A Controlled Experiment of Different Code Representations for Learning-Based Bug Repair

Namavar, Marjane; Nashid, Noor; Mesbah, Ali

doi:10.48550/arxiv.2110.14081

Cited by 2 publications

(1 citation statement)

References 51 publications

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“…Automated bug-fixing techniques based on DL can rely on different levels of code abstraction. Word tokenization is a commonly used one, even if higher-level abstractions (e.g., AST-based) allow to achieve better results [51].…”

Section: Automatic Bug-fixingmentioning

confidence: 99%

Using Transfer Learning for Code-Related Tasks

Mastropaolo¹,

Cooper²,

Palacio³

et al. 2022

Preprint

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Deep learning (DL) techniques have been used to support several code-related tasks such as code summarization and bug-fixing. In particular, pre-trained transformer models are on the rise, also thanks to the excellent results they achieved in Natural Language Processing (NLP) tasks. The basic idea behind these models is to first pre-train them on a generic dataset using a self-supervised task (e.g., filling masked words in sentences). Then, these models are fine-tuned to support specific tasks of interest (e.g., language translation). A single model can be fine-tuned to support multiple tasks, possibly exploiting the benefits of transfer learning. This means that knowledge acquired to solve a specific task (e.g., language translation) can be useful to boost performance on another task (e.g., sentiment classification). While the benefits of transfer learning have been widely studied in NLP, limited empirical evidence is available when it comes to code-related tasks. In this paper, we assess the performance of the Text-To-Text Transfer Transformer (T5) model in supporting four different code-related tasks: (i) automatic bug-fixing, (ii) injection of code mutants, (iii) generation of assert statements, and (iv) code summarization. We pay particular attention in studying the role played by pre-training and multi-task fine-tuning on the model's performance. We show that (i) the T5 can achieve better performance as compared to state-of-the-art baselines; and (ii) while pre-training helps the model, not all tasks benefit from a multi-task fine-tuning.

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Section: Automatic Bug-fixingmentioning

confidence: 99%

Using Transfer Learning for Code-Related Tasks

Mastropaolo¹,

Cooper²,

Palacio³

et al. 2022

Preprint

View full text Add to dashboard Cite

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Katana : Dual Slicing Based Context for Learning Bug Fixes

Sintaha

Nashid

Mesbah

2023

ACM Trans. Softw. Eng. Methodol.

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Contextual information plays a vital role for software developers when understanding and fixing a bug. Consequently, deep learning-based program repair techniques leverage context for bug fixes. However, existing techniques treat context in an arbitrary manner, by extracting code in close proximity of the buggy statement within the enclosing file, class, or method, without any analysis to find actual relations with the bug. To reduce noise, they use a predefined maximum limit on the number of tokens to be used as context. We present a program slicing-based approach, in which instead of arbitrarily including code as context, we analyze statements that have a control or data dependency on the buggy statement. We propose a novel concept called dual slicing , which leverages the context of both buggy and fixed versions of the code to capture relevant repair ingredients. We present our technique and tool called Katana , the first to apply slicing-based context for a program repair task. The results show Katana effectively preserves sufficient information for a model to choose contextual information while reducing noise. We compare against four recent state-of-the-art context-aware program repair techniques. Our results show Katana fixes between 1.5 to 3.7 times more bugs than existing techniques.

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A Controlled Experiment of Different Code Representations for Learning-Based Bug Repair

Cited by 2 publications

References 51 publications

Using Transfer Learning for Code-Related Tasks

Using Transfer Learning for Code-Related Tasks

Katana : Dual Slicing Based Context for Learning Bug Fixes

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