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dc.contributor.authorPícha, Petr
dc.date.accessioned2020-06-30T07:19:27Z
dc.date.available2020-06-30T07:19:27Z
dc.date.issued2019
dc.identifier.urihttp://www.kiv.zcu.cz/site/documents/verejne/vyzkum/publikace/technicke-zpravy/2019/Rigo_P%C3%ADcha_2019_4.pdf
dc.identifier.urihttp://hdl.handle.net/11025/37196
dc.description.sponsorshipproject SGS-2016-018 Data and Software Engineering for Advanced Applicationsen
dc.format140 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherZápadočeská univerzita v Plznics
dc.rights© Západočeská univerzita v Plznics
dc.subjectprojektový managementcs
dc.subjectzkvalitňování softwarového procesucs
dc.subjectprojektová datacs
dc.subjectAplikace pro správu životního cyklucs
dc.titleDetecting software development process patterns in project dataen
dc.typereporten
dc.typezprávacs
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedProject Management (PM) and Software Process Improvement (SPI) are complex activities demanding decisions which are not clear-cut even when using a defined pro- cess based on best practices proven as beneficial to product quality and project suc- cess. This is due to specific context surrounding each software development project and the fact that much of the guidance is in textual form not suitable for automatic processing. An deep know-how and extensive manual analysis of the project data and progress is therefore needed to support the correct PM and SPI decisions. This analysis is time and resource consuming, and prone to overlooking important data and reaching incorrect conclusions potentially derailing the project even further. The goal of this work is to represent the project data using a unified metamodel allowing cross-examination of different projects, detection of defined errors in PM (anti-patterns) as well as points of divergence from the defined software development processes. The expected benefit is streamlining the anti-pattern detection activities, leaving more time to handle their occurrences properly, while minimizing their ef- fects on product quality and project success. To reach the goal we analyzed software development methodologies, Application Lifecycle Management (ALM) tools com- monly used in PM, techniques of software process modeling and current research in methods and frameworks to aid PM and SPI efforts. We also review well-known PM anti-patterns from literature and present a method for their operationalization. We then propose an approach to analyze project data from ALM tools and detect anti- pattern occurrence in it. The approach is partially validated through implementation of an experimental tool.en
dc.subject.translatedproject managementen
dc.subject.translatedSoftware Process Improvementen
dc.subject.translatedproject dataen
dc.subject.translatedApplication Lifecycle Managemenen
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