Combined static and dynamic mutability analysis

Shay Artzi, Adam Kiezun, David Glasser, Michael D. Ernst
{"title":"Combined static and dynamic mutability analysis","authors":"Shay Artzi, Adam Kiezun, David Glasser, Michael D. Ernst","doi":"10.1145/1321631.1321649","DOIUrl":null,"url":null,"abstract":"Knowing which method parameters may be mutated during a method's executionis useful for many software engineering tasks. We present an approach todiscovering parameter reference immutability, in which several lightweight, scalable analyses are combined in stages, with each stage refining the overall result. The resulting analysis is scalable and combines the strengths of its component analyses. As one of the component analyses, we present a novel, dynamic mutability analysis and show how its results can be improved by random input generation. Experimental results on programs of up to 185 kLOC show that, compared to previous approaches, our approach increases both scalability and overall accuracy","PeriodicalId":191088,"journal":{"name":"Proceedings of the 22nd IEEE/ACM International Conference on Automated Software Engineering","volume":"110 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"39","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 22nd IEEE/ACM International Conference on Automated Software Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/1321631.1321649","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 39

Abstract

Knowing which method parameters may be mutated during a method's executionis useful for many software engineering tasks. We present an approach todiscovering parameter reference immutability, in which several lightweight, scalable analyses are combined in stages, with each stage refining the overall result. The resulting analysis is scalable and combines the strengths of its component analyses. As one of the component analyses, we present a novel, dynamic mutability analysis and show how its results can be improved by random input generation. Experimental results on programs of up to 185 kLOC show that, compared to previous approaches, our approach increases both scalability and overall accuracy
结合静态和动态可变性分析
了解在方法执行过程中哪些方法参数可能会发生变化,这对许多软件工程任务都很有用。我们提出了一种发现参数引用不变性的方法,在这种方法中,几个轻量级的、可扩展的分析分阶段结合在一起,每个阶段都对整体结果进行改进。结果分析是可伸缩的,并结合了其组件分析的优势。作为成分分析的一种,我们提出了一种新颖的动态可变性分析,并展示了随机输入生成如何改善其结果。在高达185 kLOC的程序上的实验结果表明,与以前的方法相比,我们的方法提高了可扩展性和整体准确性
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 求助全文
来源期刊
自引率
0.00%
发文量
0
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信