Requirements variability specification for data intensive software

TL;DR

This paper introduces a genetics-inspired methodology for specifying requirements variability in Data Intensive Software Product Lines, enhancing traditional feature modeling with biologically inspired concepts to better manage data variability.

Contribution

It presents a novel bio-inspired approach that enriches feature models for data variability, addressing limitations of existing models in DISPLs requirements specification.

Findings

Enhanced feature models with genetic features and relations.

Improved management of requirements variability in DISPLs.

Promising results in applying bio-inspired methods to data requirements.

Abstract

Nowadays, the use of feature modeling technique, in software requirements specification, increased the variation support in Data Intensive Software Product Lines (DISPLs) requirements modeling. It is considered the easiest and the most efficient way to express commonalities and variability among different products requirements. Several recent works, in DISPLs requirements, handled data variability by different models which are far from real world concepts. This,leaded to difficulties in analyzing, designing, implementing, and maintaining this variability. However, this work proposes a software requirements specification methodology based on concepts more close to the nature and which are inspired from genetics. This bio-inspiration has carried out important results in DISPLs requirements variability specification with feature modeling, which were not approached by the conventional approaches.The feature model was enriched with features and relations, facilitating the requirements variation management, not yet considered in the current relevant works.The use of genetics-based methodology seems to be promising in data intensive software requirements variability specification.