Software testing is an information service. Its goal is to provide stakeholders with objective information about the defects present in their system.
The effectiveness of an information service can be judged based on its accuracy, relevance and accessibility. Improving software testing implies making it progressively better at detecting and interpreting defects, whilst reducing the timeframes and costs.
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With the widespread growth of available digital data and computational capabilities, we are seeing the use of subsymbolic AI deliver improvements in autonomy and efficiency across many industries, including software testing. Exactpro’s new AI approach uses big data analytics to enhance the generation of test data and test scenarios as well as the interpretation of test results. On top of that, smart execution capabilities can support full-scale AI adoption in software testing and in the finance ecosystem.
With new technologies gaining more adoption among the finance space firms, industry players need to evolve towards a better understanding of their technology assets, rising up to the challenges associated with new levels of complexity and related risks.To innovate responsibly and confidently, financial services firms need to adopt higher standards of quality assurance for their platforms. Software testing frameworks should meet new technology requirements and mitigate new risks in a smarter way.
Our AI testing approach is implemented in our most recent test framework – th2-shark. th2-shark uses artificial intelligence (AI) and machine learning (ML) algorithms to streamline protocol-based software testing in the finance space to provide stakeholders with objective information about the defects present in their system faster and more efficiently. Applying AI to automate software testing processes provides a new level of efficiency and test coverage for numerous business cases in the financial services industry, strengthening the operational resilience of the ecosystem.