Mannion, M., & Kaindl, H. (2022). Similarity matching for product comparison. In SPLC ’22: Proceedings of the 26th ACM International Systems and Software Product Line Conference - Volume A (pp. 258–259). https://doi.org/10.1145/3546932.3547023
E384-01 - Forschungsbereich Software-intensive Systems
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Published in:
SPLC '22: Proceedings of the 26th ACM International Systems and Software Product Line Conference - Volume A
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ISBN:
9781450394437
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Volume:
A
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Date (published):
2022
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Event name:
SPLC '22: 26th ACM International Systems and Software Product Line Conference
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Event date:
12-Sep-2022 - 16-Sep-2022
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Event place:
Graz, Austria
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Number of Pages:
2
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Peer reviewed:
Yes
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Keywords:
binary strings; feature reuse; product similarity
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Abstract:
The volume, variety and velocity of products in software-intensive systems product lines is increasing. One challenge is to understand the range of similarity between products. Reasons for product comparison include (i) to decide whether to build a new product or not (ii) to evaluate how products of the same type differ for strategic positioning or branding reasons (iii) to gauge if a product line needs to be reorganized (iv) to assess if a product falls within the national legislative and regulatory boundaries. We will discuss two different approaches to address this challenge. One is grounded in feature modelling, the other in case-based reasoning. We will also describe a specific product comparison approach using similarity matching, in which a product configured from a product line feature model is represented as a weighted binary string, the overall similarity between products is compared using a binary string metric, and the significance of individual feature combinations for product similarity can be explored by modifying the weights. We will illustrate our ideas with a mobile phone example, and discuss some of the benefits and limitations of this approach.
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Research Areas:
Computer Engineering and Software-Intensive Systems: 100%