Comprehensibility of Variability in Model Fragments for Product Configuration

Abstract

The ability to manage variability in software has become crucial to overcome the complexity and variety of systems. To this end, a comprehensible representation of variability is important. Nevertheless, in previous works, difficulties have been detected to understand variability in an industrial environment. Specifically, domain experts had difficulty understanding variability in model fragments to produce the software for their products. Hence, the aim of this paper is to further investigate these difficulties by conducting an experiment in which participants deal with variability in order to achieve their desired product configurations. Our results show new insights into product configuration which suggest next steps to improve general variability modeling approaches, and therefore promoting the adoption of these approaches in industry.

Evaluating the benefits of empowering model‐driven development with a machine learning classifier

“…Unfortunately, since the subjects are students instead of development engineers, this work suffers from this threat. However, using students as subjects instead of software engineers is not a major issue as long as the research questions are not specifically focused on experts 35‐37 . Nevertheless, it would be necessary to replicate the experiment with different subject roles to mitigate this threat. Interaction of history and treatment: This threat appears when the experiment is conducted at a special time or day which affects the results.…”

“…However, using students as subjects instead of software engineers is not a major issue as long as the research questions are not specifically focused on experts. [35][36][37] Nevertheless, it would be necessary to replicate the experiment with different subject roles to mitigate this threat.…”