The paper addresses the topic of radar echo temporal extrapolation which is of major interest in both operational and research meteorology. Weather radar measurements are an important data source used by operational meteorologists for weather analysis, radar refectivity having a significant influence on short-term heavy rainfall prediction. Thus, extrapolating radar products’ values is important for early storm evolution assessment. The paper proposes SepConv-ens approach for temporal extrapolation of radar observations using an ensemble of three separable convolution-based deep learning models. Experiments performed on real radar data from the Romanian National Meteorological Administration (NMA) highlight a good performance of SepConv-ens in predicting radar data up to more than 40 minutes ahead and a good correlation between the radar measurements and the predictions in terms of spatial and intensity evolution of the radar echoes. SepConv-ens is integrated in the operational visualization software utilized by the Romanian NMA and is the first attempt, at the national level, to offer an artificial intelligence-based automated assistance for operational meteorologists.

Software deffect prediction, a problem of major relevance within the search-based software engineering field, aims to enhance software quality by early and precisely uncovering faulty software modules. Accurate detection of software defects in new software releases might increase the performance of the software development process in terms of cost, time and software quality. Most approaches from the software deffect prediction literature try to develop general solutions that are designed to work with any type of software deffect. From a software engineering perspective, software defects may take various forms and identifying/fixing different types of defects requires different approaches. Starting from the assumption that specific types of software defects have a particular behaviour, we are introducing in this paper, as a proof of concept, an unsupervised learning-based methodology for mining behavioural patterns for specific classes of software defects and identifying features which would be relevant for detecting the uncovered classes. The experiments performed on an open-source software deffect prediction data set collected from all releases of the Apache Ivy software highlight that the results obtained by applying the proposed methodology are highly correlated with the way human domain experts categorise and address software defects. Creating software deffect prediction models that are specifically tailored for different software deffect types may improve the accuracy of the developed models, open the possibility to apply different sets of predictive models based on the domain of the software and may accelerate the adoption of software deffect prediction approaches by the industry.

Software authorship identification has a major relevance in software development, as it can be of help in detecting potential problems with the source code, identifying false reporting of authorship within a project and it can contribute to the improvement of the code review and testing effort prioritization. This paper introduces SoftId, a one-class classification model. The model is trained to encode and recognize the programming style of a given set of software developers, thus gaining the ability to detect whether a certain source code is authored by a developer from the original set or it is an “unknown” software developer. The novelty of the SoftId classifier resides in solving the software authorship identification task in an open-set configuration using a deep autoencoder and based on textual representations of the source codes. The experimental results highlight a significant improvement of SoftId compared to One-class Support Vector Machine classifier used in the literature for anomaly and novelty detection.

The paper approaches the topic of nowcasting, one of the hottest topics in meteorology which deals with the problem of short-term forecasting of severe weather phenomena. Various types of meteorological data, including radar measurements, satellite data and weather stations’ observations are currently used for forecasting severe weather events. Radar data is one of the important sources used by meteorologists for nowcasting and for providing alerts for severe weather events. We are proposing a new one-class classifier, named RadRAR (Radar products’ values prediction using Relational Association Rules) for convective storms nowcasting based on radar data. More specifically, RadRAR is trained on radar data collected from normal weather conditions and learns to predict whether the radar echo values will be higher than 35dBZ, i.e. likely to indicate the occurrence of a storm. RadRAR is intended to be a proof a concept that relational association rule mining applied on radar data is helpful in discriminating between severe and normal weather conditions. Real radar data provided by the Romanian National Meteorological Administration is used for evaluating the effectiveness of RadRAR. A Critical Success Index of 61% was obtained, outperforming similar approaches from the literature and highlighting a good performance of RadRAR.