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Semi-Automated Feature Traceability with Embedded Annotations

Conference paper
Authors H. Abukwaik
A. Burger
Berima Andam
Thorsten Berger
Published in Proceedings 2018 IEEE International Conference on Software Maintenance and Evolution
ISBN 978-1-5386-7870-1
Publisher IEEE
Publication year 2018
Published at Department of Computer Science and Engineering (GU)
Language en
Links dx.doi.org/10.1109/icsme.2018.00049
Keywords software evolution, clone&own, variability, feature annotations, feature traceability
Subject categories Software Engineering

Abstract

Engineering software amounts to implementing and evolving features. While some engineering approaches advocate the explicit use of features, developers usually do not record feature locations in software artifacts. However, when evolving or maintaining features-especially in long-living or variant-rich software with many developers-the knowledge about features and their locations quickly fades and needs to be recovered. While automated or semi-automated feature-location techniques have been proposed, their accuracy is usually too low to be useful in practice. We propose a semi-automated, machine-learning-assisted feature-traceability technique that allows developers to continuously record feature-traceability information while being supported by recommendations about missed locations. We show the accuracy of our proposed technique in a preliminary evaluation, simulating the engineering of an open-source web-application that evolved in different, cloned variants.

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Denna text är utskriven från följande webbsida:
http://www.gu.se/english/research/publication/?publicationId=274384
Utskriftsdatum: 2019-11-13