On the Challenges of Transforming UVL to IVML

TL;DR

This paper discusses the challenges of transforming variability models between UVL and IVML, and introduces a one-way transformation tool to minimize information loss, aiding better understanding of different modeling approaches.

Contribution

It presents a novel one-way transformation method from UVL to IVML that addresses semantic preservation and reduces information loss.

Findings

Identified key challenges in transforming UVL to IVML.

Developed a transformation approach with minimal information loss.

Facilitated comparison of variability modeling techniques.

Abstract

Software product line techniques encourage the reuse and adaptation of software components for creating customized products or software systems. These different product variants have commonalities and differences, which are managed by variability modeling. Over the past three decades, both academia and industry have developed numerous variability modeling methods, each with its own advantages and disadvantages. Many of these methods have demonstrated their utility within specific domains or applications. However, comprehending the capabilities and differences among these approaches to pinpoint the most suitable one for a particular use case remains challenging. Thus, new modeling techniques and tailored tools for handling variability are frequently created. Transitioning between variability models through transformations from different approaches can help in understanding the benefits and drawbacks of different modeling approaches. However, implementing such transformations presents challenges, such as semantic preservation and avoiding information loss. TRAVART is a tool that helps with transitioning between different approaches by enabling the transformation of variability models into other variability models of different types. This paper discusses the challenges for such transformations between UVL and IVML. It also presents a one-way transformation from the UVL to IVML with as little information loss as possible.