| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 453999 | Computer Standards & Interfaces | 2016 | 19 Pages |
•Literature review of AI applications to SPL configuration issues•Develop a taxonomy based on eight different problem domains•This review shows use of logic, constraint satisfaction, reasoning, ontology and optimization.•Several important future research directions are proposed.•We justify advanced analytics and swarm intelligence as better future applications.
A software product line (SPL) is a set of industrial software-intensive systems for configuring similar software products in which personalized feature sets are configured by different business teams. The integration of these feature sets can generate inconsistencies that are typically resolved through manual deliberation. This is a time-consuming process and leads to a potential loss of business resources. Artificial intelligence (AI) techniques can provide the best solution to address this issue autonomously through more efficient configurations, lesser inconsistencies and optimized resources. This paper presents the first literature review of both research and industrial AI applications to SPL configuration issues. Our results reveal only 19 relevant research works which employ traditional AI techniques on small feature sets with no real-life testing or application in industry. We categorize these works in a typology by identifying 8 perspectives of SPL. We also show that only 2 standard industrial SPL tools employ AI in a limited way to resolve inconsistencies. To inject more interest and application in this domain, we motivate and present future research directions. Particularly, using real-world SPL data, we demonstrate how predictive analytics (a state of the art AI technique) can separately model inconsistent and consistent patterns, and then predict inconsistencies in advance to help SPL designers during the configuration of a product.
