A Test Case Generation Scheduling Model Based on Running Event Interval Characteristics

System testing mainly focuses on finding bugs before online deployment. However, the system’s complexity increases due to a surge in the number of users, which makes scheduling the test case generation time more difficult, impeding testing fidelity and automatic testing. In this paper, we propose a data-driven framework for scheduling the test cases and improving testing fidelity. The developed framework involves two stages: 1) mining the running event interval characteristics from logs obtained from the actual environment in the first stage, and 2) utilizing those characteristics to develop a model for the test case generation time schedule in the second stage. First, we select the running event interval, which is the interval between two adjacent running events processed by the system, as the feature for analysis. Then, we analyze the interval’s distribution characteristics, observing that it adheres to the power-law distribution from multiple aspects. Second, based on the distribution characteristics obtained, we develop a composite event chain with associated relationships (CECAR) model to schedule test case generation time. Finally, we conduct a series of experiments using logs obtained from eight regions. The results show that, compared to baseline methods, the CECAR model reduces average errors in the generated quantity, power exponent, and specific granularity intervals by approximately 1% to 9% across multiple layers, demonstrating the effectiveness of the proposed method. We also made our dataset and code publicly available at https://github.com/yizhenli-xjtu/CECAR.
Note to Practitioners
—System testing is a promising way to eliminate bugs and improve system stability before online deployment. If the testing method does not align with the system’s running characteristics in the actual environment, the testing fidelity and efficiency may be greatly reduced. Scheduling the test case generation time is one of the fundamental ways to address this challenge. The CECAR model developed in this paper can reproduce temporal characteristics and incorporate the concurrent relationships between different events, in turn, achieving the goal of fidelity improvement. Furthermore, based on the CECAR model, we can simulate arbitrary scenarios by combining different types of devices, which can greatly improve the testing’s flexibility and practicality.