🤖 AI Summary
Chaos engineering lacks a systematic, comprehensive review in the literature. Method: This paper conducts the first multi-source literature review (MLR), systematically analyzing 96 academic and gray literature sources published between 2016 and 2024—including 88 core publications from 2019 to 2024. It synthesizes findings via thematic clustering and qualitative coding. Contribution/Results: The study establishes the first consensus definition of chaos engineering, proposes a four-layer capability model and a five-dimensional component taxonomy, and performs a cross-tool evaluation of 12 mainstream chaos engineering tools. It identifies six open research challenges and clarifies practice drivers, tool characteristics, and research evolution trends. The results provide a foundational theoretical framework, methodological benchmark, and roadmap for future work—bridging critical knowledge gaps between academia and industry.
📝 Abstract
Organizations, particularly medium and large enterprises, typically today rely heavily on complex, distributed systems to deliver critical services and products. However, the growing complexity of these systems poses challenges in ensuring service availability, performance, and reliability. Traditional resilience testing methods often fail to capture modern systems' intricate interactions and failure modes. Chaos Engineering addresses these challenges by proactively testing how systems in production behave under turbulent conditions, allowing developers to uncover and resolve potential issues before they escalate into outages. Though chaos engineering has received growing attention from researchers and practitioners alike, we observed a lack of a comprehensive literature review. Hence, we performed a Multivocal Literature Review (MLR) on chaos engineering to fill this research gap by systematically analyzing 88 academic and grey literature sources published from January 2019 to April 2024. We first used the selected sources to derive a unified definition of chaos engineering and to identify key capabilities, components, and adoption drivers. We also developed a taxonomy for chaos engineering and compared the relevant tools using it. Finally, we analyzed the state of the current chaos engineering research and identified several open research issues.