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DILAF: A framework for distributed analysis of large-scale system logs for anomaly detection
(Wiley, 2019-02)
System logs constitute a rich source of information for detection and prediction of anomalies. However, they can include a huge volume of data, which is usually unstructured or semistructured. We introduce DILAF, a framework ...
Automated defect prioritization based on defects resolved at various project periods
(Elsevier, 2021-09)
Defect prioritization is mainly a manual and error-prone task in the current state-of-the-practice. We evaluated the effectiveness of an automated approach that employs supervised machine learning. We used two alternative ...
On the use of machine learning for predicting defect fix time violations
(Science and Technology Publications, 2022)
Accurate prediction of defect fix time is important for estimating and coordinating software maintenance efforts. Likewise, it is useful to predict whether or not the initially estimated defect fix time will be exceeded ...
Incremental analysis of large-scale system logs for anomaly detection
(IEEE, 2019)
Anomalies during system execution can be detected by automated analysis of logs generated by the system. However, large scale systems can generate tens of millions of lines of logs within days. Centralized implementations ...
Evaluation of distributed machine learning algorithms for anomaly detection from large-scale system logs: a case study
(IEEE, 2018)
Anomaly detection is a valuable feature for detecting and diagnosing faults in large-scale, distributed systems. These systems usually provide tens of millions of lines of logs that can be exploited for this purpose. ...
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