Automating Root Cause Analysis via Machine Learning in Agile Software Testing Environments

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Journal Title
Journal ISSN
Volume Title
Conference article in proceedings
Date
2019-04-01
Major/Subject
Mcode
Degree programme
Language
en
Pages
12
379-390
Series
Proceedings - 2019 IEEE 12th International Conference on Software Testing, Verification and Validation, ICST 2019
Abstract
We apply machine learning to automate the root cause analysis in agile software testing environments. In particular, we extract relevant features from raw log data after interviewing testing engineers (human experts). Initial efforts are put into clustering the unlabeled data, and despite obtaining weak correlations between several clusters and failure root causes, the vagueness in the rest of the clusters leads to the consideration of labeling. A new round of interviews with the testing engineers leads to the definition of five ground-truth categories. Using manually labeled data, we train artificial neural networks that either classify the data or pre-process it for clustering. The resulting method achieves an accuracy of 88.9%. The methodology of this paper serves as a prototype or baseline approach for the extraction of expert knowledge and its adaptation to machine learning techniques for root cause analysis in agile environments.
Description
Keywords
Artificial neural networks, Automation, Classification, Clustering, Log data analysis, Machine learning, Root cause analysis, Software testing
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Citation
Kahles , J , Torronen , J , Huuhtanen , T & Jung , A 2019 , Automating Root Cause Analysis via Machine Learning in Agile Software Testing Environments . in Proceedings - 2019 IEEE 12th International Conference on Software Testing, Verification and Validation, ICST 2019 . , 8730163 , IEEE , pp. 379-390 , IEEE International Conference on Software Testing, Verification and Validation , Xi'an , China , 22/04/2019 . https://doi.org/10.1109/ICST.2019.00047