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5. An Exploratory Study of Documentation Strategies for Product Features in Popular GitHub Projects

Author

Puhlfürß, Tim, Montgomery, Lloyd, and Maalej, Walid

Subjects
Software Engineering (cs.SE), FOS: Computer and information sciences, Computer Science - Software Engineering
Abstract

[Background] In large open-source software projects, development knowledge is often fragmented across multiple artefacts and contributors such that individual stakeholders are generally unaware of the full breadth of the product features. However, users want to know what the software is capable of, while contributors need to know where to fix, update, and add features. [Objective] This work aims at understanding how feature knowledge is documented in GitHub projects and how it is linked (if at all) to the source code. [Method] We conducted an in-depth qualitative exploratory content analysis of 25 popular GitHub repositories that provided the documentation artefacts recommended by GitHub's Community Standards indicator. We first extracted strategies used to document software features in textual artefacts and then strategies used to link the feature documentation with source code. [Results] We observed feature documentation in all studied projects in artefacts such as READMEs, wikis, and website resource files. However, the features were often described in an unstructured way. Additionally, tracing techniques to connect feature documentation and source code were rarely used. [Conclusions] Our results suggest a lacking (or a low-prioritised) feature documentation in open-source projects, little use of normalised structures, and a rare explicit referencing to source code. As a result, product feature traceability is likely to be very limited, and maintainability to suffer over time., Comment: Accepted for the New Ideas and Emerging Results (NIER) track of the 38th IEEE International Conference on Software Maintenance and Evolution (ICSME)

Published
2022

18. Automating the Evaluation of Education Apps With App Store Data.

Author

Haering, Marlo, Bano, Muneera, Zowghi, Didar, Kearney, Matthew, and Maalej, Walid

Abstract

With the vast number of apps and the complexity of their features, it is becoming challenging for teachers to select a suitable learning app for their courses. Several evaluation frameworks have been proposed in the literature to assist teachers with this selection. The iPAC framework is a well-established mobile learning framework highlighting the learners’ experience of personalization, authenticity, and collaboration (iPAC). In this article, we introduce an approach to automate the identification and comparison of iPAC relevant apps. We experiment with natural language processing and machine learning techniques, using data from the app description and app reviews publicly available in app stores. We further empirically validate the keyword base of the iPAC framework based on the app users’ language in app reviews. Our approach automatically identifies iPAC relevant apps with promising results (F 1 score ~ 72%) and evaluates them similarly as domain experts (Spearman's rank correlation 0.54). We discuss how our findings can be useful for teachers, students, and app vendors. [ABSTRACT FROM AUTHOR]

Published
2021
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40. Welcome to RSSE 2012

Author

Maalej, Walid, primary, Robillard, Martin, additional, Walker, Robert J., additional, and Zimmermann, Thomas, additional

Published
2012
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43. Foreword

Author

Maalej, Walid, primary and Thurimella, Anil Kumar, additional

Published
2011
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44. Social Software Product Lines

Author

Ali, Raian, primary, Solis, Carlos, additional, Dalpiaz, Fabiano, additional, Maalej, Walid, additional, Giorgini, Paolo, additional, and Nuseibeh, Bashar, additional

Published
2011
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