Extracting Interaction-Related Failure Indicators for Online Detection and Prediction of Content Failures

With the increasing complexity of software-intensive systems, software health management is proposed to assure their runtime dependability, in which online failure detection and prediction is one of the most significant components. Failure indicators are characteristics of internal states and behavior of a system which indicate potential failures. However, previous studies mostly focused on extracting failure indicators from network and hardware outside of a software system or operating system level, neglected the runtime dynamics on application level. Besides, most of these studies aimed at detecting and predicting performance-related failures. As a major category of software failures, content failures are often omitted. This paper proposes an experiment-based approach to extract interaction-related failure indicators on application level for content failures, composed of abnormal execution time of modules and abnormal interaction times between modules. Firstly, an experiment-based failure data generation method is proposed due to a lack of real-world failure data which can reflect the runtime states and behavior of a software system. Then a machine learning method is selected and applied on the failure dataset to construct classifiers for normal data and failure data, from which failure indicators are extracted. Finally, three open-source software were selected to show the validity of our extracting method and the effectiveness of the extracted failure indicators. Interaction-related failure indicators extracted by the proposed approach can be used for runtime failure detection and prediction of content failures, thus improving runtime dependability of complex software-intensive systems.