Automatic Quality Assessment of Source Code Comments: The JavadocMiner

Khamis, Ninus; Witte, René; Rilling, Juergen

doi:10.1007/978-3-642-13881-2_7

Cited by 69 publications

(67 citation statements)

References 6 publications

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“…This paradigm shift leads to situations where the major documentation, such as software requirements specifications or design and implementation decisions, are only available in form of source code comments. Therefore, the quality of this documentation becomes increasingly important for developers attempting to perform the various software engineering and maintenance tasks [4].…”

Section: Software Engineering Backgroundmentioning

confidence: 99%

“…The goal of our JavadocMiner tool [4] is to enable users to automatically assess the quality of source code comments. The JavadocMiner is also capable of providing users with recommendations on how a Javadoc comment may be improved based Fig.…”

Section: Quality Analysis Of Source Code Commentsmentioning

confidence: 99%

“…(2) The readability metrics [4] detected the Javadoc comment as being below the Flesch threshold FLESCHMetric and FleschExplanation, and above the Fog threshold FOGMetric and FOGExplanation, which indicates a comment that exceeds the readability thresholds set by the user. (3) Because the comment does not use a third person writing style as stated in guideline (1), the JavadocMiner generates a recommendation MethodCommentStyle that explains the steps needed in order for the comment to adhere to the Javadoc guidelines.…”

Section: Quality Analysis Of Source Code Commentsmentioning

confidence: 99%

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Intelligent Software Development Environments: Integrating Natural Language Processing with the Eclipse Platform

Witte

Sateli

Khamis

et al. 2011

Advances in Artificial Intelligence

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Abstract. Software engineers need to be able to create, modify, and analyze knowledge stored in software artifacts. A significant amount of these artifacts contain natural language, like version control commit messages, source code comments, or bug reports. Integrated software development environments (IDEs) are widely used, but they are only concerned with structured software artifacts -they do not offer support for analyzing unstructured natural language and relating this knowledge with the source code. We present an integration of natural language processing capabilities into the Eclipse framework, a widely used software IDE. It allows to execute NLP analysis pipelines through the Semantic Assistants framework, a service-oriented architecture for brokering NLP services based on GATE. We demonstrate a number of semantic analysis services helpful in software engineering tasks, and evaluate one task in detail, the quality analysis of source code comments.

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Section: Software Engineering Backgroundmentioning

confidence: 99%

Section: Quality Analysis Of Source Code Commentsmentioning

confidence: 99%

Section: Quality Analysis Of Source Code Commentsmentioning

confidence: 99%

See 1 more Smart Citation

Intelligent Software Development Environments: Integrating Natural Language Processing with the Eclipse Platform

Witte

Sateli

Khamis

et al. 2011

Advances in Artificial Intelligence

Self Cite

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show abstract

“…Siy et al similarly found that a non-trivial portion of the issues that arise during industrial code inspection concern the quality of documentation [57]. Recently, there has been preliminary work examining both the completeness of comments and the consistency of comment updates with respect to future defects [32,41,55]. In all cases, low-quality and inconsistently updated comments correlated with higher defect density.…”

Section: Ensuring Documentation Completeness and Consistencymentioning

confidence: 99%

“…[4,15,20,23,30,31,45,47,53,71]) and to ensure long-term system understandability and evolvability (e.g. [11,12,13,41,55,58,59]). Despite these advancements, developers still struggle to handle the volume of maintenance tasks that arise in practice [31].…”

Section: Introductionmentioning

confidence: 99%

Leveraging Light-Weight Analyses to Aid Software Maintenance

Fry

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Software maintenance can account for up to 90% of a system's life cycle cost. As a result, many automated techniques have been developed to reduce the overall effort necessary to sustain software over time. While many of these tools work well under certain circumstances, we believe that they could be improved by taking advantage of large untapped sources of unstructured information that result from natural software development. As the size and complexity of systems increases, taking advantage of such previously-overlooked information is of paramount importance.The proposed research will design lightweight analyses to extract latent information encoded by humans in software development artifacts and thereby reduce the costs of software maintenance. We will design analyses that apply throughout the maintenance process, focusing on three areas: (1) triaging large collections of exposed defects; (2) guiding the search for automatic defect repairs; and (3) ensuring the continued consistency of system documentation over time.For each area of proposed research we aim to improve upon existing approaches to reduce the total cost of software maintenance while requiring minimal additional developer effort. We will evaluate the proposed work using these criteria on a diverse set of real world programs totaling millions of lines of code to concretely show the reduction of maintenance cost and improvement upon existing processes.

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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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Automatic Quality Assessment of Source Code Comments: The JavadocMiner

Cited by 69 publications

References 6 publications

Intelligent Software Development Environments: Integrating Natural Language Processing with the Eclipse Platform

Intelligent Software Development Environments: Integrating Natural Language Processing with the Eclipse Platform

Leveraging Light-Weight Analyses to Aid Software Maintenance

Does your code need comment?

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