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ASE 2019
Sun 10 - Fri 15 November 2019 San Diego, California, United States
Wed 13 Nov 2019 16:40 - 17:00 at Hillcrest - Performance Chair(s): Tim Menzies

Designing field-representative load tests is an essential step for the quality assurance of large-scale systems. Practitioners may capture user behaviour at different levels of granularity. A coarse-grained load test may miss detailed user behaviour, leading to a non-representative load test; while an extremely fine-grained load test would simply replay user actions step by step, leading to load tests that are costly to develop, execute and maintain. Workload recovery is core of these load tests. Prior research often captures the workload as the frequency of user actions. However, there exists much valuable information in the context and sequences of user actions. Such richer information would ensure that the load tests that leverage such workloads are more field-representative. In this experience paper, we study the use of different granularities of user behaviour, i.e., basic user actions, basic user actions with contextual information and user action sequences with contextual information, when recovering workloads for use in the load testing of large-scale systems. We propose three approaches that are based on the three granularities of user behaviour and evaluate our approaches on four subject systems, namely Apache James, OpenMRS, Google Borg, and an ultra-large-scale industrial system (SA) from CompanyA. Our results show that our approach that is based on user action sequences with contextual information outperforms the other two approaches and can generate more representative load tests with similar throughput and CPU usage to the original field workload (i.e., mostly statistically insignificant or with small/trivial effect sizes). Such representative load tests are generated only based on a small number of clusters of users, leading to a low cost of conducting/maintaining such tests. Finally, we demonstrate that our approaches can detect injected users in the original field workloads with high precision and recall. Our paper demonstrates the importance of user action sequences with contextual information in the workload recovery of large-scale systems.

Wed 13 Nov

16:00 - 17:50: Papers - Performance at Hillcrest
Chair(s): Tim MenziesNorth Carolina State University
ase-2019-papers16:00 - 16:20
Accurate Modeling of Performance Histories for Evolving Software Systems
Stefan MühlbauerBauhaus-University Weimar, Sven ApelSaarland University, Norbert SiegmundBauhaus-University Weimar
ase-2019-papers16:20 - 16:40
An Industrial Experience Report on Performance-Aware Refactoring on a Database-centric Web Application
Boyuan ChenYork University, Zhen Ming (Jack) JiangYork University, Paul MatosCopywell Inc., Michael LacariaCopywell Inc.
Authorizer link Pre-print
ase-2019-papers16:40 - 17:00
An Experience Report of Generating Load Tests Using Log-recovered Workloads at Varying Granularities of User Behaviour
Jinfu ChenJiangsu University, Weiyi (Ian) ShangConcordia University, Canada, Ahmed E. HassanQueen's University, Yong WangAlibaba Group, Jiangbin LinAlibaba Group
ase-2019-papers17:00 - 17:10
How Do API Selections Affect the Runtime Performance of Data Analytics Tasks?
Yida TaoShenzhen University, Shan TangShenzhen University, Yepang LiuSouthern University of Science and Technology, Zhiwu XuShenzhen University, Shengchao QinUniversity of Teesside
ase-2019-papers17:10 - 17:20
Demystifying Application Performance Management Libraries for Android
Yutian TangThe Hong Kong Polytechnic University, Zhan XianThe Hong Kong Polytechnic University, Hao ZhouThe Hong Kong Polytechnic University, Xiapu LuoThe Hong Kong Polytechnic University, Zhou XuWuhan University, Yajin ZhouZhejiang University, Qiben YanMichigan State University
ase-2019-Demonstrations17:20 - 17:30
PeASS: A Tool for Identifying Performance Changes at Code Level
David Georg ReicheltUniversität Leipzig, Stefan KühneUniversität Leipzig, Wilhelm HasselbringKiel University
Pre-print Media Attached File Attached
ase-2019-papers17:30 - 17:50
ReduKtor: How We Stopped Worrying About Bugs in Kotlin Compiler
Daniil StepanovSaint Petersburg Polytechnic University, Marat AkhinSaint Petersburg Polytechnic University / JetBrains Research, Mikhail BelyaevSaint Petersburg Polytechnic University