Data analytics for decision support in software release management

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dc.contributor.advisorRuhe, Günther
dc.contributor.advisorPfahl, Dietmar
dc.contributor.authorDidar Al Alam, S. M.
dc.contributor.committeememberElhajj, Reda
dc.contributor.committeememberMoussavi, Mahmood
dc.contributor.committeememberIzurieta, Clemente
dc.contributor.committeememberWalker, Robert J.
dc.date2018-06
dc.date.accessioned2018-04-30T17:07:22Z
dc.date.available2018-04-30T17:07:22Z
dc.date.issued2018-04-26
dc.description.abstractThe dynamism and uncertainty in iterative software development demand for frequent releases with shorter duration, reduced scope, and strong stakeholder involvement. The lack of custom-built integrated support for release management in iterative releases mainly motivated this research. Key contribution of this thesis is the proposed Plan-Monitor-Improve (PMI) Framework. PMI integrates i) strategic planning of a release, ii) monitoring release readiness and iii) improving release readiness. Methodologically, this thesis is positioned at the intersection of three key concepts: i) release management ii) data analytics techniques, and iii) decision support systems. The PMI-Plan method introduces a multi-objective particle swarm optimization-based planning method to support the multi-dimensional nature of iterative releases. Maximum stakeholder incorporation is achieved in a three-stage interactive modeling. The research is triggered by industry collaborators in an NSERC CRD project and studies with a prototype tool in real world planning problems. Inspired by statistical process control approaches, the PMI-Monitor method continuously monitors release readiness. PMI-Monitor formulates release readiness prediction as a binary classification problem and solves using machine learning classifiers. The PMI-Improve method addresses release readiness by identifying key attributes to be used for decision-making. PMI-Improve also applies analogy-based reasoning for proactively identifying bottlenecks and their characteristics. The release readiness improvement factor identification is formulated as a search problem and solved using a genetic algorithm. The PMI is an integrated decision centric framework, custom built to support iterative release management. PMI integrates a portfolio of data analytics techniques (e.g. analogical reasoning, multi-objective optimization, classification, process control, interactive search) to gain better insight from the frequent release iterations and strong stakeholder involvements. PMI integrates the characteristics of iterative development and applies it towards an operational platform to make better data driven decisions. Initial empirical evaluation of PMI further supports applicability and benefits of the framework in real world iterative software development.en_US
dc.identifier.citationDidar Al Alam, S. M. (2018). Data analytics for decision support in software release management (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/31856en_US
dc.identifier.doihttp://dx.doi.org/10.11575/PRISM/31856
dc.identifier.urihttp://hdl.handle.net/1880/106570
dc.language.isoeng
dc.publisher.facultyGraduate Studies
dc.publisher.facultyScience
dc.publisher.institutionUniversity of Calgaryen
dc.publisher.placeCalgaryen
dc.rightsUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.
dc.subjectData analytics
dc.subjectDecision Support
dc.subjectRelease management
dc.subject.classificationComputer Scienceen_US
dc.titleData analytics for decision support in software release management
dc.typedoctoral thesis
thesis.degree.disciplineComputer Science
thesis.degree.grantorUniversity of Calgary
thesis.degree.nameDoctor of Philosophy (PhD)
ucalgary.item.requestcopytrue
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