Predictive Analytics for Product Configurations in Software Product Lines

Authors

Uzma Afzal¹, Tariq Mahmood², Raihan ur Rasool^3, , Ayaz H. Khan^{4, *,} , Rehan Ullah Khan^5, , Ali Mustafa Qamar⁶

¹Computer Science Department, Federal Urdu University of Arts, Science and Technology, Karachi, Pakistan

²Computer Science Department, Institute of Business Administration, Karachi, Pakistan

³Centre for Applied Informatics, Institute for Sustainable Industries & Liveable Cities, Engineering and Science, Victoria University, Melbourne, Australia

⁴Computer Science Department, Habib University, Karachi, Pakistan

⁵Department of Information Technology, College of Computer, Qassim University, Buraydah, Saudi Arabia

⁶Department of Computer Science, College of Computer, Qassim University, Buraydah, Saudi Arabia

^*Corresponding author. Email: ayazhk@gmail.com; ayaz.hassan@sse.habib.edu.pk

DOI

10.2991/ijcis.d.210620.003How to use a DOI?

Keywords

Software product line; Predictive analytics; Data science; Feature model; Inconsistency; Information system

Abstract

A Software Product Line (SPL) is a collection of software for configuring software products in which sets of features are configured by different teams of product developers. This process often leads to inconsistencies (or dissatisfaction of constraints) in the resulting product configurations, whose resolution consumes considerable business resources. In this paper, we aim to solve this problem by learning, or mathematically modeling, all previous patterns of feature selection by SPL developers, and then use these patterns to predict inconsistent configuration patterns at runtime. We propose and implement an informative Predictive Analytics tool called predictive Software Product LIne Tool (p-SPLIT) which provides runtime decision support to SPL developers in three ways: 1) by identifying configurations of feature selections (patterns) that lead to inconsistent product configurations, 2) by identifying feature selection patterns that lead to consistent product configurations, and 3) by predicting feature inconsistencies in the product that is currently being configured (at runtime). p-SPLIT provides the first application of Predictive Analytics for the SPL feature modeling domain at the application engineering level. With different experiments in representative SPL settings, we obtained 85% predictive accuracy for p-SPLIT and a 98% Area Under the Curve (AUC) score. We also obtained subjective feedback from the practitioners who validate the usability of p-SPLIT in providing runtime decision support to SPL developers. Our results prove that p-SPLIT technology is a potential addition for the global SPL product configuration community, and we further validate this by comparing p-SPLIT's characteristics with state-of-the-art SPL development solutions.

Copyright

© 2021 The Authors. Published by Atlantis Press B.V.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)
View full text (HTML)

TY  - JOUR
AU  - Uzma Afzal
AU  - Tariq Mahmood
AU  - Raihan ur Rasool
AU  - Ayaz H. Khan
AU  - Rehan Ullah Khan
AU  - Ali Mustafa Qamar
PY  - 2021
DA  - 2021/06/28
TI  - Predictive Analytics for Product Configurations in Software Product Lines
JO  - International Journal of Computational Intelligence Systems
SP  - 1880
EP  - 1894
VL  - 14
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.210620.003
DO  - 10.2991/ijcis.d.210620.003
ID  - Afzal2021
ER  -