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Locating Clone-and-Own Relationships in Model-Based Industrial Families of Software Products to Encourage Reuse
Author(s) -
Francisca Perez,
Manuel Ballarin,
Raul Lapena,
Carlos Cetina
Publication year - 2018
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2018.2873509
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
Companies often develop similar product variants that share a high degree of functionality (i.e., features) by copying and modifying code (the clone-and-own approach). In an industrial context with a large amount of variants, software reuse can become complex for engineers. Identifying the clone-and-own relationships between the same features in different product variants can encourage reuse (e.g., suggesting improvements on how features are reused or detecting feature reuse impediments). This paper presents our approach to locate the clone-and-own relationships. To do this, our approach proposes an algorithm that combines feature location and code-comparison techniques. We evaluated our approach in three model-based industrial families of two domains (firmware for induction hobs and train control software). In our evaluation, we measure the performance (in terms of precision and recall) and compare our approach with its previous version (baseline), which uses a different technique to compare the code of each feature with its variants. The results show that our approach is able to locate clone-and-own relationships in different domains of real-world environments, and it outperforms the baseline up to 65.37% in terms of precision.

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