Accurate Modeling of Performance Histories for Evolving Software Systems
The performance of a software system plays a crucial role for user perception. Learning from the history of a software system’s performance behavior does not only help discovering and locating performance bugs, but also identifying evolutionary performance patterns and general trends, such as when technical debt accumulates in a slow but steady performance degradation. Exhaustive regression testing is usually impractical, because rigorous performance benchmarking requires executing a realistic workload per commit, which results in large execution times. In this paper, we propose a novel active revision sampling approach, which aims at tracking and understanding a system’s performance history by approximating the performance behavior of a software system across all of its revisions. In a nutshell, we iteratively sample and measure the performance of specific revisions that help us in building an exact performance- evolution model, and we use Gaussian Process models to assess in which revision ranges our model is most uncertain with the goal to to sample further revisions for measurement. We have conducted an empirical analysis of the evolutionary performance behavior modeled as a time series of the history of 6 real-world software systems. Our evaluation demonstrates that Gaussian Process models are able to accurately estimate the performance- evolution history of real-world software systems with only few measurements and to reveal interesting behaviors and trends.
Wed 13 NovDisplayed time zone: Tijuana, Baja California change
16:00 - 17:50 | PerformanceResearch Papers / Demonstrations at Hillcrest Chair(s): Tim Menzies North Carolina State University | ||
16:00 20mTalk | Accurate Modeling of Performance Histories for Evolving Software Systems Research Papers Stefan Mühlbauer Bauhaus-University Weimar, Sven Apel Saarland University, Norbert Siegmund Bauhaus-University Weimar Pre-print | ||
16:20 20mTalk | An Industrial Experience Report on Performance-Aware Refactoring on a Database-centric Web Application Research Papers Boyuan Chen York University, Zhen Ming (Jack) Jiang York University, Paul Matos Copywell Inc., Michael Lacaria Copywell Inc. Authorizer link Pre-print | ||
16:40 20mTalk | An Experience Report of Generating Load Tests Using Log-recovered Workloads at Varying Granularities of User Behaviour Research Papers Jinfu Chen Jiangsu University, Weiyi Shang Concordia University, Canada, Ahmed E. Hassan Queen's University, Yong Wang Alibaba Group, Jiangbin Lin Alibaba Group Pre-print | ||
17:00 10mTalk | How Do API Selections Affect the Runtime Performance of Data Analytics Tasks? Research Papers Yida Tao Shenzhen University, Shan Tang Shenzhen University, Yepang Liu Southern University of Science and Technology, Zhiwu Xu Shenzhen University, Shengchao Qin University of Teesside | ||
17:10 10mTalk | Demystifying Application Performance Management Libraries for Android Research Papers Yutian Tang The Hong Kong Polytechnic University, Xian Zhan The Hong Kong Polytechnic University, Hao Zhou The Hong Kong Polytechnic University, Xiapu Luo The Hong Kong Polytechnic University, Zhou Xu Wuhan University, Yajin Zhou Zhejiang University, Qiben Yan Michigan State University | ||
17:20 10mDemonstration | PeASS: A Tool for Identifying Performance Changes at Code Level Demonstrations David Georg Reichelt Universität Leipzig, Stefan Kühne Universität Leipzig, Wilhelm Hasselbring Kiel University Pre-print Media Attached File Attached | ||
17:30 20mTalk | ReduKtor: How We Stopped Worrying About Bugs in Kotlin Compiler Research Papers Daniil Stepanov Saint Petersburg Polytechnic University, Marat Akhin Saint Petersburg Polytechnic University / JetBrains Research, Mikhail Belyaev Saint Petersburg Polytechnic University Pre-print |