Classifying Code Comments in Java Open-Source Software Systems

Pascarella, Luca; Bacchelli, Alberto

doi:10.1109/msr.2017.63

Cited by 81 publications

(82 citation statements)

References 22 publications

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“…However, besides summarizing APIs, developers may also add TODO lists, notes, etc. in the method comments [20], which are noises in our scenario. Therefore, we filter out these tuples, if the word sequence in a tuple:…”

Section: Word-api Tuple Constructionmentioning

confidence: 99%

Bridging Semantic Gaps between Natural Languages and APIs with Word Embedding

Jiang

Kamei

et al. 2020

IIEEE Trans. Software Eng.

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Developers increasingly rely on text matching tools to analyze the relation between natural language words and APIs. However, semantic gaps, namely textual mismatches between words and APIs, negatively affect these tools. Previous studies have transformed words or APIs into low-dimensional vectors for matching; however, inaccurate results were obtained due to the failure of modeling words and APIs simultaneously. To resolve this problem, two main challenges are to be addressed: the acquisition of massive words and APIs for mining and the alignment of words and APIs for modeling. Therefore, this study proposes Word2API to effectively estimate relatedness of words and APIs. Word2API collects millions of commonly used words and APIs from code repositories to address the acquisition challenge. Then, a shuffling strategy is used to transform related words and APIs into tuples to address the alignment challenge. Using these tuples, Word2API models words and APIs simultaneously. Word2API outperforms baselines by 10%-49.6% of relatedness estimation in terms of precision and NDCG. Word2API is also effective on solving typical software tasks, e.g., query expansion and API documents linking. A simple system with Word2API-expanded queries recommends up to 21.4% more related APIs for developers. Meanwhile, Word2API improves comparison algorithms by 7.9%-17.4% in linking questions in Question&Answer communities to API documents.

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Section: Word-api Tuple Constructionmentioning

confidence: 99%

Bridging Semantic Gaps between Natural Languages and APIs with Word Embedding

Jiang

Kamei

et al. 2020

IIEEE Trans. Software Eng.

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“…They focused on macroscopic comments, such as a module header or method description. Pascarella and Bacchelli further developed this idea and proposed a hierarchical categories [3].…”

Section: Backgroundsmentioning

confidence: 99%

“…While a comment extent and comment target are mostly syntactic elements, a comment category involved some sort of semantics. There are several existing works about comment categories [2], [3], but we independently made our list of categories that are suitable for local comments. The procedure of making the category list is the following: We reviewed each comment and checked if this comment can be in one of existing categories.…”

Section: Comment Categorymentioning

confidence: 99%

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Analyzing Code Comments to Boost Program Comprehension

Shinyama

Arahori

Gondow

2018

2018 25th Asia-Pacific Software Engineering Conference (APSEC)

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We are trying to find source code comments that help programmers understand a nontrivial part of source code. One of such examples would be explaining to assign a zero as a way to "clear" a buffer. Such comments are invaluable to programmers and identifying them correctly would be of great help. Toward this goal, we developed a method to discover explanatory code comments in a source code. We first propose eleven distinct categories of code comments. We then developed a decision-tree based classifier that can identify explanatory comments with 60% precision and 80% recall. We analyzed 2,000 GitHub projects that are written in two languages: Java and Python. This task is novel in that it focuses on a microscopic comment ("local comment") within a method or function, in contrast to the prior efforts that focused on API-or methodlevel comments. We also investigated how different category of comments is used in different projects. Our key finding is that there are two dominant types of comments: preconditional and postconditional. Our findings also suggest that many English code comments have a certain grammatical structure that are consistent across different projects.

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“…Considering all comments the same may bring to incorrect evaluations especially when code comments are used to perceive the quality of inspected codes, for example, by measuring the code/comment ratio [2,6]. In a recent study, Pascarella et al confirmed this limitation arguing that code comments contribute to different meanings [8]. They proposed a novel taxonomy to classify Java comments, investigated how developers of OSS systems use comments, and experimented with automatically classifying code comments.…”

Section: Introductionmentioning

confidence: 99%

Classifying code comments in Java mobile applications

Pascarella

2018

Proceedings of the 5th International Conference on Mobile Software Engineering and Systems

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Developers adopt code comments for different reasons such as document source codes or change program flows. Due to a variety of use scenarios, code comments may impact on readability and maintainability. In this study, we investigate how developers of 5 open-source mobile applications use code comments to document their projects. Additionally, we evaluate the performance of two machine learning models to automatically classify code comments. Initial results show marginal differences between desktop and mobile applications.

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Classifying Code Comments in Java Open-Source Software Systems

Cited by 81 publications

References 22 publications

Bridging Semantic Gaps between Natural Languages and APIs with Word Embedding

Bridging Semantic Gaps between Natural Languages and APIs with Word Embedding

Analyzing Code Comments to Boost Program Comprehension

Classifying code comments in Java mobile applications

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