Technical insights, stories and opinions from our team of consultants, analysts, developers and testers.

A codebase with 100% code coverage can still let a simple logic bug through, because coverage only shows which lines ran during a test, not whether a test would notice if they changed. This piece looks at why coverage falls short as a single measure of test quality, how mutation testing with Stryker.NET exposes weak tests and what a run against a production codebase of around 5,000 unit tests revealed.

Senior Test Engineer, Roberta Lo Mastro, shares her journey from studying pharmacy in Italy to testing at Audacia, the projects she's enjoyed most and what she loves about the culture.

When something goes wrong in production, there are no breakpoints or debuggers to step through - the only evidence available is what the system was deliberately built to record. This piece looks at what makes a log message useful, how to query logs effectively using Azure Application Insights as an example and how alerting builds on a foundation of good logging.

Lead Data Scientist Chris Bentley discusses his route into data science, the variety of projects he works on at Audacia, and why he considers himself as much a consultant as a data scientist these days.

In our latest team story, People Manager Charlotte Dyer talks us through her role, chairing Audacia's working groups, and how she believes Audacia is working to improve diversity, wellbeing and community connection.

Sumaira Ahmed shares her journey from Audacia Academy graduate to Software Engineer - covering her day-to-day work, client projects in financial services and the technical community that makes Audacia a great place to grow.

Discover why measuring Engineer Experience matters and how to do it effectively – exploring how people, processes and tools shape productivity across software engineering teams.

Amund Farifteh, Lead Delivery Manager at Audacia, discusses his journey from concept art and gaming into delivery management, the variety of projects he's worked on, and why Audacia's culture of continuous improvement suits him well.

Rolling out AI to engineering teams successfully depends on three interconnected phases: establishing solid foundations, executing a structured rollout and committing to continuous improvement. These observations come from our internal AI deployment at Audacia and supporting other organisations through similar transformations.