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A Deep Learning approach to predict software bugs using micro patterns and software metrics

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Název: A Deep Learning approach to predict software bugs using micro patterns and software metrics
Autoři: Brumfield, Marcus
Zdroj: Theses and Dissertations
Informace o vydavateli: Scholars Junction
Rok vydání: 2020
Témata: Deep Learning, Traceable Code Patterns, Software Metrics
Popis: Software bugs prediction is one of the most active research areas in the software engineering community. The process of testing and debugging code proves to be costly during the software development life cycle. Software metrics measure the quality of source code to identify software bugs and vulnerabilities. Traceable code patterns are able to de- scribe code at a finer granularity level to measure quality. Micro patterns will be used in this research to mechanically describe java code at the class level. Machine learning has also been introduced for bug prediction to localize source code for testing and debugging. Deep Learning is a branch of Machine Learning that is relatively new. This research looks to improve the prediction of software bugs by utilizing micro patterns with deep learning techniques. Software bug prediction at a finer granularity level will enable developers to localize code to test and debug during the development process.
Druh dokumentu: text
Popis souboru: application/pdf
Jazyk: unknown
Relation: https://scholarsjunction.msstate.edu/td/101; https://scholarsjunction.msstate.edu/context/td/article/1100/viewcontent/Marcus_Brumfield_Thesis.pdf
Dostupnost: https://scholarsjunction.msstate.edu/td/101
https://scholarsjunction.msstate.edu/context/td/article/1100/viewcontent/Marcus_Brumfield_Thesis.pdf
Přístupové číslo: edsbas.92F2BEE1
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  Data: A Deep Learning approach to predict software bugs using micro patterns and software metrics
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  Data: Theses and Dissertations
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  Data: Software bugs prediction is one of the most active research areas in the software engineering community. The process of testing and debugging code proves to be costly during the software development life cycle. Software metrics measure the quality of source code to identify software bugs and vulnerabilities. Traceable code patterns are able to de- scribe code at a finer granularity level to measure quality. Micro patterns will be used in this research to mechanically describe java code at the class level. Machine learning has also been introduced for bug prediction to localize source code for testing and debugging. Deep Learning is a branch of Machine Learning that is relatively new. This research looks to improve the prediction of software bugs by utilizing micro patterns with deep learning techniques. Software bug prediction at a finer granularity level will enable developers to localize code to test and debug during the development process.
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