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Machine Learning Optimization of KPI Prediction
Haris, Daniel ; Burget, Radek (referee) ; Bartík, Vladimír (advisor)
This thesis aims to optimize the machine learning algorithms for predicting KPI metrics for an organization. The organization is predicting whether projects meet planned deadlines of the last phase of development process using machine learning. The work focuses on the analysis of prediction models and sets the goal of selecting new candidate models for the prediction system. We have implemented a system that automatically selects the best feature variables for learning. Trained models were evaluated by several performance metrics and the best candidates were chosen for the prediction. Candidate models achieved higher accuracy, which means, that the prediction system provides more reliable responses. We suggested other improvements that could increase the accuracy of the forecast.
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Generation of Hypertext Documentation of Testing
Haris, Daniel ; Šimková, Hana (referee) ; Smrčka, Aleš (advisor)
The objective of this bachalor's thesis is to design and implement a helper tool for requirements-based testing. Tool consists of a web application, which focuses on visualization of coverage of requirements specification by user defined test cases. Another part of this tool is an agent, which generates templates of test suites based on information from the tool itself, executes test suites and returns test execution results. Output of this tool is a summary report about actual coverage of requirements specification.
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Machine Learning Optimization of KPI Prediction
Haris, Daniel ; Burget, Radek (referee) ; Bartík, Vladimír (advisor)
This thesis aims to optimize the machine learning algorithms for predicting KPI metrics for an organization. The organization is predicting whether projects meet planned deadlines of the last phase of development process using machine learning. The work focuses on the analysis of prediction models and sets the goal of selecting new candidate models for the prediction system. We have implemented a system that automatically selects the best feature variables for learning. Trained models were evaluated by several performance metrics and the best candidates were chosen for the prediction. Candidate models achieved higher accuracy, which means, that the prediction system provides more reliable responses. We suggested other improvements that could increase the accuracy of the forecast.
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Generation of Hypertext Documentation of Testing
Haris, Daniel ; Šimková, Hana (referee) ; Smrčka, Aleš (advisor)
The objective of this bachalor's thesis is to design and implement a helper tool for requirements-based testing. Tool consists of a web application, which focuses on visualization of coverage of requirements specification by user defined test cases. Another part of this tool is an agent, which generates templates of test suites based on information from the tool itself, executes test suites and returns test execution results. Output of this tool is a summary report about actual coverage of requirements specification.
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