Hoppity: Learning Graph Transformations to Detect and Fix Bugs in Programs

By Elizabeth Dinella, Hanjun Dai, Ziyang Li, Mayur Naik, Le Song, Ke Wang, Ke Wang. 2020

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Citations in the corpus

Program Synthesis with Large Language Models (2021, methodology): “Many learning methods have been proposed for program repair [Allamanis et al., 2018b, Tarlow et al., 2019, Hellendoorn et al., 2019, Dinella et al., 2019, Yasunaga and Liang, 2020, Chen et al., 2019b, Pradel and Sen, 2018].”
Break-It-Fix-It: Unsupervised Learning for Program Repair (2021, background): “Development of an automatic code fixer is thus a key to enhancing programming productivity (Seo et al., 2014) and is an active area of research (Mesbah et al., 2019; Ding et al., 2020; Dinella et al., 2020).”
Graph-based, Self-Supervised Program Repair from Diagnostic Feedback (2020, background): “, 2019), name-based bugs (Pradel & Sen, 2018) and Javascript bugs (Dinella et al., 2020).”
CodeBLEU: a Method for Automatic Evaluation of Code Synthesis (2020, methodology): “…and Vechev 2014; Oda et al. 2015; Barone and Sennrich 2017; Chen, Liu, and Song 2018; Kanade et al. 2019; Husain et al. 2019; Feng et al. 2020; Dinella et al. 2020; Lachaux et al. 2020), different automatic evaluation methods for code synthesis are leveraged, including n-gram accuracy…”
Open Graph Benchmark: Datasets for Machine Learning on Graphs (2020, methodology): “Benchmarking results are given in Table 18, where we add “next-token edges” on top of the AST (as illustrated in Figure 4) to better capture the semantics of code graphs (Dinella et al., 2020).”
A syntax-guided edit decoder for neural program repair (2021, methodology): “Several existing DL-based approaches also use the idea of generating edits on programs [4, 5, 9, 65, 74].”
Can OpenAI's Codex Fix Bugs?: An evaluation on QuixBugs (2022, methodology): “A variety of different APR approaches have been proposed in the recent years: i) using genetic-programming (e.g., GenProg [8] or ARJA [29]), ii) using repair patterns (such as PAR [13], ELIXIR [24] or TBar [17]), iii) code retrieval-based approaches (e.g., ssFix [26] or LSRepair [18]), iv) or using ”
Patching as Translation: the Data and the Metaphor (2020, result): “nappropriate for the task. On the other hand, our results shed light on the effectiveness of a recent competing approach: repairing bugs by synthesizing edits of code conditional on the buggy context [8, 29, 31]. Not only does this approach agree better with our empirical findings, we implemented a ”
Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks (2020, unknown): “GNNs have been applied a variety of program representations and static analysis tasks [2, 8, 13, 21, 23, 25, 28].”
Learning semantic program embeddings with graph interval neural network (2020, methodology): “2019), bug localization and xing (Dinella et al. 2020), and type inference (Wei et al.”
Neural Edit Completion (2020, methodology): “Dinella et al. [2020] evaluated their model on examples that contain three single-node operations at most.”
Leveraging Automated Unit Tests for Unsupervised Code Translation (2021, methodology): “In recent years, deep learning methods have been used to tackle various tasks in software engineering, with a particular interest in bug detection and repair (Wang et al., 2018; Chen et al., 2019; Allamanis et al., 2018; Tarlow et al., 2020; Murali et al., 2021; Dinella et al., 2020; Yasunaga & Lian”
PLUR: A Unifying, Graph-Based View of Program Learning, Understanding, and Repair (2021, methodology): “For example, VarMisuseH and Hoppity are graph-based program repair tasks but with differing output representations.”