Requirements-Driven Automated Software Testing: A Systematic Review

TL;DR

This systematic review analyzes the current state of requirements-driven automated software testing, highlighting prevalent techniques, limitations, and future research directions to improve automation and effectiveness.

Contribution

It provides a comprehensive overview of REDAST research, identifying gaps and offering evidence-based recommendations for advancing automated testing driven by software requirements.

Findings

Functional requirements and model-based specifications are most common.

Rule-based techniques dominate, machine learning approaches are underexplored.

Full automation in REDAST remains rare due to input quality and process dependencies.

Abstract

Automated software testing has significant potential to enhance efficiency and reliability within software development processes. However, its broader adoption faces considerable challenges, particularly concerning alignment between test generation methodologies and software requirements. REquirements-Driven Automated Software Testing (REDAST) addresses this gap by systematically leveraging requirements as the foundation for automated test artifact generation. This systematic literature review (SLR) critically examines the REDAST landscape, analyzing the current state of requirements input formats, transformation techniques, generated test artifacts, evaluation methods, and prevailing limitations. We conducted a thorough analysis of 156 relevant studies selected through a rigorous multi-stage filtering process from an initial collection of 27,333 papers sourced from six major research databases. Our findings highlight the predominance of functional requirements, model-based specifications, and natural language formats. Rule-based techniques are extensively utilized, while machine learning-based approaches remain relatively underexplored. Furthermore, most existing frameworks are sequential and dependent on singular intermediate representations, and while test cases, structured textual formats, and requirements coverage are common, full automation remains rare. We identify significant gaps related to automation completeness and dependency on input quality. This comprehensive synthesis provides a detailed overview of REDAST research and limitations, offering clear, evidence-based recommendations to guide future advancements in automated software testing.