Extracting features from online software reviews to aid requirements reuse
Sets of common features are essential assets to be reused in fulfilling specific needs in software product line methodology. In Requirements Reuse (RR), the extraction of software features from Software Requirement Specifications (SRS) is viable only to practitioners who have access to these softwar...
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| Main Authors: | , , , |
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| Format: | Article |
| Published: |
Elsevier
2016
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| Subjects: | |
| Online Access: | http://eprints.um.edu.my/18443/ https://doi.org/10.1016/j.asoc.2016.07.048 |
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| Institution: | Universiti Malaya |
| Summary: | Sets of common features are essential assets to be reused in fulfilling specific needs in software product line methodology. In Requirements Reuse (RR), the extraction of software features from Software Requirement Specifications (SRS) is viable only to practitioners who have access to these software artefacts. Due to organisational privacy, SRS are always kept confidential and not easily available to the public. As alternatives, researchers opted to use the publicly available software descriptions such as product brochures and online software descriptions to identify potential software features to initiate the RR process. The aim of this paper is to propose a semi-automated approach, known as Feature Extraction for Reuse of Natural Language requirements (FENL), to extract phrases that can represent software features from software reviews in the absence of SRS as a way to initiate the RR process. FENL is composed of four stages, which depend on keyword occurrences from several combinations of nouns, verbs, and/or adjectives. In the experiment conducted, phrases that could reflect software features, which reside within online software reviews were extracted by utilising the techniques from information retrieval (IR) area. As a way to demonstrate the feature groupings phase, a semi-automated approach to group the extracted features were then conducted with the assistance of a modified word overlap algorithm. As for the evaluation, the proposed extraction approach is evaluated through experiments against the truth data set created manually. The performance results obtained from the feature extraction phase indicates that the proposed approach performed comparably with related works in terms of recall, precision, and F-Measure. |
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