Early-Stage Requirements Transformation Approaches: A Systematic Review
Keletso J. Letsholo

TL;DR
This systematic review analyzes early-stage requirements transformation approaches, highlighting the prevalent use of NLP techniques, common challenges like incomplete models and lack of robust evaluation, and the need for improved transparency and traceability in the field.
Contribution
It provides a comprehensive overview of 25 studies from 2000 to 2014, identifying key techniques, challenges, and gaps in early requirements transformation methods.
Findings
Widespread use of NLP tools like Stanford parser and WordNet.
Most approaches produce incomplete models with missing elements.
Traceability in requirements transformation is largely neglected.
Abstract
Transformation approaches for automatically constructing analysis models from textual requirements are critical to software development, as they can bring forward the use of precise formal languages from the coding phase to the requirement analysis phase in the software development life-cycle. Over the decades, numerous transformation approaches have been developed in an attempt to fully or partially automate this initial phase. This systematic review examines transformation approaches in the early stages of software development, examining 25 studies on early-stage requirements transformation documented between 2000 and 2014. The review highlights the widespread use of natural language processing techniques, with tools like the Stanford parser and WordNet being essential. Intermediate models are often used in the transformation process to bridge the gap between textual requirements and…
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Taxonomy
TopicsSoftware Engineering Techniques and Practices · Software Reliability and Analysis Research · Advanced Software Engineering Methodologies
