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Automatically learning usage behavior and generating event sequences for black-box testing of reactive systems
(The ACM Digital Library, 2019-06)
We propose a novel technique based on recurrent artificial neural networks to generate test cases for black-box testing of reactive systems. We combine functional testing inputs that are automatically generated from a model ...
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 ...
Evaluating the effectiveness of multi-level greedy modularity clustering for software architecture recovery
(Springer Nature, 2019)
Software architecture recovery approaches mainly analyze various types of dependencies among software modules to group them and reason about the high-level structural decomposition of a system. These approaches employ a ...
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 ...
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