A New Word Embedding Approach to Evaluate Potential Fixes for Automated Program Repair

Amorim, Leonardo Afonso; Freitas, Mateus F.; Dantas, Altino; Souza, Eduardo Faria de; Camilo-Junior, Celso G.; Martins, Wellington Santos

doi:10.1109/ijcnn.2018.8489079

Cited by 5 publications

(3 citation statements)

References 25 publications

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“…Most studies leverage source-code naturalness. For instance, Tufano et al [320] extracted millions of bug-fixing pairs from GitHub, Amorim et al [29] leveraged the naturalness obtained from a corpus of known fixes, and Chen et al [73] used natural language structures from source code. Furthermore, many studies develop their own large-scale bug benchmarks.…”

Section: Data Collectionmentioning

confidence: 99%

“…These studies mostly employ word embeddings for code representation and abstraction. In particular, Amorim et al [29], Santos et al [270], Svyatkovskiy et al [306], and Chen et al [73], leveraged source-code naturalness and applied nlp-based metrics. Tian et al [314] employed different representation learning approaches for code changes to derive embeddings for similarity computations.…”

Section: Feature Extractionmentioning

confidence: 99%

“…This study uses bert transformer-based embeddings. 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].…”

Section: Model Trainingmentioning

confidence: 99%

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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: Data Collectionmentioning

confidence: 99%