A genetic programming approach to automated software repair

By Stephanie Forrest, Thanhvu Nguyen, Westley Weimer, Claire Le Goues. 2009

264 citations recorded by the source corpus.

Subfields: Information Systems, Software

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Citations in the corpus

Automating Biomedical Data Science Through Tree-Based Pipeline Optimization (2016, background): “…have seen the growth of intelligent systems such as evolutionary algorithms that are capable of outperforming humans in a wide variety of tasks, such as antenna design for space missions [2], discovering and patching bugs in large software projects [3], and even in the study of ﬁnite algebras [4].”
The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities (2018, neutral): “One such algorithm is GenProg [45], which applies digital evolution to evolve code (called genetic programming [68]).”
A systematic study of automated program repair: Fixing 55 out of 105 bugs for $8 each (2012, methodology): “In previous work, we introduced GenProg [11], [12], [13], [14], a general method that uses genetic programming (GP) to repair a wide range of defect types in legacy software (e.”
Context-Aware Patch Generation for Better Automated Program Repair (2018, unknown): “Reducing the number of candidate patches and reducing the test cases required for validating a patch are two important aspects to boost the efficiency of search-based APR techniques [11].”
GenProg: A Generic Method for Automatic Software Repair (2012, background): “Some of these points were previously presented in early versions of this work [25], [26] or summarized for general audiences [27].”
An analysis of patch plausibility and correctness for generate-and-validate patch generation systems (2015, methodology): “For a more comprehensive treatment, including an analysis of the patches produced by the original GenProg system [58, 21] and a more comprehensive discussion of the ClearView patches and other related work, see the full version of the paper [49].”
Automated feedback generation for introductory programming assignments (2012, background): “There are also some genetic programming approaches that exploit redundancy present in other parts of the code for fixing faults [5, 14].”
Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science (2016, background): “From space antenna design [9] to software development [7] and debugging [5] to the study of ﬁnite algebras [17], evolutionary algorithms have outperformed humans in a variety of domains that were previously considered exclusive to humans.”
Automatically patching errors in deployed software (2009, background): “Genetic programming techniques can generate and search a space of abstract syntax tree modifications with the goal of automatically correcting an exercised defect in the underlying program [18].”