White papers

Deliberate Practice of Software Testing in an Agile World

Exactpro is an open access software testing subsidiary of the London Stock Exchange Group.
Exactpro provides functional and non-functional testing services to the Group’s own markets, as well as other exchanges, market infrastructures, banks and financial firms worldwide. Fully dedicated to the quality assurance of FinTech software, the company employs one in eight of the LSE staff.

Iosif Itkin, CEO, Exactpro, London Stock Exchange Group

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CLA-2016: Using intelligent systems and structural analysis to assure orderly operations of the modern trading and exchange platforms

The sequence of operations is an important financial subject that has attracted the researchers' attention for many years. The quality of electronic trading platforms can be improved by validating the sequence of operations via two modes. The first one is detection and prevention of abusive behavior in the market. Surveillance systems ensure the monitoring of this parameter. The second one is verification of technical stability. Defect management is an essential part of improving software reliability by means of test tools. We propose that using data analysis and artificial intelligence can help significantly improve the quality of electronic trading.

Anna Gromova, Researcher, Exactpro, London Stock Exchange Group
Olga Moskaleva, Researcher, Exactpro, London Stock Exchange Group

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EXTENT-2016: Managing QA for Complex Systems in Agile Development Framework

QA, as we see it, is a continuous learning process. It is about learning how the system should operate in production, followed by learning the actual behavior of the system by way of performing tests and, after comparison, ensuring that all the discrepancies are either fixed or documented. It is the epitome of all QA activities. The other essential task that QA performs is developing the Test Design, coming up with Test Tools and Automated Test Libraries, which is an ongoing process, as opposed to learning at the beginning and testing as the end result. We are still learning when the system goes into production and still receive feedback from it. That allows us to reveal as many issues as possible.

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