Using binary strings for comparing products from software-intensive systems product lines

Mike Mannion; Hermann Kaindl

doi:10.5220/0010441002570266

Using binary strings for comparing products from software-intensive systems product lines

Mike Mannion, Hermann Kaindl

School of Computing, Engineering and Built Environment

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Citations (Scopus)

47 Downloads (Pure)

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 to evaluate its impact on product line management. This paper contributes to product line management by presenting a product similarity evaluation process in which (i) a product configured from a product line feature model is represented as a weighted binary string (ii) the overall similarity between products is compared using the Jaccard Coefficient similarity metric (iii) the significance of individual features and feature combinations to product similarity is explored by modifying the weights. We propose a method for automatically allocating weights to features depending on their position in a product line feature model, although we do not claim that this allocation method nor the use of the Jaccard Coefficient is optimal. We illustrate our ideas with mobile phone worked examples.

Original language	English
Title of host publication	Proceedings of 23rd International Conference on Enterprise Information Systems
Editors	Joaquim Filipe, Michal Smialek, Alexander Brodsky, Slimane Hammoudi
Place of Publication	Prague
Publisher	SciTePress
Pages	257-266
Number of pages	10
Volume	2
ISBN (Electronic)	9789897585098
ISBN (Print)	9789897585098
DOIs	https://doi.org/10.5220/0010441002570266
Publication status	Published - 28 Apr 2021
Event	23rd International Conference on Enterprise Information Systems - Online Duration: 26 Apr 2021 → 28 Apr 2021 https://iceis.scitevents.org/?y=2021 (Link to conference website)

Publication series

Name
ISSN (Print)	2184-4992

Conference

Conference	23rd International Conference on Enterprise Information Systems
Abbreviated title	ICEIS 2021
Period	26/04/21 → 28/04/21
Internet address	https://iceis.scitevents.org/?y=2021 (Link to conference website)

Keywords

product line, string similarity, metrics
Metrics
String Similarity
Product Line

ASJC Scopus subject areas

Information Systems
Information Systems and Management
Computer Science Applications

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Documents and Links

10.5220/0010441002570266

Mannion, M. & Kaindl, H. (2021) Using binary strings for comparing products from software-intensive systems product lines
© 2021 SciTePress. This is the accepted author manuscript of the paper: Mannion, M. and Kaindl, H. (2021). Using Binary Strings for Comparing Products from Software-intensive Systems Product Lines. In Proceedings of the 23rd International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-758-509-8 ISSN 2184-4992, pages 257-266. DOI: 10.5220/0010441002570266
Author accepted manuscript, 261 KB

Cite this

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title = "Using binary strings for comparing products from software-intensive systems product lines",

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 to evaluate its impact on product line management. This paper contributes to product line management by presenting a product similarity evaluation process in which (i) a product configured from a product line feature model is represented as a weighted binary string (ii) the overall similarity between products is compared using the Jaccard Coefficient similarity metric (iii) the significance of individual features and feature combinations to product similarity is explored by modifying the weights. We propose a method for automatically allocating weights to features depending on their position in a product line feature model, although we do not claim that this allocation method nor the use of the Jaccard Coefficient is optimal. We illustrate our ideas with mobile phone worked examples.",

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author = "Mike Mannion and Hermann Kaindl",

note = "Acceptance in SharePoint (note of date from author). ST 13/07/21 Unknown publication date – used last date of conference. ST 07/07/21 ISSN listed on paper webpage: 2184-4992. Unable to locate series title/pub info. ST 25/06/21 Paper published with SCITEPRESS. Webpage shows CC-BY-NC-ND 4.0 however login required to access paper. ST 25/06/21 ^Added in ISSN - appears to be for this conference across different years. ET 28/6/21 ^^Monitor for AAM - treat as 'unknown publisher policy - upload AAM' re access issue of VoR. ET 28/6/21 - Unknown publisher policy - made file open and contacted publisher. ST 07/07/21 ^ Publisher confirmed ok, email in mailbox (amended citation as directed). ST 12/07/21; 23rd International Conference on Enterprise Information Systems, ICEIS 2021 ; Conference date: 26-04-2021 Through 28-04-2021",

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month = apr,

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language = "English",

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editor = "Joaquim Filipe and Michal Smialek and Alexander Brodsky and Slimane Hammoudi",

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Mannion, M & Kaindl, H 2021, Using binary strings for comparing products from software-intensive systems product lines. in J Filipe, M Smialek, A Brodsky & S Hammoudi (eds), Proceedings of 23rd International Conference on Enterprise Information Systems . vol. 2, SciTePress, Prague, pp. 257-266, 23rd International Conference on Enterprise Information Systems, 26/04/21. https://doi.org/10.5220/0010441002570266

Using binary strings for comparing products from software-intensive systems product lines. / Mannion, Mike; Kaindl, Hermann.

Proceedings of 23rd International Conference on Enterprise Information Systems . ed. / Joaquim Filipe; Michal Smialek; Alexander Brodsky; Slimane Hammoudi. Vol. 2 Prague : SciTePress, 2021. p. 257-266.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - Using binary strings for comparing products from software-intensive systems product lines

AU - Mannion, Mike

AU - Kaindl, Hermann

N1 - Acceptance in SharePoint (note of date from author). ST 13/07/21 Unknown publication date – used last date of conference. ST 07/07/21 ISSN listed on paper webpage: 2184-4992. Unable to locate series title/pub info. ST 25/06/21 Paper published with SCITEPRESS. Webpage shows CC-BY-NC-ND 4.0 however login required to access paper. ST 25/06/21 ^Added in ISSN - appears to be for this conference across different years. ET 28/6/21 ^^Monitor for AAM - treat as 'unknown publisher policy - upload AAM' re access issue of VoR. ET 28/6/21 - Unknown publisher policy - made file open and contacted publisher. ST 07/07/21 ^ Publisher confirmed ok, email in mailbox (amended citation as directed). ST 12/07/21

PY - 2021/4/28

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N2 - 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 to evaluate its impact on product line management. This paper contributes to product line management by presenting a product similarity evaluation process in which (i) a product configured from a product line feature model is represented as a weighted binary string (ii) the overall similarity between products is compared using the Jaccard Coefficient similarity metric (iii) the significance of individual features and feature combinations to product similarity is explored by modifying the weights. We propose a method for automatically allocating weights to features depending on their position in a product line feature model, although we do not claim that this allocation method nor the use of the Jaccard Coefficient is optimal. We illustrate our ideas with mobile phone worked examples.

AB - 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 to evaluate its impact on product line management. This paper contributes to product line management by presenting a product similarity evaluation process in which (i) a product configured from a product line feature model is represented as a weighted binary string (ii) the overall similarity between products is compared using the Jaccard Coefficient similarity metric (iii) the significance of individual features and feature combinations to product similarity is explored by modifying the weights. We propose a method for automatically allocating weights to features depending on their position in a product line feature model, although we do not claim that this allocation method nor the use of the Jaccard Coefficient is optimal. We illustrate our ideas with mobile phone worked examples.

KW - product line, string similarity, metrics

KW - Metrics

KW - String Similarity

KW - Product Line

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DO - 10.5220/0010441002570266

M3 - Conference contribution

SN - 9789897585098

VL - 2

SP - 257

EP - 266

BT - Proceedings of 23rd International Conference on Enterprise Information Systems

A2 - Filipe, Joaquim

A2 - Smialek, Michal

A2 - Brodsky, Alexander

A2 - Hammoudi, Slimane

PB - SciTePress

CY - Prague

T2 - 23rd International Conference on Enterprise Information Systems

Y2 - 26 April 2021 through 28 April 2021

ER -

Using binary strings for comparing products from software-intensive systems product lines

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

UN SDGs

Documents and Links

Fingerprint

Cite this