Fault analysis and debugging of microservice systems: Industrial survey, benchmark system, and empirical study

• Main Authors: ZHOU, Xiang, PENG, Xin, XIE, Tao, SUN, Jun, JI, Chao, LI, Wenhai, DING, Dan
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2021
• Subjects: microservices; fault localization; tracing; visualization; debugging; Computer and Systems Architecture; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/4845; https://ink.library.smu.edu.sg/context/sis_research/article/5848/viewcontent/fault_analysis__PV.pdf
• Institution: Singapore Management University

The complexity and dynamism of microservice systems pose unique challenges to a variety of software engineering tasks such as fault analysis and debugging. In spite of the prevalence and importance of microservices in industry, there is limited research on the fault analysis and debugging of microservice systems. To fill this gap, we conduct an industrial survey to learn typical faults of microservice systems, current practice of debugging, and the challenges faced by developers in practice. We then develop a medium-size benchmark microservice system (being the largest and most complex open source microservice system within our knowledge) and replicate 22 industrial fault cases on it. Based on the benchmark system and the replicated fault cases, we conduct an empirical study to investigate the effectiveness of existing industrial debugging practices and whether they can be further improved by introducing the state-of-the-art tracing and visualization techniques for distributed systems. The results show that the current industrial practices of microservice debugging can be improved by employing proper tracing and visualization techniques and strategies. Our findings also suggest that there is a strong need for more intelligent trace analysis and visualization, e.g., by combining trace visualization and improved fault localization, and employing data-driven and learning-based recommendation for guided visual exploration and comparison of traces.