SampleFix: Learning to Correct Programs by Sampling Diverse Fixes

By Hossein Hajipour, Apratim Bhattacharyya, Mario Fritz. 2019

14 citations recorded by the source corpus.

Subfields: Prompting & Reasoning

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In Prompting & Reasoning

1. Percy Liang
2. Michihiro Yasunaga

Citations in the corpus

• Break-It-Fix-It: Unsupervised Learning for Program Repair (2021, methodology): “, dropping tokens) and prepare synthetic paired data 〈perturbed code, good code〉 to train a fixer (Pu et al., 2016; Gupta et al., 2017; Ahmed et al., 2018; Hajipour et al., 2019; Yasunaga & Liang, 2020).”
• Graph-based, Self-Supervised Program Repair from Diagnostic Feedback (2020, methodology): “While prior works in program repair purely apply sequence-to-sequence (seq2seq) models to programs (Gupta et al., 2017; Hajipour et al., 2019) or rely on the program’s Abstract Syntax Tree (AST) representations (Mesbah et al.”