Requirements-Driven Automated Software Testing: A Systematic Review

2025-02-25

AI Summary

Automated software testing suffers from insufficient alignment between test generation and requirements. This paper presents the first systematic literature review (SLR) on Requirement-Driven Automated Software Testing (REDAST), synthesizing 156 studies retrieved from ACM Digital Library, IEEE Xplore, and other major repositories. Applying thematic coding and cross-dimensional analysis, we construct the first comprehensive REDAST framework, clarifying requirements representation formats, abstraction-level mapping patterns, and critical gaps in evaluation methodologies. We categorize requirements formalisms—including natural language, UML, and SysML—and map them to underlying technical approaches such as model checking, NLP-based test generation, and constraint solving. Seven recurring bottlenecks are identified (e.g., poor scalability, high sensitivity to input quality), and four industrially viable evolutionary directions are proposed. The study establishes a structured benchmark and roadmap to advance both the theoretical foundations and practical adoption of REDAST.

Technology Category

• Knowledge Representation and Reasoning: Automated Reasoning and Theorem Proving
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Automated software testing has the potential to enhance efficiency and reliability in software development, yet its adoption remains hindered by challenges in aligning test generation with software requirements. REquirements-Driven Automated Software Testing (REDAST) aims to bridge this gap by leveraging requirements as the foundation for automated test artifact generation. This systematic literature review (SLR) explores the landscape of REDAST by analyzing requirements input, transformation techniques, test outcomes, evaluation methods, and existing limitations. We conducted a comprehensive review of 156 papers selected from six major research databases. Our findings reveal the predominant types, formats, and notations used for requirements in REDAST, the automation techniques employed for generating test artifacts from requirements, and the abstraction levels of resulting test cases. Furthermore, we evaluate the effectiveness of various testing frameworks and identify key challenges such as scalability, automation gaps, and dependency on input quality. This study synthesizes the current state of REDAST research, highlights trends, and proposes future directions, serving as a reference for researchers and practitioners aiming to advance automated software testing.

Problem

• Aligning test generation with software requirements
• Automating test artifact generation from requirements
• Evaluating effectiveness of testing frameworks

Innovation

• Automated test artifact generation from requirements
• Systematic literature review of REDAST techniques
• Evaluation of testing frameworks effectiveness and challenges