Defect Prediction on the Hardware Repository - A Case Study on the OpenRISC1000 Project

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dc.contributor.advisorAboelaze, Mokhtar
dc.creatorMusa, Silviu
dc.date.accessioned2017-07-27T13:44:27Z
dc.date.available2017-07-27T13:44:27Z
dc.date.copyright2017-03-09
dc.date.issued2017-07-27
dc.date.updated2017-07-27T13:44:27Z
dc.degree.disciplineComputer Science
dc.degree.levelMaster's
dc.degree.nameMSc - Master of Science
dc.description.abstractSoftware defect prediction is one of the most active research topics in the area of mining software engineering data. The software engineering data sources like the code repositories and the bug databases contain rich information about software development history. Mining these data can guide software developers for future development activities and help managers to improve the development process. Nowadays, the computer-engineering field has rapidly evolved from 1972 until present times to the modern chip design, which looks superficially and very much like software design. Hence, the main objective of this thesis is to check whether it would be possible to apply software defect prediction techniques on hardware repositories. In this thesis, we have applied various data mining methods (e.g., linear regression, logistic regression, random forests, and entropy) to predict the post-release bugs of OpenRISC 1000 projects. We have conducted two types of studies: classification (predicting buggy and non-buggy files) and ranking (predicting the buggiest files). In particular, the classification studies show promising results with an average precision and recall of up to 74% and 70% for projects written in Verilog and close to 100% for projects written in C.
dc.identifier.urihttp://hdl.handle.net/10315/33569
dc.language.isoen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subjectComputer science
dc.subject.keywordsSoftware
dc.subject.keywordsDefects
dc.subject.keywordsPrediction
dc.subject.keywordsHardware
dc.subject.keywordsRepository
dc.subject.keywordsOpen
dc.subject.keywordsProject
dc.subject.keywordsCode
dc.subject.keywordsCommits
dc.subject.keywordsBugzilla
dc.subject.keywordsWEKA
dc.subject.keywordsComplexity
dc.subject.keywordsMetrics
dc.subject.keywordsData
dc.subject.keywordsCleaning
dc.subject.keywordsEvaluation
dc.subject.keywordsF-measure
dc.subject.keywordsPrecision
dc.subject.keywordsRecall
dc.titleDefect Prediction on the Hardware Repository - A Case Study on the OpenRISC1000 Project
dc.typeElectronic Thesis or Dissertation

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