Detection Software Content Failures using Dynamic Execution Information

• Published in: 2021 IEEE 21st International Conference on Software Quality, Reliability and Security Companion (QRS-C) pp. 141 - 147
• Main Authors: Kong, Shiyi, Lu, Minyan, Sun, Bo, Ai, Jun, Wang, Shuguang
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.12.2021
• Subjects: Data models; dynamic binary instrumentation; Machine learning; Runtime; runtime execution infor-mation; Software; software failure detection; Software quality; software runtime entropy; Software systems; Training
• ISSN: 2693-9371
• DOI: 10.1109/QRS-C55045.2021.00029

Modern software systems become more and more complex, which makes them difficult to test and validate. Detecting software partial failures in complex systems at runtime assist to handle software unintended behaviors, avoiding catastrophic software failures and improving software runtime availability. These detection techniques aim to find the manifestation of faults before they finally lead to unavoidable failures, thus supporting following runtime fault-tolerant techniques. We review the state-of-the-art articles and find that the content failures account for the majority of all kinds of software failures, but its detection methods are rarely studied. In this work, we propose a novel failure detection indicator based on the software runtime dynamic execution information for software content failures. The runtime information is recorded during software execution, then transformed to a measure named runtime entropy and finally fed into decision tree models. The machine-learning models are built to classify the intended and unintended behaviors of the objected software systems. A series of controlled experiments on several open-source projects are conducted to prove the feasibility of the method. We also evaluate the accuracy of machine-learning models built in this work.