Code Naturalness to Assist Search Space Exploration in Search-Based Program Repair Methods

Dantas, Altino; Souza, Eduardo Faria de; Souza, Jerffeson; Camilo-Junior, Celso G.

doi:10.1007/978-3-030-27455-9_12

Cited by 5 publications

(2 citation statements)

References 6 publications

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“…For instance, Saha et al [266], Wang et al [337], and Chen et al [74] leveraged Defects4J for the evaluations of their approaches. Additionally, Dantas et al [88] used the IntroClass benchmark and Majd et al [207] conducted experiments using 119,989 C/C++ programs within Code4Bench. Wu et al [343] used the DeepFix dataset that contains 46,500 correct C programs and 6,975 programs with errors for their graph-based dl approach for syntax error correction.…”

Section: Data Collectionmentioning

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%

“…Such approaches often leverage word embeddings. Dantas et al [88] combined Doc2Vec and lstm, to capture dependencies between source code statements, and improve the fault-localization step of program repair. Ahmed et al [7] developed a repair approach (Tracer) for fixing compilation errors using rnns.…”

Section: Model Trainingmentioning

confidence: 99%