Mining Version Control System for Automatically Generating Commit Comment

Huang, Yuan; Zheng, Qiaoyang; Chen, Xiangping; Xiong, Yingfei; Liu, Zhiyong; Luo, Xiliang

doi:10.1109/esem.2017.56

Cited by 39 publications

(21 citation statements)

References 31 publications

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“…To identify the comments in source code, an AST‐based static analysis algorithm is employed to analyze the syntactic type of each code line in source code . The common syntactic types in Java language include IfStatement , ForStatement , ReturnType , VariableDeclaration , etc . The static analysis algorithm identifies the comment line in source code as CommentStatement type.…”

Section: Empirical Studymentioning

confidence: 99%

“…” In our study, the process of the structural features extraction is performed using a variety of techniques. For example, to extract features Return Value , Invoked Methods , and Variable Amount from method snippets, we employ AST‐based static analysis algorithm to identify the syntactic types of each code line, and then identify whether the method contains the corresponding features. To extract the Cyclomatic Complexity feature, we use SourceMonitor tool to calculate the cyclomatic complexity of source code.…”

Section: Commenting Necessity Identificationmentioning

confidence: 99%

“…In this study, the code syntax is subdivided into 96 types (eg, IfStatement , ForStatement , ReturnType , VariableDeclaration , etc) . We employ the abstract syntax tree to parse the syntax structure of each code statement in the source code.…”

Section: Commenting Necessity Identificationmentioning

confidence: 99%

“…We apply our approach to identify the commenting necessity of the methods on eight Java open‐source projects in our evaluation. The selected projects are commonly evaluated projects in most of the software engineering‐related research . Since we would like to evaluate the performance of our approach when applying it on the projects coming from different domains, the selected projects belong to text editor, chart library, graphics framework, search engine toolkit, etc.…”

Section: Commenting Necessity Identificationmentioning

confidence: 99%

“…According to the previous experience in mining data sets from software repository, there is a small portion of instances in the training set might represent incorrect commenting instances. Therefore, it is reasonable to assume that some instances that required header comments are not actually commented, while others are unnecessarily commented.…”

Section: Commenting Necessity Identificationmentioning

confidence: 99%

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Does your code need comment?

et al. 2019

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Code comments convey information about the programmers' intention in a more explicit but less rigorous manner than source code. This information can assist programmers in various tasks, such as code comprehension, reuse, and maintenance. To better understand the properties of the comments existing in the source code, we analyzed more than 450 000 comments across 136 popular open-source software systems coming different domains. We found that the methods involving header comments and internal comments were shown low percentages in software systems, ie, 4.4% and 10.27%, respectively. As an application of our findings, we propose an automatic approach to determine whether a method needs a header comment, known as commenting necessity identification. Specifically, we identify the important factors for determining the commenting necessity of a method and extract them as structural features, syntactic features, and textual features. Then, by applying machine learning techniques and noise-handling techniques, we achieve a precision of 88.5% on eight open-source software from GitHub. The encouraging experimental results demonstrate the feasibility and effectiveness of our approach.

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Section: Empirical Studymentioning

confidence: 99%

Section: Commenting Necessity Identificationmentioning

confidence: 99%

Section: Commenting Necessity Identificationmentioning

confidence: 99%

Section: Commenting Necessity Identificationmentioning

confidence: 99%

Section: Commenting Necessity Identificationmentioning

confidence: 99%

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