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abstract

Enhancing Product Comparison through Automated Similarity Matching

Published: 13 June 2022 Publication History

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 process 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.

References

[1]
M. Mannion, H. Kaindl, J. Wheadon, B Keepence, 1999. Representing Requirements of Generic Software in an Application Family Model, in Proceedings of the 21st International Conference on Software Engineering (ICSE’99), 453–462.
[2]
M. Mannion, O. Lewis, H. Kaindl, G. Montroni, J. Wheadon, 2000. Representing Requirements of Generic Software in an Application Family Model, in Proceedings of the 6th International Conference on Software Reuse (ICSR-6), Vienna, Austria, LNCS 1844, 153–159.
[3]
M Mannion, H Kaindl, 2008. Using parameters and discriminants for product line requirements Systems Engineering 11, 1 (2008), 61–80.
[4]
H. Kaindl, M. Smialek, W. Nowakowski, 2010. Case-based Reuse with Partial Requirements Specifications, in Proceedings of the 18th IEEE International Requirements Engineering Conference (RE’10), 399–400.
[5]
M Mannion, H Kaindl, 2014. Using Similarity Metrics for Mining Variability from Software Repositories, 2nd Workshop on Reverse Variability Engineering, 18th Int'l Conf on Software Product Line Engineering: Companion Volume for Workshops, Demonstrations and Tools - Vol 2, Florence, Italy, 32–35.
[6]
H. Kaindl, M. Mannion, 2014. A Feature-Similarity Model for Product Line Engineering, in Proceedings of the 14th International Conference on Software Reuse (ICSR 2015), Software Reuse for Dynamic Systems in the Cloud and Beyond, LNCS 8919, 34–41.
[7]
M Mannion, H Kaindl, 2021. Using Binary Strings for Comparing Products from Software-Intensive Systems Product Lines, in Proceedings of the Int'l Conf on Enterprise Information Systems, 257–266.

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EASE '22: Proceedings of the 26th International Conference on Evaluation and Assessment in Software Engineering
June 2022
466 pages
ISBN:9781450396134
DOI:10.1145/3530019
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 June 2022

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Author Tags

  1. Feature reuse
  2. binary strings
  3. product similarity

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EASE 2022

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Overall Acceptance Rate 71 of 232 submissions, 31%

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