Augmenting the software testing workflow with machine learning
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.
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| Other Authors: | |
| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2018
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| Online Access: | http://hdl.handle.net/1721.1/119752 |
| _version_ | 1826189507481829376 |
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| author | Cao, Bingfei |
| author2 | Kalyan Veeramachaneni. |
| author_facet | Kalyan Veeramachaneni. Cao, Bingfei |
| author_sort | Cao, Bingfei |
| collection | MIT |
| description | Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018. |
| first_indexed | 2024-09-23T08:15:57Z |
| format | Thesis |
| id | mit-1721.1/119752 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T08:15:57Z |
| publishDate | 2018 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1197522019-04-09T17:41:49Z Augmenting the software testing workflow with machine learning Cao, Bingfei Kalyan Veeramachaneni. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. 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). 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. by Bingfei Cao. M. Eng. 2018-12-18T19:48:32Z 2018-12-18T19:48:32Z 2018 2018 Thesis http://hdl.handle.net/1721.1/119752 1078691212 eng MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 68 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Cao, Bingfei Augmenting the software testing workflow with machine learning |
| title | Augmenting the software testing workflow with machine learning |
| title_full | Augmenting the software testing workflow with machine learning |
| title_fullStr | Augmenting the software testing workflow with machine learning |
| title_full_unstemmed | Augmenting the software testing workflow with machine learning |
| title_short | Augmenting the software testing workflow with machine learning |
| title_sort | augmenting the software testing workflow with machine learning |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/119752 |
| work_keys_str_mv | AT caobingfei augmentingthesoftwaretestingworkflowwithmachinelearning |