Abstract
In practice, many organizations rely on cloning to implement customer-specific variants of a system. While this approach can have several disadvantages, organizations fear to extract reusable features later on, due to the corresponding efforts and risks. A particularly challenging and poorly supported task is to decide which features to extract. To tackle this problem, we aim to develop a recommender system that proposes suitable features based on automated analyses of the cloned legacy systems. In this paper, we sketch this recommender and its empirically derived metrics, which comprise cohesion, impact, and costs of features as well as the users’ previous decisions. Overall, we will facilitate the adoption of systematic reuse based on an integrated platform.
Original language | English |
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Title of host publication | International Conference on Software Engineering Companion (ICSE-C) |
Publisher | Association for Computing Machinery, Inc |
Pages | 518-520 |
Number of pages | 3 |
DOIs | |
Publication status | Published - 2018 |
Bibliographical note
DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.Keywords
- Software Product Line
- Extractive Approach
- Software Maintenance