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Conference Papers Year : 2022

Correlating Test Events With Monitoring Logs For Test Log Reduction And Anomaly Prediction

Abstract

Automated fault identification in long test logs is a tough problem, mainly because of their sequential character and the impossibility of constructing training sets for zero-day faults. To reduce software testers' workload, rule-based approaches have been extensively investigated as solutions for efficiently finding and predicting the fault. Based on software system status monitoring log analysis, we propose a new learning-based technique to automate anomaly detection, correlate test events to anomalies and predict system failures. Since the meaning of fault is not established in system status monitoring-based fault detection, the suggested technique first detects periods of time when a software system status encounters aberrant situations (Bug-Zones). The suggested technique is then tested in a realtime system for anomaly prediction of new tests. The model may be used in two ways. It can assist testers to focus on faulty-like time intervals by reducing the number of test logs. It may also be used to forecast a Bug-Zone in an online system, allowing system administrators to anticipate or even prevent a system failure. An extensive study on a real-world database acquired by a telecommunication operator demonstrates that our approach achieves 71% accuracy as a Bug-Zones predictor.
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Dates and versions

hal-03970805 , version 1 (02-02-2023)

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Bahareh Afshinpour, Roland Groz, Massih-Reza Amini. Correlating Test Events With Monitoring Logs For Test Log Reduction And Anomaly Prediction. 2022 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW), Oct 2022, Charlotte, United States. pp.274-280, ⟨10.1109/ISSREW55968.2022.00079⟩. ⟨hal-03970805⟩
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