Automatic crash reporting systems have become a de-facto standard in software development. These systems monitor target software, and if a crash occurs they send details to a backend application. Later on, these reports are aggregated and used in the development process to 1) understand whether it is a new or an existing issue, 2) assign these bugs to appropriate developers, and 3) gain a general overview of the application's bug landscape. The efficiency of report aggregation and subsequent operations heavily depends on the quality of the report similarity metric. However, a distinctive feature of this kind of report is that no textual input from the user (i.e., bug description) is available: it contains only stack trace information. In this paper, we present S3M ("extreme") - the first approach to computing stack trace similarity based on deep learning. It is based on a siamese architecture that uses a biLSTM encoder and a fully-connected classifier to compute similarity. Our experiments demonstrate the superiority of our approach over the state-of-the-art on both open-sourced data and a private JetBrains dataset. Additionally, we review the impact of stack trace trimming on the quality of the results.

R-tree is a data structure used for multidimensional indexing. Essentially, it is a balanced tree consisting of nested hyper-rectangles which are used to locate the data. One of the most performance sensitive parts of this data structure is its split algorithm, which runs during node overflows. The split can be performed in multiple ways, according to many different criteria and in general the problem of finding an optimal solution is NP-hard. There are many heuristic split algorithms. In this paper we study an existing k-means node split algorithm. We describe a number of serious issues in its theoretical foundation, which made us to re-design k-means split. We propose several well-grounded solutions to the re-emerged problem of k-means split. Finally, we report the comparison results using PostgreSQL and contemporary benchmark for multidimensional structures.