{"title":"Interactive Production Performance Feedback in the IDE","authors":"Jürgen Cito, P. Leitner, M. Rinard, H. Gall","doi":"10.1109/ICSE.2019.00102","DOIUrl":null,"url":null,"abstract":"Because of differences between development and production environments, many software performance problems are detected only after software enters production. We present PerformanceHat, a new system that uses profiling information from production executions to develop a global performance model suitable for integration into interactive development environments. PerformanceHat's ability to incrementally update this global model as the software is changed in the development environment enables it to deliver near real-time predictions of performance consequences reflecting the impact on the production environment. We implement PerformanceHat as an Eclipse plugin and evaluate it in a controlled experiment with 20 professional software developers implementing several software maintenance tasks using our approach and a representative baseline (Kibana). Our results indicate that developers using PerformanceHat were significantly faster in (1) detecting the performance problem, and (2) finding the root-cause of the problem. These results provide encouraging evidence that our approach helps developers detect, prevent, and debug production performance problems during development before the problem manifests in production.","PeriodicalId":6736,"journal":{"name":"2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE)","volume":"64 1","pages":"971-981"},"PeriodicalIF":0.0000,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"15","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSE.2019.00102","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 15
Abstract
Because of differences between development and production environments, many software performance problems are detected only after software enters production. We present PerformanceHat, a new system that uses profiling information from production executions to develop a global performance model suitable for integration into interactive development environments. PerformanceHat's ability to incrementally update this global model as the software is changed in the development environment enables it to deliver near real-time predictions of performance consequences reflecting the impact on the production environment. We implement PerformanceHat as an Eclipse plugin and evaluate it in a controlled experiment with 20 professional software developers implementing several software maintenance tasks using our approach and a representative baseline (Kibana). Our results indicate that developers using PerformanceHat were significantly faster in (1) detecting the performance problem, and (2) finding the root-cause of the problem. These results provide encouraging evidence that our approach helps developers detect, prevent, and debug production performance problems during development before the problem manifests in production.