A Feature Based Methodology for Variable Requirements Reverse Engineering

TL;DR

This paper presents an integrated, feature-based methodology for reverse engineering software requirements, enhancing feature model semantics, pattern recognition, and graph-based slicing to improve understanding and maintenance.

Contribution

It introduces a comprehensive methodology with semantic attributes, feature pattern recognition, and advanced graph slicing, addressing gaps in existing approaches.

Findings

Features are specified uniformly with semantic attributes.

Supports feature pattern recognition and graph-based slicing.

Enhanced slicing criteria enable better requirements maintenance.

Abstract

In the past years, software reverse engineering dealt with source code understanding. Nowadays, it is levered to software requirements abstract level, supported by feature model notations, language independent, and simpler than the source code reading. The recent relevant approaches face the following insufficiencies: lack of a complete integrated methodology, adapted feature model, feature patterns recognition, and Graph based slicing. This work aims to provide some solutions to the above challenges through an integrated methodology. The following results are unique. Elementary and configuration features are specified in a uniform way by introducing semantics specific attributes. The reverse engineering supports feature pattern recognition and requirements feature model graph-based slicing. The slicing criteria are rich enough to allow answering questions of software requirements maintainers. A comparison of this proposed methodology, based on effective criteria, with the similar works, seems to be valuable and competitive: the enrichment of the feature model and feature pattern recognition were never approached and the proposed slicing technique is more general, effective, and practical.