A taxonomy of risk-based testing
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Abstract (arXiv)
Software testing has often to be done under severe pressure due to limited resources and a challenging time schedule facing the demand to assure the fulfillment of the software requirements. In addition, testing should unveil those software defects that harm the mission-critical functions of the software. Risk-based testing uses risk (re-)assessments to steer all phases of the test process in order to optimize testing efforts and limit risks of the software-based system. Due to its importance and high practical relevance several risk-based testing approaches were proposed in academia and industry. This paper presents a taxonomy of risk-based testing providing a framework to understand, categorize, assess, and compare risk-based testing approaches to support their selection and tailoring for specific purposes. The taxonomy is aligned with the consideration of risks in all phases of the test process and consists of the top-level classes risk drivers, risk assessment, and risk-based test process. The taxonomy of risk-based testing has been developed by analyzing the work presented in available publications on risk-based testing. Afterwards, it has been applied to the work on risk-based testing presented in this special section of the International Journal on Software Tools for Technology Transfer.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Dec 2019.
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Citing papers per year: 2022: 1; 2023: 0; 2024: 1; 2025: 1.
3 papers.
- 2024Retrieval-Augmented Test Generation: How Far Are We?cited by 7
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- 2025Navigating the growing field of research on AI for software testing -- the taxonomy for AI-augmented software testing and an ontology-driven literature surveycited by 2
- 2025Navigating the growing field of research on AI for software testing -- the taxonomy for AI-augmented software testing and an ontology-driven literature survey17 Jun 2025
- 2024Retrieval-Augmented Test Generation: How Far Are We?19 Sep 2024
- 2022A Taxonomy of Information Attributes for Test Case Prioritisation: Applicability, Machine Learning16 Jan 2022