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Semantic Software Lab
Concordia University
Montréal, Canada

Assessing the quality factors found in in-line documentation written in natural language: The JavadocMiner

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TitleAssessing the quality factors found in in-line documentation written in natural language: The JavadocMiner
Publication TypeJournal Article
Year of Publication2013
AuthorsKhamis, N., J. Rilling, and R. Witte
Refereed DesignationRefereed
JournalData & Knowledge Engineering
Date Published03/2013
KeywordsAutomated quality analysis, Javadoc Ontology, Source code comments

An important software engineering artefact used by developers and maintainers to assist in software comprehension and maintenance is source code documentation. It provides the insight needed by software engineers when performing a task, and therefore ensuring the quality of this documentation is extremely important. In-line documentation is at the forefront of explaining a programmer’s original intentions for a given implementation. Since this documentation is written in natural language, ensuring its quality so far needed to be performed manually. In this paper, we present an effective and automated approach for assessing the quality of in-line documentation using a set of heuristics, targeting both the quality of language and consistency between source code and its comments. Our evaluation is made up of two parts: We first apply the JavadocMiner tool to the different modules of two open source applications (ArgoUML and Eclipse) in order to automatically assess their intrinsic comment quality. In the second part of our evaluation, we correlate the results returned by the analysis with bug defects reported for the individual modules in order to examine connections between natural language documentation and source code quality.


Copyright © 2013 Elsevier B.V. All rights reserved. NOTICE: this is the author's version of a work that was accepted for publication in Data & Knowledge Engineering. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Data & Knowledge Engineering Volume 87, September 2013, Pages 19–40; DOI#€10.1016/j.datak.2013.02.001

Impact Factor

Impact Factor: 1.519 (2012); 5-Year Impact Factor: 1.710


Received 31 December 2010
Received in revised form 26 September 2011
Accepted 14 February 2013
Available online 13 March 2013
Available print September 2013


This research was partially funded by DRDC Valcartier (contract no. W7701-081745/001/QCV). The authors would like to thank Bahar Sateli for implementing the Semantic Assistants Eclipse plug-in.

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