What MTBF Measures in Software Reliability Engineering
Knowing what MTBF leaves out matters more than the number itself.
Deshawn Calloway
Staff Writer
Deshawn Calloway
Deshawn covers the emerging intersection of machine learning tooling and software operations, with a particular focus on autonomous remediation systems and the trust questions they raise for on-call engineers. Before writing full time, he worked as a platform engineer and later a developer advocate, giving him fluency in both the tooling and the teams adopting it.
11 stories
Incident Management Roles and Responsibilities in On-Call Engineering
Clear roles before the alert fires prevent chaos when production breaks at 2 AM.
Deshawn Calloway
Autonomous Root Cause Analysis in Distributed Systems
Automated systems now pinpoint the actual failing service instead of chasing downstream symptoms.
Deshawn Calloway
How to Instrument Regression Detection Into a Deployment Pipeline
Catch regressions automatically in production before they harm users and drain engineering time.
Deshawn Calloway
Regression Testing vs Unit Testing for Backend Services
Unit tests catch logic errors; regression tests catch integration failures that unit tests miss.
Deshawn Calloway
Regression Testing vs Functional Testing in CI Pipelines
Confusing functional and regression testing is why pipelines pass while production breaks.
Deshawn Calloway
Real-Time Anomaly Detection on Production Metrics After Deploy
Catch regressions introduced by new deployments before they become production incidents.
Deshawn Calloway
Runbook vs Playbook in Incident Response
Runbooks execute single procedures; playbooks orchestrate decisions across teams during incidents.
Deshawn Calloway
What a Runbook Is in Production Engineering
A runbook is the difference between improvisation under pressure and a procedure proven to work.
Deshawn Calloway
Alert Fatigue Root Causes in Backend Engineering Teams
Most backend teams field thousands of alerts weekly, but only a fraction demand immediate action.
Deshawn Calloway
Time Series Anomaly Detection for Deployment Regression
Adaptive models beat fixed thresholds at catching deployment regressions early.
Deshawn Calloway