Please use this identifier to cite or link to this item: https://dl.ucsc.cmb.ac.lk/jspui/handle/123456789/4590
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dc.contributor.authorIsaac, R. M.-
dc.date.accessioned2022-05-23T09:42:18Z-
dc.date.available2022-05-23T09:42:18Z-
dc.date.issued2022-05-23-
dc.identifier.urihttps://dl.ucsc.cmb.ac.lk/jspui/handle/123456789/4590-
dc.description.abstractEach and every one prefers to know and understand what ahead in our paths. For software industry this is essential. Being able to predict the customer satisfaction with maximizing efficiency and productivity would offer the ability to build reliable software. The tremendous amount of time and costs would be saved across the effectively manage human and material resources delivering the service that client expect of them. The presence of temporal trends in incidents tends to show patterns in data with certain period of time. The incidents demonstrate certain issue classifications such as defect, explanation, data setup issue, data correction, connectivity issue, enhancement and invalid. These certain patterns in relation to these set periods, spend time, and priority, the output can be used to predict future trends. This study discusses various ranges of problem of incident processes exist in the real world. Also discusses challenges when gathering such data. Then we process these incidents and visualize them using Google Data Studio “Data Studio is a free tool that turns your data into informative, easy to read, easy to share, and fully customizable dashboards and reports” (Welcome to Data Studio! - Data Studio Help, 2021). Finally we incorporate machine learning based approaches to predict customer satisfaction / dissatisfaction using processed incidents.en_US
dc.language.isoen_USen_US
dc.titleProcess Incident and Defect Visualizeren_US
dc.typeThesisen_US
Appears in Collections:2021

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