In this blog we discuss:
- How automated testing suites overcome the complexity of real trading environments
- The trading system pre-requisites before utilising AI and ML in software testing
- The benefits of using AI and ML tools in trading systems
There is no doubt that financial markets are increasing in complexity. Much of this complexity is fuelled by the digital transformation and adoption of emerging technologies that are at the top of many financial institution’s strategic agendas.
Introducing machine learning (ML), artificial intelligence (AI), and automation to trading software is completely revolutionising the way traders and technology providers can run testing processes on their modern trading systems.
Technology advances come with their risks, however, and the adoption of AI and ML can increase the complexity of business workflows and decrease transparency.
Unfortunately, current software testing practices do not capture the complexity of modern-day real trading environments. For example, software testing is not able to replicate the actual use cases of traders and lacks their know-how of using trading platforms, so manual input is required. This is even the case for some automated testing processes, resulting in them becoming labour intensive and costly.
An effective automated test harness should fully simulate the trading environment, thereby removing the need for manual validation at any step in the workflow.
The use of trading technologies, particularly those utilising highly automated or AI/ML-driven processes, should therefore have comprehensive automated testing suites.
Requirements for utilising AI and Machine Learning in software testing
Testing sophisticated trading systems should be data-driven. This can be achieved by capturing the data, storing it, providing secure access, and processing it to generate efficient machine-driven libraries for testing all essential components, to generate insights, and automatically replicate the human-machine interaction.
This ability to harness the data generated during testing to automatically create new test data is the driving force behind the next generation of multi-layered automation testing.
Another requirement is having an open interface in different protocols that will enable concurrent testing through a variety of system interfaces, including simulating GUI and API interactions.
AI and ML will not replace human testers in quality assurance (QA) testing, but it will augment them by providing valuable data insights and helping in the decision-making process. The volume and speed of testing can also be significantly increased through the adoption of these technologies.
Innovation for reducing costs and overall complexity
AI and ML tools in testing can be used in trading systems to look at the behaviour of traders in real-time, via the captured data from multiple interfaces, and then simulate this behaviour accurately in a testing environment.
The benefit of imitating reality in a testing environment, both in positive and negative testing, is that ML algorithms can find errors through self-learned test cases, instead of a large team of traders and other testers being committed to every product release and update.
Financial institutions (FIs) are looking for a consolidated solution that diagnoses common errors; improves testing cycles and test cases, and identifies negative patterns that can risk software stability.
ML algorithms boost software testing practices
| wdt_ID | Testing Practice | ML Algorithm Benefit |
|---|---|---|
| 1 | Root Cause Analysis | Traders can easily break down the sequence of events in an application error and accurately pinpoint where there are coding issues. Using a pre-processed dataset of test execution reports, ML algorithms detect the functional area of the defect and help rule out false-negative test results. ML techniques can also be applied to defect reports filed by the testers, enabling prediction of the bug’s severity and time needed to fix it. |
| 2 | Regression Testing | Enables testers to work with large volumes of data and generate new smart scenarios based on the models and data extracted from the system under test or synthetically created to enhance test coverage. |
| 3 | Consistent Testing | Applies process mining approaches to look for discrepancies in transactional data in different environments (test vs production) or in different release versions of the system under test. |
| 4 | Automated validation UI testing | Finds visual elements in the app GUI without being attached to a code behind these elements. |
Automate trading software with Quod’s ML capabilities and th2
Exactpro has teamed up with Quod Financial to implement the first use of AI/ML-driven test automation for an execution and algo trading software provider.
Using real scenarios, including algorithmic trading, market making, and smart order routing, Quod has been able to perform 4,000 real-time end-to-end tests per second, per release.
This will offer Quod’s clients an automated and integrated testing environment where real scenarios can be used for Quod trading. With these tools, Quod’s clients can upgrade more frequently, develop algos more confidently, and focus more on innovation.
Read the press release: utilising a data-driven approach to software testing
What is AI in software testing?
