The research aims to decrease maintenance costs and improve software quality via ML-enabled analysis of bug report descriptions. Feature selection helps better predict resolution times for various purposes.
This paper presents Petri net models assembling crucial processes executed within order-driven systems: order submission, application of precedence rules, and the order matching mechanism.
The paper presents an overview of a test automation framework aimed at end-to-end functional and non-functional testing of DLT-based hybrid financial software for post-trade. The proposed solution comprises the components designed for testing user-facing parts of the SUT as well as business logic specific for different DLT-based architectures. This combined approach is seen as a viable solution of the problem of the SUT complexity as well the variety of possible DLT architectural decisions.
Testing of distributed systems is a complex task, which is hampered by the impossibility of guaranteed reproduction of errors associated with race conditions. Even minor instrumentation of the system significantly changes its characteristics, which becomes critical, especially for load testing. All of that increases the importance of quality control methods based on the system log analysis. In this paper, we present our experience of semi-automated analysis of the behavior of clearing and settlement system by utilizing its logs for the purpose of identifying and classifying errors.
Market surveillance systems, used for monitoring and analysis of all transactions in the financial market, have gained importance since the latest financial crisis. Such systems are designed to detect market abuse behavior and prevent it. The latest approach to the development of such systems is to use machine learning methods. The approach presents a challenge from the standpoint of quality assurance and the standard testing methods. We propose several types of test cases which are based on the equivalence classes methodology. The division into equivalence classes is performed after the analysis of the real data used by real surveillance systems. This paper describes our findings from using this method to test a market surveillance system that is based on machine learning techniques.
Olga Moskaleva, Researcher, Exactpro, London Stock Exchange Group
Anna Gromova, Researcher, Exactpro, London Stock Exchange Group
This paper describes the concept of reconciliation testing - a process of using data reconciliation tools to validate the system in parallel with other activities. The authors studied information about two major software failures in electronic trading area: Facebook IPO on NASDAQ and Knight Capital runaway algorithms. This paper contributes to the subject matter by identifying aspects related to data reconciliation during these two events. The authors discuss the balance between automated and manual reactions to discrepancies reported by reconciliation tools and analyze the necessity of introducing reconciliation testing as part of system development life cycle for complex transactional processing systems.
Anna-Maria Kriger, Kostroma State Technological University
Alyona Pochukalina, Obninsk Institute for Nuclear Power Engineering
Vladislav Isaev, Yuri Gagarin State Technical University of Saratov
