Iterating the minimizing Delta debugging algorithm

Dániel Vince
{"title":"Iterating the minimizing Delta debugging algorithm","authors":"Dániel Vince","doi":"10.1145/3548659.3561314","DOIUrl":null,"url":null,"abstract":"Probably the most well-known solution to automated test case minimization is the minimizing Delta Debugging algorithm (DDMIN). It is widely used because it “just works” on any kind of input. In this paper, we focus on the fixed-point iteration of DDMIN (named DDMIN*), more specifically whether it can improve on the result of the original algorithm. We present a carefully crafted example where the output of DDMIN could be reduced further, and iterating the algorithm finds a new, smaller local optimum. Then, we evaluate the idea on a publicly available test suite. We have found that the output of DDMIN* was usually smaller than the output of DDMIN. Using characters as units of reduction, the output became smaller by 67.94% on average, and in the best case, fixed-point iteration could improve as much as 89.68% on the output size of the original algorithm.","PeriodicalId":264587,"journal":{"name":"Proceedings of the 13th International Workshop on Automating Test Case Design, Selection and Evaluation","volume":"136 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 13th International Workshop on Automating Test Case Design, Selection and Evaluation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3548659.3561314","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

Abstract

Probably the most well-known solution to automated test case minimization is the minimizing Delta Debugging algorithm (DDMIN). It is widely used because it “just works” on any kind of input. In this paper, we focus on the fixed-point iteration of DDMIN (named DDMIN*), more specifically whether it can improve on the result of the original algorithm. We present a carefully crafted example where the output of DDMIN could be reduced further, and iterating the algorithm finds a new, smaller local optimum. Then, we evaluate the idea on a publicly available test suite. We have found that the output of DDMIN* was usually smaller than the output of DDMIN. Using characters as units of reduction, the output became smaller by 67.94% on average, and in the best case, fixed-point iteration could improve as much as 89.68% on the output size of the original algorithm.
迭代最小化增量调试算法
自动化测试用例最小化的最著名的解决方案可能是最小化增量调试算法(DDMIN)。它被广泛使用,因为它“只适用于”任何类型的输入。本文主要研究DDMIN的不动点迭代(命名为DDMIN*),更具体地说,它是否能够改进原算法的结果。我们提供了一个精心设计的示例,其中DDMIN的输出可以进一步减少,并且迭代算法找到一个新的,更小的局部最优。然后,我们在一个公开可用的测试套件上评估这个想法。我们发现DDMIN*的输出通常小于DDMIN的输出。使用字符作为约简的单位,输出平均缩小了67.94%,在最好的情况下,不动点迭代可以在原算法的输出大小上提高89.68%。
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:481959085
Book学术官方微信