Requirements variability specification for data intensive software
This work addresses the problem of specifying and managing requirements variability in data-intensive software for developers and engineers, but it appears incremental as it builds upon existing feature modeling techniques.
The paper tackles the difficulty of managing data variability in Data Intensive Software Product Lines (DISPLs) by proposing a bio-inspired methodology based on genetics, which enriches feature models with new features and relations to facilitate requirements variation management.
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.