Automated Large Program Repair based on Big Code

Thuy, Hoang Van; Anh, Phan Viet; Hoài, Nguyễn Xuân

doi:10.1145/3287921.3287958

Cited by 2 publications

(2 citation statements)

References 17 publications

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“…There are several approaches that use logic-based metrics based on the relationships of the features used. Specifically, Van Thuy et al [326] extracted twelve relations of statements and blocks for Bi-gram model using Big code to prune the search space, and make the patches generated by Prophet [197] more efficient and precise. Alrajeh et al [24] identified counterexamples and witness traces using model checking for logic-based learning to perform repair process automatically.…”

Section: Feature Extractionmentioning

confidence: 99%

“…Furthermore, Amorim et al [29] applied, a word embedding model (Word2Vec), to facilitate the evaluation of repair processes, by considering the naturalness obtained from known bug fixes. Van Thuy et al [326] have also applied word representations, and extracted relations of statements and blocks for a Bi-gram model using Big code, to improve the existing learning-aid-based repair tool Prophet [197]. Gupta et al [122] used word embeddings and reinforcement learning to fix erroneous C student programs with typographic errors.…”

Section: Model Trainingmentioning

confidence: 99%

See 1 more Smart Citation

A Survey on Machine Learning Techniques for Source Code Analysis

Sharma¹,

Kechagia²,

Georgiou³

et al. 2021

Preprint

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Context:The advancements in machine learning techniques have encouraged researchers to apply these techniques to a myriad of software engineering tasks that use source code analysis such as testing and vulnerabilities detection. A large number of studies poses challenges to the community to understand the current landscape. Objective: We aim to summarize the current knowledge in the area of applied machine learning for source code analysis. Method: We investigate studies belonging to twelve categories of software engineering tasks and corresponding machine learning techniques, tools, and datasets that have been applied to solve them. To do so, we carried out an extensive literature search and identified 364 primary studies published between 2002 and 2021. We summarize our observations and findings with the help of the identified studies. Results: Our findings suggest that the usage of machine learning techniques for source code analysis tasks is consistently increasing. We synthesize commonly used steps and the overall workflow for each task, and summarize the employed machine learning techniques. Additionally, we collate a comprehensive list of available datasets and tools useable in this context. Finally, we summarize the perceived challenges in this area that include availability of standard datasets, reproducibility and replicability, and hardware resources. CCS Concepts: • Software and its engineering → Software libraries and repositories; Software maintenance tools; Software post-development issues; Maintaining software; • Computing methodologies → Machine learning.

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Section: Feature Extractionmentioning

confidence: 99%