A systematic differential analysis for fast and robust detection of software aging
Software systems running continuously for a long time often confront software aging, which is the phenomenon of progressive degradation of execution environment caused by latent software faults. Removal of such faults in software development process is a crucial issue for system reliability. A known major obstacle is typically the large latency to discover the existence of software aging. We propose a systematic approach to detect software aging which has shorter test time and higher accuracy compared to traditional aging detection via stress testing and trend detection. The approach is based on a differential analysis where a software version under test is compared against a previous version in terms of behavioral changes of resource metrics. A key instrument adopted is a divergence chart, which expresses time-dependent differences between two signals. Our experimental study focuses on memory-leak detection and evaluates divergence charts computed using multiple statistical techniques paired with application-level memory related metrics (RSS and Heap Usage). The results show that the proposed method achieves good performance for memory-leak detection in comparison to techniques widely adopted in previous works (e.g., linear regression, moving average and median).
