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.
An assessment of filters for classic oversampled audio waveshaping schemes is carried out in this paper, pursuing aliasing reduction. For this purpose, the quality measure of the Aweighted noise-to-mask ratio is computed for test tones covering the frequency range from 27.5 Hz to 4.186 kHz, sampled at 44.1 kHz, and processed at eight-times oversampling. All filters are designed to have their passband contained within a ±1 dB range and to display a minimum stopband attenuation value of 40 dB. Waveshaping of sinusoids via hard clipping is investigated: the spectral enrichment due to the discontinuities introduced by its nonlinear transfer function maximizes aliasing distortion. The obtained results suggest that linear interpolation equalized with a high shelving filter is a sufficiently good method for upsampling. Concerning decimation, the interpolated FIR, elliptic, and cascaded integrator-comb filters all improve the results with respect to the trivial case. Regarding performance, the cascaded integrator-comb filter is the only tested decimation filter that achieves perceptually sufficient aliasing suppression for the entire frequency range when combined with the linear interpolator.
We propose a novel machine-learning pipeline for clustering unknown IoT devices in an industrial 5G mobilenetwork setting. Organizing IoT devices as few homogeneous device groups improves the applicability of network-intrusion detection systems. More specifically, we develop feature engineering methods that transform IP-flows into device-level data points, define distance metrics between the data points, and apply the DBSCAN algorithm on them. Our experiments on a simulated IoT device network with varying levels of noise show that our proposed methodology outperforms alternative methods and is the only one producing a robust grouping of the IoT devices with noise present in the traffic data.
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