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
-
Erschienen in:
SPLC '22: Proceedings of the 26th ACM International Systems and Software Product Line Conference - Volume A
-
ISBN:
9781450394437
-
Band:
A
-
Datum (veröffentlicht):
2022
-
Veranstaltungsname:
SPLC '22: 26th ACM International Systems and Software Product Line Conference
en
Veranstaltungszeitraum:
12-Sep-2022 - 16-Sep-2022
-
Veranstaltungsort:
Graz, Österreich
-
Umfang:
2
-
Peer Reviewed:
Ja
-
Keywords:
binary strings; feature reuse; product similarity
en
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.
en
Forschungsschwerpunkte:
Computer Engineering and Software-Intensive Systems: 100%