Augmenting the software testing workflow with machine learning
Name
1078691212-MIT.pdf
Description
Full printable version
Size
11 MB
Format
Adobe PDF
Checksum (MD5)
eb3e8bc8290e5388386c4bbc84bac726
Author(s)
Cao, Bingfei
Advisor(s)
Kalyan Veeramachaneni.
Date Issued
2018
Publisher
Massachusetts Institute of Technology
Abstract
This work presents the ML Software Tester, a system for augmenting software testing processes with machine learning. It allows users to plug in a Git repository of the choice, specify a few features and methods specific to that project, and create a full machine learning pipeline. This pipeline will generate software test result predictions that the user can easily integrate with their existing testing processes. To do so, a novel test result collection system was built to collect the necessary data on which the prediction models could be trained. Test data was collected for Flask, a well-known Python open-source project. This data was then fed through SVDFeature, a matrix prediction model, to generate new test result predictions. Several methods for the test result prediction procedure were evaluated to demonstrate various methods of using the system.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 67-68).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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