跟踪测试:R的自动单元测试提取

Filip Krikava, J. Vitek
{"title":"跟踪测试:R的自动单元测试提取","authors":"Filip Krikava, J. Vitek","doi":"10.1145/3213846.3213863","DOIUrl":null,"url":null,"abstract":"Unit tests are labor-intensive to write and maintain. This paper looks into how well unit tests for a target software package can be extracted from the execution traces of client code. Our objective is to reduce the effort involved in creating test suites while minimizing the number and size of individual tests, and maximizing coverage. To evaluate the viability of our approach, we select a challenging target for automated test extraction, namely R, a programming language that is popular for data science applications. The challenges presented by R are its extreme dynamism, coerciveness, and lack of types. This combination decrease the efficacy of traditional test extraction techniques. We present Genthat, a tool developed over the last couple of years to non-invasively record execution traces of R programs and extract unit tests from those traces. We have carried out an evaluation on 1,545 packages comprising 1.7M lines of R code. The tests extracted by Genthat improved code coverage from the original rather low value of 267,496 lines to 700,918 lines. The running time of the generated tests is 1.9 times faster than the code they came from","PeriodicalId":20542,"journal":{"name":"Proceedings of the 27th ACM SIGSOFT International Symposium on Software Testing and Analysis","volume":"2 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2018-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"15","resultStr":"{\"title\":\"Tests from traces: automated unit test extraction for R\",\"authors\":\"Filip Krikava, J. Vitek\",\"doi\":\"10.1145/3213846.3213863\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Unit tests are labor-intensive to write and maintain. This paper looks into how well unit tests for a target software package can be extracted from the execution traces of client code. Our objective is to reduce the effort involved in creating test suites while minimizing the number and size of individual tests, and maximizing coverage. To evaluate the viability of our approach, we select a challenging target for automated test extraction, namely R, a programming language that is popular for data science applications. The challenges presented by R are its extreme dynamism, coerciveness, and lack of types. This combination decrease the efficacy of traditional test extraction techniques. We present Genthat, a tool developed over the last couple of years to non-invasively record execution traces of R programs and extract unit tests from those traces. We have carried out an evaluation on 1,545 packages comprising 1.7M lines of R code. The tests extracted by Genthat improved code coverage from the original rather low value of 267,496 lines to 700,918 lines. The running time of the generated tests is 1.9 times faster than the code they came from\",\"PeriodicalId\":20542,\"journal\":{\"name\":\"Proceedings of the 27th ACM SIGSOFT International Symposium on Software Testing and Analysis\",\"volume\":\"2 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-07-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"15\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 27th ACM SIGSOFT International Symposium on Software Testing and Analysis\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3213846.3213863\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 27th ACM SIGSOFT International Symposium on Software Testing and Analysis","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3213846.3213863","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 15

摘要

编写和维护单元测试需要大量的劳动。本文研究了如何从客户端代码的执行轨迹中提取目标软件包的单元测试。我们的目标是减少创建测试套件所涉及的工作量,同时最小化单个测试的数量和大小,并最大化覆盖率。为了评估我们方法的可行性,我们为自动测试提取选择了一个具有挑战性的目标,即R,一种流行于数据科学应用程序的编程语言。R带来的挑战是它的极端动态性、强制性和缺乏类型。这种组合降低了传统测试提取技术的效果。我们介绍Genthat,这是过去几年开发的一种工具,用于非侵入性地记录R程序的执行轨迹,并从这些轨迹中提取单元测试。我们对1545个包含1.7M行R代码的包进行了评估。Genthat提取的测试将代码覆盖率从原来相当低的267,496行提高到700,918行。生成的测试的运行时间比它们来自的代码快1.9倍
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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
Tests from traces: automated unit test extraction for R
Unit tests are labor-intensive to write and maintain. This paper looks into how well unit tests for a target software package can be extracted from the execution traces of client code. Our objective is to reduce the effort involved in creating test suites while minimizing the number and size of individual tests, and maximizing coverage. To evaluate the viability of our approach, we select a challenging target for automated test extraction, namely R, a programming language that is popular for data science applications. The challenges presented by R are its extreme dynamism, coerciveness, and lack of types. This combination decrease the efficacy of traditional test extraction techniques. We present Genthat, a tool developed over the last couple of years to non-invasively record execution traces of R programs and extract unit tests from those traces. We have carried out an evaluation on 1,545 packages comprising 1.7M lines of R code. The tests extracted by Genthat improved code coverage from the original rather low value of 267,496 lines to 700,918 lines. The running time of the generated tests is 1.9 times faster than the code they came from
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
LAND: a user-friendly and customizable test generation tool for Android apps Bench4BL: reproducibility study on the performance of IR-based bug localization Search-based detection of deviation failures in the migration of legacy spreadsheet applications Identifying implementation bugs in machine learning based image classifiers using metamorphic testing Tests from traces: automated unit test extraction for R
×
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