Test Observability & Engineering effectiveness

Observability, as a concept, has been around for a while and has its roots in fields like control theory and systems engineering. It became more prominent in the context of software systems as organizations started dealing with increasingly complex and distributed systems.

The ability to measure a system’s state based on the data it generates, such as logs, metrics, and traces. It helps IT teams understand and improve performance, user experience, and business outcomes in complex cloud environments.

We invest considerable effort in crafting numerous tests to uphold the quality of our software systems, spanning from unit, component, integration, contract, functional, to non-functional tests, and beyond. These tests serve as the safety-net for the system’s state.

As software systems evolve and grow in complexity, we continually augment our test suite to ensure their desired functionality. However, as the number of tests and executions proliferate, managing and maintaining these test assets becomes an enormous challenge. Over time, these once-vital test assets transform into adversaries, impeding efficiency rather than facilitating quicker releases.

What we require is a mechanism that comprehensively analyzes these test assets, execution results, and historical data to generate actionable insights. Such insights are instrumental in ensuring that your test assets not only endure but consistently contribute value to your development and release processes.