or software evolution [45], [48], [40].”
Neurosymbolic Programming (2021, methodology): “Evolutionary methods are a popular paradigm in classical program synthesis (Krawiec, 2016; Le Goues et al., 2011).”
Studying the Usage of Text-To-Text Transfer Transformer to Support Code-Related Tasks (2021, background): “Several of them [27]–[35] rely on the redundancy assumption, claiming that large programs contain the seeds of their own repair.”
Is the cure worse than the disease? overfitting in automated program repair (2015, methodology): “Several experiments described in Section 4 use this method for measuring patch quality in terms of overﬁtting and generalizability (the inverse of overﬁtting).”
RepairAgent: An Autonomous, LLM-Based Agent for Program Repair (2024, neutral): “…have explored various approaches to address the challenge of automatically ﬁxing bugs [1], including techniques based on manually designed [2], [3] and (semi-)automatically extracted [4], [5], [6] ﬁx patterns, based on symbolic constraints [7], [8], [9], and various machine learning-based…”
TBar: revisiting template-based automated program repair (2019, methodology): “given that faults in software cause substantial financial losses to the software industry [5, 49], there is a momentum in minimizing the time-to-fix intervals by APR. Recently, various APR approaches [7, 8, 13, 14, 17, 19, 21, 22, 25, 27, 33, 34, 36, 37, 39, 45, 48, 61, 63, 71, 72] have been propose”
A Large-Scale Study on Repetitiveness, Containment, and Composability of Routines in Open-Source Projects (2016, methodology): “First, the automated program repairing approaches [22, 54] involve searching a large space of programs in the codebase with the assumption that a ﬁx might already occur in the same program or other ones [22].”
Post-compiler software optimization for reducing energy (2014, methodology): “Earlier studies of a similar approach used for program repair suggest that our minimization step mitigates this threat signiﬁcantly [34, 55].”
DLFix: Context-based Code Transformation Learning for Automated Program Repair (2020, methodology): “Anti-patterns were integrated in existing search-based APR tools [38] (namely, GenProg [9] and SPR [23]) to help alleviate the problem of incor rect or incomplete fixes.”
Cross-project code clones in GitHub (2018, background): “…somewhere that is surprisingly similar to code elsewhere, and can arise from copy-pasting practices (Kim et al. 2004), automatic program generators (Goues et al. 2012; Meng et al. 2011, 2013), or even emerge independently, due to the predictable syntax of underlying programming languages and API…”
The strength of random search on automated program repair (2014, methodology): “In most cases, it is the computation of ﬁtness (by running ﬁxed size of test cases for each candidate patch) that occupies the largest part of the overall repair process [15, 23].”