Abstract
In today’s software industry, systems are constantly changing. To maintain their quality and to prevent failures at controlled costs is a challenge. One way to foster quality is through thorough and systematic testing. Therefore, the definition of adequate tests is crucial for saving time, cost and effort. This paper presents a framework that generates software test cases automatically based on user interaction data. We propose a data-driven software test generation solution that combines the use of frequent sequence mining and Markov chain modeling. We assess the quality of the generated test cases by empirically evaluating their coverage with respect to observed user interactions and code. We also measure the plausibility of the distribution of the events in the generated test sets using the Kullback-Leibler divergence.

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ECIR 2020 1 was one of the many conferences affected by the COVID-19 pandemic. The Conference Chairs decided to keep the initially planned dates (April 14-17, 2020) and move to a fully online event. In this report, we describe the experience of organising the ECIR 2020 Workshops in this scenario from two perspectives: the workshop organisers and the workshop participants. We provide a report on the organisational aspect of these events and the consequences for participants. Covering the scientific dimension of each workshop is outside the scope of this article. A detailed account of the main conference can be found in the Spring 2020 edition of BCS Informer [BCS, 2020].

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Data are growing fast in today's world and great portion of that is in the form of stream. In many situations, data streams are imbalanced making it difficult to use with classical data mining methods. However, mining these special kinds of streams is one of the most attractive research area. In this paper, we propose two algorithms for learning from imbalanced regression data streams. Both methods are based on Chebychev's inequality but in a different way. The first method, under-samples from the frequent target value examples while the second method over-samples the rare and extreme target value examples. This way, the learner will focus in the rare and more difficult cases. We applied our methods to train regression models using two benchmark datasets and two well-known regression algorithms: Perceptron and FIMT-DD. Our obtained results from the simulations indicate the usefulness of our proposed methods.