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Machine Learning to Uncover Correlations Between Software Code Changes and Test Results


Please use this identifier to cite or link to this item: http://hdl.handle.net/2077/54576

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Title: Machine Learning to Uncover Correlations Between Software Code Changes and Test Results
Authors: Fazeli, Negar
Issue Date: 5-Dec-2017
Degree: Student essay
Abstract: Statistics show that many large software companies, particularly those dealing with large-scale legacy systems, ultimately face an ever-growing code base. As the product grows, it becomes increasingly difficult to adequately test new changes in the code and maintain quality at a low cost without running a large number of test cases [1, 2, 3]. So a common problem with such products is that, thoroughly testing changes to the source code can become prohibitively time consuming and generally adhoc ... more
URI: http://hdl.handle.net/2077/54576
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