2015Unpublished venueRequires access

CloCom: Mining existing source code for automatic comment generation

Edmund L. Wong, Taiyue Liu, Lin Tan

Open publisher page 168 citations

Abstract

Code comments are an integral part of software development. They improve program comprehension and software maintainability. The lack of code comments is a common problem in the software industry. Therefore, it is beneficial to generate code comments automatically. In this paper, we propose a general approach to generate code comments automatically by analyzing existing software repositories. We apply code clone detection techniques to discover similar code segments and use the comments from some code segments to describe the other similar code segments. We leverage natural language processing techniques to select relevant comment sentences. In our evaluation, we analyze 42 million lines of code from 1,005 open source projects from GitHub, and use them to generate 359 code comments for 21 Java projects. We manually evaluate the generated code comments and find that only 23.7% of the generated code comments are good. We report to the developers the good code comments, whose code segments do not have an existing code comment. Amongst the reported code comments, seven have been confirmed by the developers as good and committable to the software repository while the rest await for developers' confirmation. Although our approach can generate good and committable comments, we still have to improve the yield and accuracy of the proposed approach before it can be used in practice with full automation.

About this research paper

What this paper is about

Code comments are an integral part of software development. They improve program comprehension and software maintainability. The lack of code comments is a common problem in the software industry. Therefore, it is beneficial to generate code comments automatically. In this paper, we propose a general approach to generate code comments automatically by analyzing existing software repositories. We apply code clone detection techniques to discover similar code segments and use the comments from some code segments to describe the other similar code segments. We leverage natural language processing techniques to select relevant comment sentences. In our evaluation, we analyze 42 million lines of code from 1,005 open source projects from GitHub, and use them to generate 359 code comments for 21 Java projects. We manually evaluate the generated code comments and find that only 23.7% of the generated code comments are good. We report to the developers the good code comments, whose code segments do not have an existing code comment. Amongst the reported code comments, seven have been confirmed by the developers as good and committable to the software repository while the rest await for developers' confirmation. Although our approach can generate good and committable comments, we still have to improve the yield and accuracy of the proposed approach before it can be used in practice with full automation.

Why it matters

OpenAlex reports 168 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

Code comments are an integral part of software development. They improve program comprehension and software maintainability. The lack of code comments is a common problem in the software industry. Therefore, it is beneficial to generate code comments automatically. In this paper, we propose a general approach to generate code comments automatically by analyzing existing software repositories. We apply code clone detection techniques to discover similar code segments and use the comments from some code segments to describe the other similar code segments. We leverage natural language processing techniques to select relevant comment sentences. In our evaluation, we analyze 42 million lines of code from 1,005 open source projects from GitHub, and use them to generate 359 code comments for 21 Java projects. We manually evaluate the generated code comments and find that only 23.7% of the generated code comments are good. We report to the developers the good code comments, whose code segments do not have an existing code comment. Amongst the reported code comments, seven have been confirmed by the developers as good and committable to the software repository while the rest await for developers' confirmation. Although our approach can generate good and committable comments, we still have to improve the yield and accuracy of the proposed approach before it can be used in practice with full automation.

Key concepts: Computer science, KPI-driven code analysis, Code review, Program comprehension, Static program analysis, Source code, Codebase, Programming language

Related papers

Back to paper searchBrowse research topicsOriginal source
CloCom: Mining existing source code for automatic comment generation — Research Paper | ScholarLens