Comparing apples and oranges? Investigating the consistency of CPU and memory profiler results across multiple java versions

IF 2 2区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING Automated Software Engineering Pub Date : 2024-03-22 DOI:10.1007/s10515-024-00423-2
Myles Watkinson, Alexander E. I. Brownlee
{"title":"Comparing apples and oranges? Investigating the consistency of CPU and memory profiler results across multiple java versions","authors":"Myles Watkinson,&nbsp;Alexander E. I. Brownlee","doi":"10.1007/s10515-024-00423-2","DOIUrl":null,"url":null,"abstract":"<div><p>Profiling is an important tool in the software developer’s box, used to identify <i>hot</i> methods where most computational resources are used, to focus efforts at improving efficiency. Profilers are also important in the context of Genetic improvement (GI) of software. GI applies search-based optimisation to existing software with many examples of success in a variety of contexts. GI generates variants of the original program, testing each for functionality and properties such as run time or memory footprint, and profiling can be used to target the code variations to increase the search efficiency. We report on an experimental study comparing two profilers included with different versions of the Java Development Kit (JDK), HPROF (JDK 8) and Java Flight Recorder (JFR) (JDK 8, 9, and 17), within the GI toolbox Gin on six open-source applications, for both run time and memory use. We find that a core set of methods are labelled <i>hot</i> in most runs, with a long tail appearing rarely. We suggest five repeats enough to overcome this noise. Perhaps unsurprisingly, changing the profiler and JDK dramatically change the <i>hot</i> methods identified, so profiling must be rerun for new JDKs. We also show that using profiling for test case subset selection is unwise, often missing relevant members of the test suite. Similar general patterns are seen for memory profiling as for run time but the identified <i>hot</i> methods are often quite different.</p></div>","PeriodicalId":55414,"journal":{"name":"Automated Software Engineering","volume":"31 1","pages":""},"PeriodicalIF":2.0000,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10515-024-00423-2.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Automated Software Engineering","FirstCategoryId":"94","ListUrlMain":"https://link.springer.com/article/10.1007/s10515-024-00423-2","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, SOFTWARE ENGINEERING","Score":null,"Total":0}
引用次数: 0

Abstract

Profiling is an important tool in the software developer’s box, used to identify hot methods where most computational resources are used, to focus efforts at improving efficiency. Profilers are also important in the context of Genetic improvement (GI) of software. GI applies search-based optimisation to existing software with many examples of success in a variety of contexts. GI generates variants of the original program, testing each for functionality and properties such as run time or memory footprint, and profiling can be used to target the code variations to increase the search efficiency. We report on an experimental study comparing two profilers included with different versions of the Java Development Kit (JDK), HPROF (JDK 8) and Java Flight Recorder (JFR) (JDK 8, 9, and 17), within the GI toolbox Gin on six open-source applications, for both run time and memory use. We find that a core set of methods are labelled hot in most runs, with a long tail appearing rarely. We suggest five repeats enough to overcome this noise. Perhaps unsurprisingly, changing the profiler and JDK dramatically change the hot methods identified, so profiling must be rerun for new JDKs. We also show that using profiling for test case subset selection is unwise, often missing relevant members of the test suite. Similar general patterns are seen for memory profiling as for run time but the identified hot methods are often quite different.

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
比较苹果和橘子?调查跨多个 Java 版本的 CPU 和内存剖析器结果的一致性
剖析是软件开发人员的一个重要工具,用于识别使用计算资源最多的热门方法,以集中精力提高效率。剖析器在软件遗传改进(GI)方面也很重要。基因改进将基于搜索的优化技术应用于现有软件,在各种情况下都有许多成功的例子。GI 会生成原始程序的变体,测试每个变体的功能和属性,如运行时间或内存占用,而剖析可用于针对代码变体提高搜索效率。我们报告了一项实验研究,在 GI 工具箱 Gin 中,比较了 Java Development Kit (JDK) 不同版本所包含的两种剖析器 HPROF (JDK 8) 和 Java Flight Recorder (JFR) (JDK 8、9 和 17),在运行时间和内存使用方面对六个开源应用程序进行了剖析。我们发现,在大多数运行中,一组核心方法都被贴上了热标签,长尾方法很少出现。我们建议重复五次就足以克服这种噪音。也许不足为奇的是,改变剖析器和 JDK 会显著改变识别出的热门方法,因此必须针对新的 JDK 重新运行剖析。我们还发现,使用剖析来选择测试用例子集是不明智的,往往会遗漏测试套件中的相关成员。内存剖析的一般模式与运行时间剖析类似,但识别出的热点方法往往大相径庭。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Automated Software Engineering
Automated Software Engineering 工程技术-计算机:软件工程
CiteScore
4.80
自引率
11.80%
发文量
51
审稿时长
>12 weeks
期刊介绍: This journal details research, tutorial papers, survey and accounts of significant industrial experience in the foundations, techniques, tools and applications of automated software engineering technology. This includes the study of techniques for constructing, understanding, adapting, and modeling software artifacts and processes. Coverage in Automated Software Engineering examines both automatic systems and collaborative systems as well as computational models of human software engineering activities. In addition, it presents knowledge representations and artificial intelligence techniques applicable to automated software engineering, and formal techniques that support or provide theoretical foundations. The journal also includes reviews of books, software, conferences and workshops.
期刊最新文献
Evoattack: suppressive adversarial attacks against object detection models using evolutionary search Multi-objective improvement of Android applications Contractsentry: a static analysis tool for smart contract vulnerability detection Exploring the impact of code review factors on the code review comment generation A holistic approach to software fault prediction with dynamic classification
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1