View in EDS

A bayesian belief network based model for predicting software faults in early phase of software development process.

Saved in:
Bibliographic Details
Title: A bayesian belief network based model for predicting software faults in early phase of software development process.
Authors: Chatterjee, Subhashis1 chatterjee_subhashis@rediffmail.com, Maji, Bappa1 bappamath@gmail.com
Source: Applied Intelligence. Aug2018, Vol. 48 Issue 8, p2214-2228. 15p.
Subjects: Bayesian analysis, Statistical decision making, Computer software development, Prediction models, Probability theory
Abstract: It is always better to have an idea about the future situation of a present work. Prediction of software faults in the early phase of software development life cycle can facilitate to the software personnel to achieve their desired software product. Early prediction is of great importance for optimizing the development cost of a software project. The present study proposes a methodology based on Bayesian belief network, developed to predict total number of faults and to reach a target value of total number of faults during early development phase of software lifecycle. The model has been carried out using the information from similar or earlier version software projects, domain expert’s opinion and the software metrics. Interval type-2 fuzzy logic has been applied for obtaining the conditional probability values in the node probability tables of the belief network. The output pattern corresponding to the total number of faults has been identified by artificial neural network using the input pattern from similar or earlier project data. The proposed Bayesian framework facilitates software personnel to gain the required information about software metrics at early phase for achieving targeted number of software faults. The proposed model has been applied on twenty six software project data. Results have been validated by different statistical comparison criterion. The performance of the proposed approach has been compared with some existing early fault prediction models. [ABSTRACT FROM AUTHOR]
Copyright of Applied Intelligence is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
FullText Text:
  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 130772925
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: A bayesian belief network based model for predicting software faults in early phase of software development process.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Chatterjee%2C+Subhashis%22">Chatterjee, Subhashis</searchLink><relatesTo>1</relatesTo><i> chatterjee_subhashis@rediffmail.com</i><br /><searchLink fieldCode="AR" term="%22Maji%2C+Bappa%22">Maji, Bappa</searchLink><relatesTo>1</relatesTo><i> bappamath@gmail.com</i>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Applied+Intelligence%22">Applied Intelligence</searchLink>. Aug2018, Vol. 48 Issue 8, p2214-2228. 15p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Bayesian+analysis%22">Bayesian analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+decision+making%22">Statistical decision making</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+software+development%22">Computer software development</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br /><searchLink fieldCode="DE" term="%22Probability+theory%22">Probability theory</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: It is always better to have an idea about the future situation of a present work. Prediction of software faults in the early phase of software development life cycle can facilitate to the software personnel to achieve their desired software product. Early prediction is of great importance for optimizing the development cost of a software project. The present study proposes a methodology based on Bayesian belief network, developed to predict total number of faults and to reach a target value of total number of faults during early development phase of software lifecycle. The model has been carried out using the information from similar or earlier version software projects, domain expert’s opinion and the software metrics. Interval type-2 fuzzy logic has been applied for obtaining the conditional probability values in the node probability tables of the belief network. The output pattern corresponding to the total number of faults has been identified by artificial neural network using the input pattern from similar or earlier project data. The proposed Bayesian framework facilitates software personnel to gain the required information about software metrics at early phase for achieving targeted number of software faults. The proposed model has been applied on twenty six software project data. Results have been validated by different statistical comparison criterion. The performance of the proposed approach has been compared with some existing early fault prediction models. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Applied Intelligence is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=130772925
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1007/s10489-017-1078-x
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 15
        StartPage: 2214
    Subjects:
      – SubjectFull: Bayesian analysis
        Type: general
      – SubjectFull: Statistical decision making
        Type: general
      – SubjectFull: Computer software development
        Type: general
      – SubjectFull: Prediction models
        Type: general
      – SubjectFull: Probability theory
        Type: general
    Titles:
      – TitleFull: A bayesian belief network based model for predicting software faults in early phase of software development process.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Chatterjee, Subhashis
      – PersonEntity:
          Name:
            NameFull: Maji, Bappa
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 08
              Text: Aug2018
              Type: published
              Y: 2018
          Identifiers:
            – Type: issn-print
              Value: 0924669X
          Numbering:
            – Type: volume
              Value: 48
            – Type: issue
              Value: 8
          Titles:
            – TitleFull: Applied Intelligence
              Type: main
ResultId 1