Sandeep Soumya Sekhar Mishra, P. Dutta, Gayatri Nayak, A. Tripathy, P. Kishore, S. Barisal
{"title":"Designing Fault-Counter for Object-Oriented Software using Bagging Technique","authors":"Sandeep Soumya Sekhar Mishra, P. Dutta, Gayatri Nayak, A. Tripathy, P. Kishore, S. Barisal","doi":"10.1109/APSIT58554.2023.10201726","DOIUrl":null,"url":null,"abstract":"In Software Engineering, the faults present in software are the most critical issues, since they produce many incorrect and unreliable results. For developing reliable software, these faults must be resolved. In this project, a fault counter model is designed to predict the number of faulty modules present in a software project. There are four contributions in this work. The first contribution is to collect the dataset. The collected dataset contains numerous high-ranged and null values. In the second contribution, data pre-processing techniques like standard scaling and null-value removal are applied. The third contribution is to apply feature selection techniques to remove the least important features from the dataset. The fourth contribution is to predict the number of faults present in software projects using the Bagging Technique. The proposed model achieves a 0.55 R2_ Score.","PeriodicalId":170044,"journal":{"name":"2023 International Conference in Advances in Power, Signal, and Information Technology (APSIT)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 International Conference in Advances in Power, Signal, and Information Technology (APSIT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/APSIT58554.2023.10201726","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In Software Engineering, the faults present in software are the most critical issues, since they produce many incorrect and unreliable results. For developing reliable software, these faults must be resolved. In this project, a fault counter model is designed to predict the number of faulty modules present in a software project. There are four contributions in this work. The first contribution is to collect the dataset. The collected dataset contains numerous high-ranged and null values. In the second contribution, data pre-processing techniques like standard scaling and null-value removal are applied. The third contribution is to apply feature selection techniques to remove the least important features from the dataset. The fourth contribution is to predict the number of faults present in software projects using the Bagging Technique. The proposed model achieves a 0.55 R2_ Score.