The automotive industry is continuously innovating and adapting new technologies. Along with that, the companies work towards maintaining the quality of a hardware
product and meeting the customer demands. Before delivering the product to the customer, it is essential to test and approve it for the safe use. The concept remains
the same when it comes to a software. Adapting modern technologies will further improve the efficiency of testing a software.
The thesis aims to build a machine learning algorithm for the implementation during the software testing. In general, the evaluation of a generated test report
after the testing consumes more time. The built algorithm should be able to reduce the time spent and the manual effort during the evaluation. Basically, the machine
learning algorithms will analyze and learn the data available in the old test reports. Based on the learnt data pattern, it will suggest the possible root causes for the
failed test cases in the future. The thesis report has the literature survey that helped in understanding the machine learning concepts in different industries for similar problems. The tasks involved
while building the model are data loading, data pre-processing, selecting the best conditions for each algorithm and comparison of the performance among them.
It also suggest the possible future work towards improving the performance of the models. The entire work is implemented in Jupyter notebook using pandas and
scikit-learn libraries.
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:79059 |
| Date | 15 March 2024 |
| Creators | Pallathadka Shivarama, Anupama |
| Contributors | Hardt, Wolfram, Nagler, Michael, Schriefer, Michael, Bobb, Niko, Technische Universität Chemnitz |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, doc-type:masterThesis, info:eu-repo/semantics/masterThesis, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
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