A Systematic Differential Analysis for Fast and Robust Detection of Software Aging
A Systematic Differential Analysis for Fast and Robust Detection of Software Aging
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DOI：
10.1109/srds.2014.38
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发表时间：
2014-10
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通讯作者：
Rivalino Matias;A. Andrzejak;F. Machida;Diego Elias;Kishor S. Trivedi
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作者：
Rivalino Matias;A. Andrzejak;F. Machida;Diego Elias;Kishor S. Trivedi
长时间连续运行的软件系统经常会遇到软件老化，这是由于潜在的软件故障而导致执行环境逐渐退化的现象。软件开发过程中消除此类故障是系统可靠性的关键问题。一个已知的主要障碍通常是发现软件老化存在的较长延迟。我们提出了一种系统的软件老化检测方法，与通过压力测试和趋势检测的传统老化检测相比，该方法具有更短的测试时间和更高的准确度。该方法基于比较差异分析，通过观察资源指标系统测试期间的行为（信号）变化，将正在测试的软件版本与先前的稳健版本进行比较。采用的关键工具是散度图，它表示两个信号之间随时间变化的差异，使我们能够检测系统指标值的变化，从而表明软件老化的存在。在我们的实验研究中，我们专注于内存泄漏检测，并使用各种多种统计技术与不同的应用程序级内存相关指标（RSS 和堆使用情况）相结合来计算和评估散度图。实验结果表明，与之前工作中广泛采用的技术（例如线性回归、移动平均值和中值）相比，我们提出的方法中使用的统计过程控制技术在内存泄漏检测方面取得了良好的性能。
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 in a shorter test time and higher accuracy compared to traditional aging detection via stress testing and trend detection with high confidence. The approach is based on a comparative differential analysis where a software version under test is compared with against a previous robust version by observing in terms of behavioral (signal) changes during system tests of resource metrics. A key instrument adopted is a divergence chart, which expresses time-dependent differences between two signals, allowing us to detect changes in the system metrics' values which indicate the existence of software aging. In our experimental study, we focuses on memory-leak detection and the and evaluates divergence charts are computed using various multiple statistical techniques combined paired with different application-level memory related metrics (RSS and Heap Usage). The experimental results show that the statistical process control techniques used in our approach proposed method achieves good performance for memory-leak detection, when compared with other in comparison to techniques widely adopted in previous works (e.g., linear regression, moving average and median).