Collaborative learning in groups has practical and pedagogical benefits. In software engineering education, the group formation stage can have an important influence on the quality of learning and students' satisfaction with their group. Knowledge of student experiences and preferences regarding group formation can inform course design and hopefully lead to better learning outcomes and student satisfaction. In this paper, we report experiences of group formation in software engineering courses, focusing on students' perspectives. We used surveys administered on three master's level software engineering courses, through which we explored students' prior experiences with group work in educational settings and their perspectives on how groups should be formed. We present results on students' thoughts about who should form groups, what information criteria they would consider as relevant when forming groups, and what kind of group formation strategy they prefer. Our results indicate that students preferred to keep the group formation process in their own hands, that they considered grade ambitions, educational background, and time availability to be important factors to consider, and that they generally preferred the formation strategy already used on the courses they attended. We also discuss how students' prior experiences may have coloured their perceptions of group formation strategies and the ethical dilemmas facing instructors when choosing different strategies. We present points of consideration for teachers who are interested in improving group formation on their software engineering courses.

Software quality sits at the core of software engineering as a discipline. Yet, although each university software-engineering and the software-development course covers software quality to some extent, practitioners still lament on graduates’ readiness for practise for this very reason—poor quality of their code. As a result, we have engaged university industrial partners in designing a master-degree Software Quality course that puts the key software quality topics in one place.

Software Reliability Growth Models (SRGMs) are based on underlying assumptions which make them typically more suited for quality evaluation of closed-source projects and their development lifecycles. Their usage in open-source software (OSS) projects is a subject of debate. Although the studies investigating the SRGMs applicability in OSS context do exist, they are limited by the number of models and projects considered which might lead to inconclusive results. In this paper, we present an experimental study of SRGMs applicability to a total of 88 OSS projects, comparing nine SRGMs, looking at the stability of the best models on the whole projects, on releases, on different domains, and according to different projects’ attributes. With the aid of the STRAIT tool, we automated repository mining, data processing, and SRGM analysis for better reproducibility. Overall, we found good applicability of SRGMs to OSS, but with different performance when segmenting the dataset into releases and domains, highlighting the difficulty in generalizing the findings and in the search for one-fits-all models.

Data analytics and data science play a significant role in nowadays society. In the context of smart grids, the collection of vast amounts of data has seen the emergence of a plethora of data analysis approaches. In this article, we conduct a systematic mapping study aimed at getting insights about different facets of SG data analysis: application subdomains (e.g., power load control), aspects covered (e.g., forecasting), used techniques (e.g., clustering), tool support, research methods (e.g., experiments/simulations), and replicability/reproducibility of research. The final goal is to provide a view of the current status of research. Overall, we found that each subdomain has its peculiarities in terms of techniques, approaches, and research methodologies applied. Simulations and experiments play a crucial role in many areas. The replicability of studies is limited concerning the provided implemented algorithms, and to a lower extent due to the usage of private datasets.

Reliability is an essential attribute of mission-and safety-critical systems. Software Reliability Growth Models (SRGMs) are regression-based models that use historical failure data to predict the reliability-related parameters. At the moment, there is no dedicated tool available that would be able to cover the whole process of SRGMs data preparation and application from issue repositories, discouraging replications and reuse in other projects. In this paper, we introduce STRAIT, a free and open-source tool for automatic software reliability growth analysis which utilizes data from issue repositories. STRAIT features downloading, filtering and processing of data from provided issue repositories for use in multiple SRGMs, suggesting the best fitting SRGM with multiple data snapshots to consider software evolution. The tool is designed to be highly extensible, in terms of additional issue repositories, SRGMs, and new data filtering and processing options. Quality engineers can use STRAIT for the evaluation of their software systems. The research community can use STRAIT for empirical studies which involve evaluation of new SRGMs or comparison of multiple SRGMs.

One of the main goals of the power distribution utilities is to provide stable supply of the power load. The growing popularity of smart grids, i.e. power grids enhanced with modern ICT, opened new possibilities to make the grid more efficient, secure and reliable. However, by introducing new elements into the infrastructure, such as small-scale photovoltaic power plants, the management of power load is becoming more challenging.

In recent years, we have been witnessing profound transformation of energy distribution systems fueled by Information and Communication Technologies (ICT), towards the so called Smart Grid. However, while the Smart Grid design strategies have been studied by academia, only anecdotal guidance is provided to the industry with respect to increasing the level of grid intelligence. In this paper, we report on a successful project in assisting the industry in this way, via conducting a large anomaly-detection study on the data of one of the power distribution companies in the Czech Republic. In the study, we move away from the concept of single events identified as anomaly to the concept of collective anomaly, that is itemsets of events that may be anomalous based on their patterns of appearance. This can assist the operators of the distribution system in the transformation of their grid to a smarter grid. By analyzing Smart Meters data streams, we used frequent itemset mining and categorical clustering with clustering silhouette thresholding to detect anomalous behaviour. As the main result, we provided to stakeholders both a visual representation of the candidate anomalies and the identification of the top-10 anomalies for a subset of Smart Meters.