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Automation and Evaluation of Software Fault Prediction
Mälardalen University, School of Innovation, Design and Engineering.
2018 (English)Independent thesis Advanced level (degree of Master (One Year)), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

Delivering a fault-free software to the client requires exhaustive testing, which in today's ever-growing software systems, can be expensive and often impossible. Software fault prediction aims to improve software quality while reducing the testing effort by identifying fault-prone modules in the early stages of development process. However, software fault prediction activities are yet to be implemented in the daily work routine of practitioners as a result of a lack of automation of this process. This thesis presents an Eclipse plug-in as a fault prediction automation tool that can predict fault-prone modules using two prediction methods, Naive Bayes and Logistic Regression, while also reflecting on the performance of these prediction methods compared to each other. Evaluating the prediction methods on open source projects concluded that Logistic Regression performed better than Naive Bayes.As part of the prediction process, this thesis also reflects on the easiest metrics to automatically gather for fault prediction concluding that LOC, McCabe Complexity and CK suite metrics are the easiest to automatically gather.

Place, publisher, year, edition, pages
2018. , p. 42
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-39995OAI: oai:DiVA.org:mdh-39995DiVA, id: diva2:1222499
Subject / course
Computer Science
Presentation
2018-06-05, 14:49 (English)
Supervisors
Examiners
Available from: 2018-07-01 Created: 2018-06-21 Last updated: 2018-07-01Bibliographically approved

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fulltext(755 kB)8 downloads
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CiteExportLink to record
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Citation style
  • apa
  • ieee
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  • vancouver
  • Other style
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  • de-DE
  • en-GB
  • en-US
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  • nn-NO
  • nn-NB
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  • Other locale
More languages
Output format
  • html
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