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ISSTA 2020
Sat 18 - Wed 22 July 2020
Tue 21 Jul 2020 16:50 - 17:10 at Zoom - CHALLENGING DOMAINS Chair(s): Yi Li

Apache Spark has been widely used to build big data applications. Spark utilizes the abstraction of Resilient Distributed Dataset (RDD) to store and retrieve large-scale data. To reduce duplicate computation of an RDD, Spark can cache the RDD in memory and then reuse it later, thus improving performance. Spark relies on application developers to enforce caching decisions by using persist() and unpersist() APIs, e.g., which RDD is persisted and when the RDD is persisted / unpersisted. Incorrect RDD caching decisions can cause duplicate computations, or waste precious memory resource, thus introducing serious performance degradation in Spark applications. In this paper, we propose CacheCheck, to automatically detect cache-related bugs in Spark applications. We summarize six cache-related bug patterns in Spark applications, and then dynamically detect cache-related bugs by analyzing the execution traces of Spark applications. We evaluate CacheCheck on six real-world Spark applications. The experimental result shows that CacheCheck detects 72 previously unknown cache-related bugs, and 28 of them have been fixed by developers.

Tue 21 Jul
Times are displayed in time zone: Tijuana, Baja California change

16:10 - 17:10: CHALLENGING DOMAINSTechnical Papers at Zoom
Chair(s): Yi LiNanyang Technological University, Singapore

Public Live Stream/Recording. Registered participants should join via the Zoom link distributed in Slack.

16:10 - 16:30
Talk
Intermittently Failing Tests in the Embedded Systems Domain
Technical Papers
Per Erik StrandbergWestermo Network Technologies AB, Thomas Ostrand, Elaine WeyukerMälardalen University, Wasif AfzalMälardalen University, Daniel SundmarkMälardalen University
DOI Pre-print Media Attached
16:30 - 16:50
Talk
Feasible and Stressful Trajectory Generation for Mobile RobotsArtifacts Evaluated – ReusableArtifacts AvailableArtifacts Evaluated – FunctionalDistinguished Artifact
Technical Papers
Carl HildebrandtUniversity of Virginia, Sebastian ElbaumUniversity of Virginia, USA, Nicola BezzoUniversity of Virginia, Matthew B DwyerUniversity of Virginia
DOI
16:50 - 17:10
Talk
Detecting Cache-Related Bugs in Spark ApplicationsArtifacts Evaluated – ReusableArtifacts AvailableArtifacts Evaluated – Functional
Technical Papers
Hui Li, Dong WangInstitute of software, Chinese academy of sciences, Tianze Huang, Yu GaoInstitute of Software, Chinese Academy of Sciences, China, Wensheng DouInstitute of Software, Chinese Academy of Sciences, Lijie XuInstitute of Software, Chinese Academy of Sciences, Wei Wang, Jun WeiState Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences & University of Chinese Academy of Sciences, Hua Zhong
DOI