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

Julen Kahles, Juha Torronen, Timo Huuhtanen, Alexander Jung

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference contributionScientificvertaisarvioitu

391 Lataukset (Pure)

Abstrakti

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.

AlkuperäiskieliEnglanti
OtsikkoProceedings - 2019 IEEE 12th International Conference on Software Testing, Verification and Validation, ICST 2019
KustantajaIEEE
Sivut379-390
Sivumäärä12
ISBN (elektroninen)9781728117355
DOI - pysyväislinkit
TilaJulkaistu - 1 huhtikuuta 2019
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaIEEE International Conference on Software Testing, Verification and Validation - Xi'an, Kiina
Kesto: 22 huhtikuuta 201927 huhtikuuta 2019
Konferenssinumero: 12

Conference

ConferenceIEEE International Conference on Software Testing, Verification and Validation
LyhennettäICST
MaaKiina
KaupunkiXi'an
Ajanjakso22/04/201927/04/2019

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