Statistical Analysis for Revealing Defects in Software Projects

Detalhes bibliográficos
Autor(a) principal: Elsayed, Alia Nabil Mahmoud Faried
Data de Publicação: 2022
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/132673
Resumo: Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies Management
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spelling Statistical Analysis for Revealing Defects in Software ProjectsDefectsSoftware projectsStatistical modelLinear regressionLogistic regressionSDG 9 - Industry, innovation and infrastructureDissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementDefect detection in software is the procedure to identify parts of software that may comprise defects. Software companies always seek to improve the performance of software projects in terms of quality and efficiency. They also seek to deliver the soft-ware projects without any defects to the communities and just in time. The early revelation of defects in software projects is also tried to avoid failure of those projects, save costs, team effort, and time. Therefore, these companies need to build an intelligent model capable of detecting software defects accurately and efficiently. This study seeks to achieve two main objectives. The first goal is to build a statistical model to identify the critical defect factors that influence software projects. The second objective is to build a statistical model to reveal defects early in software pro-jects as reasonable accurately. A bibliometric map (VOSviewer) was used to find the relationships between the common terms in those domains. The results of this study are divided into three parts: In the first part The term "software engineering" is connected to "cluster," "regression," and "neural network." Moreover, the terms "random forest" and "feature selection" are connected to "neural network," "recall," and "software engineering," "cluster," "regression," and "fault prediction model" and "software defect prediction" and "defect density." In the second part We have checked and analyzed 29 manuscripts in detail, summarized their major contributions, and identified a few research gaps. In the third part Finally, software companies try to find the critical factors that affect the detection of software defects and find any of the intelligent or statistical methods that help to build a model capable of detecting those defects with high accuracy. Two statistical models (Multiple linear regression (MLR) and logistic regression (LR)) were used to find the critical factors and through them to detect software defects accurately. MLR is executed by using two methods which are critical defect factors (CDF) and premier list of software defect factors (PLSDF). The accuracy of MLR-CDF and MLR-PLSDF is 82.3 and 79.9 respectively. The standard error of MLR-CDF and MLR-PLSDF is 26% and 28% respectively. In addition, LR is executed by using two methods which are CDF and PLSDF. The accuracy of LR-CDF and LR-PLSDF is 86.4 and 83.8 respectively. The standard error of LR-CDF and LR-PLSDF is 22% and 25% respectively. Therefore, LRCDF outperforms on all the proposed models and state-of-the-art methods in terms of accuracy and standard error.Santos, Vitor Manuel Pereira Duarte dosRUNElsayed, Alia Nabil Mahmoud Faried2022-02-10T16:00:48Z2022-01-172022-01-17T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/132673TID:202938778enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:11:22Zoai:run.unl.pt:10362/132673Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:47:34.051391Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Statistical Analysis for Revealing Defects in Software Projects
title Statistical Analysis for Revealing Defects in Software Projects
spellingShingle Statistical Analysis for Revealing Defects in Software Projects
Elsayed, Alia Nabil Mahmoud Faried
Defects
Software projects
Statistical model
Linear regression
Logistic regression
SDG 9 - Industry, innovation and infrastructure
title_short Statistical Analysis for Revealing Defects in Software Projects
title_full Statistical Analysis for Revealing Defects in Software Projects
title_fullStr Statistical Analysis for Revealing Defects in Software Projects
title_full_unstemmed Statistical Analysis for Revealing Defects in Software Projects
title_sort Statistical Analysis for Revealing Defects in Software Projects
author Elsayed, Alia Nabil Mahmoud Faried
author_facet Elsayed, Alia Nabil Mahmoud Faried
author_role author
dc.contributor.none.fl_str_mv Santos, Vitor Manuel Pereira Duarte dos
RUN
dc.contributor.author.fl_str_mv Elsayed, Alia Nabil Mahmoud Faried
dc.subject.por.fl_str_mv Defects
Software projects
Statistical model
Linear regression
Logistic regression
SDG 9 - Industry, innovation and infrastructure
topic Defects
Software projects
Statistical model
Linear regression
Logistic regression
SDG 9 - Industry, innovation and infrastructure
description Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies Management
publishDate 2022
dc.date.none.fl_str_mv 2022-02-10T16:00:48Z
2022-01-17
2022-01-17T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/132673
TID:202938778
url http://hdl.handle.net/10362/132673
identifier_str_mv TID:202938778
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
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collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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