{"title":"软件应用中的涟漪效应识别","authors":"Anushree Agrawal, R. K. Singh","doi":"10.4018/ijossp.2020010103","DOIUrl":null,"url":null,"abstract":"Changes are made frequently in software to incorporate new requirements. The changes made to one class are not limited to that particular class, but they also affect other entities. Early identification of these change prone entities is very essential for minimizing future faults in the software applications. Thus, it is very important to develop quality models for identifying the ripple effect of changed classes to effectively utilize the limited resources during the software development lifecycle. Association rule mining is a popular approach suggested in literature, but a major limitation of this approach is its inability to generate recommendations in case of new addition of classes. This article suggests the development of prediction model using learning techniques to overcome this limitation. The authors evaluate the performance of thirteen statistical, ML, and search-based techniques using eight open source software applications in this work. The findings of this study are promising and support the application of SBT and ML techniques for ripple effect identification.","PeriodicalId":53605,"journal":{"name":"International Journal of Open Source Software and Processes","volume":"11 1","pages":"41-56"},"PeriodicalIF":0.0000,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Ripple Effect Identification in Software Applications\",\"authors\":\"Anushree Agrawal, R. K. Singh\",\"doi\":\"10.4018/ijossp.2020010103\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Changes are made frequently in software to incorporate new requirements. The changes made to one class are not limited to that particular class, but they also affect other entities. Early identification of these change prone entities is very essential for minimizing future faults in the software applications. Thus, it is very important to develop quality models for identifying the ripple effect of changed classes to effectively utilize the limited resources during the software development lifecycle. Association rule mining is a popular approach suggested in literature, but a major limitation of this approach is its inability to generate recommendations in case of new addition of classes. This article suggests the development of prediction model using learning techniques to overcome this limitation. The authors evaluate the performance of thirteen statistical, ML, and search-based techniques using eight open source software applications in this work. The findings of this study are promising and support the application of SBT and ML techniques for ripple effect identification.\",\"PeriodicalId\":53605,\"journal\":{\"name\":\"International Journal of Open Source Software and Processes\",\"volume\":\"11 1\",\"pages\":\"41-56\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Open Source Software and Processes\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4018/ijossp.2020010103\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"Computer Science\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Open Source Software and Processes","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4018/ijossp.2020010103","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Computer Science","Score":null,"Total":0}
Ripple Effect Identification in Software Applications
Changes are made frequently in software to incorporate new requirements. The changes made to one class are not limited to that particular class, but they also affect other entities. Early identification of these change prone entities is very essential for minimizing future faults in the software applications. Thus, it is very important to develop quality models for identifying the ripple effect of changed classes to effectively utilize the limited resources during the software development lifecycle. Association rule mining is a popular approach suggested in literature, but a major limitation of this approach is its inability to generate recommendations in case of new addition of classes. This article suggests the development of prediction model using learning techniques to overcome this limitation. The authors evaluate the performance of thirteen statistical, ML, and search-based techniques using eight open source software applications in this work. The findings of this study are promising and support the application of SBT and ML techniques for ripple effect identification.
期刊介绍:
The International Journal of Open Source Software and Processes (IJOSSP) publishes high-quality peer-reviewed and original research articles on the large field of open source software and processes. This wide area entails many intriguing question and facets, including the special development process performed by a large number of geographically dispersed programmers, community issues like coordination and communication, motivations of the participants, and also economic and legal issues. Beyond this topic, open source software is an example of a highly distributed innovation process led by the users. Therefore, many aspects have relevance beyond the realm of software and its development. In this tradition, IJOSSP also publishes papers on these topics. IJOSSP is a multi-disciplinary outlet, and welcomes submissions from all relevant fields of research and applying a multitude of research approaches.