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Detect anomalies in software stacks prior to deployment

Detect anomalies in software stacks (e.g. security or downtime risks). Fro example, banks tend to have complicated legacy systems based both on multiple generations of development and software codes but also driven by historic merger activity in the industry.

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Risk reduction

Risk reduction - Maximise system uptime

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Operational

Operational - Increased machine uptime

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Operational

Operational - Faster system build / test / deploy

£

Cost

Cost - Job automation

Description

Detect anomalies in software stacks prior to deployment

Technology & data

*** check categry and function

Linked Case Studies

H&R Block improves filing acceptance rates by 10% by monitoring the performance of its online tax filing application and user experience using machine learning

ReTest automates testing of Java based software application GUI with 82% accuracy using artificial neural networks and machine learning

Trek Bicycle reduces error rates in its website and bike sharing app services from 8-14% to <1% using machine learning to monitor anomalies in real-time

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Editor: sdg