Capturing customer workloads of database systems to replay these workloads during internal testing can be beneficial for software quality assurance. However, we experienced that such replays can produce a large amount of false positive alerts that make the results unreliable or time consuming to analyze. Therefore, we design a machine learning based approach that attributes root causes to the alerts. This provides several benefits for quality assurance and allows for example to classify whether an alert is true positive or false positive. Our approach considerably reduces manual effort and improves the overall quality assurance for the database system SAP HANA. We discuss the problem, the design and result of our approach, and we present practical limitations that may require further research.
翻译:利用数据库系统的客户工作量,在内部测试期间重现这些工作量,可有助于软件质量保证;然而,我们发现,这种重播可产生大量虚假的正面警报,使结果不可靠或耗费时间进行分析;因此,我们设计了一种基于机器的学习方法,将根本原因归结于警报,为质量保证提供若干好处,并允许对警报是否真正正面或假正面进行分类;我们的做法大大减少了人工操作,改进了数据库系统SAP HANNA的总体质量保证。我们讨论了问题、我们的方法的设计和结果,我们提出了可能需要进一步研究的实际限制。