Key Metrics Engineers Should Analyze

Metrics that Tune Systems and Tame Technical Debt In the realm of software engineering, orchestrating success requires more than just coding prowess. Here are pivotal metrics to fine-tune your systems and tame technical debt: 1. Code Quality Metrics: Like artisans scrutinizing every brushstroke on a canvas, engineers analyze code complexity and duplication. Each line must harmonize to compose a masterpiece. 2. Test Coverage: Imagine automated tests as a safety net beneath a high-wire act. Comprehensive coverage ensures confident performances with each deployment. 3. Deployment Frequency: Picture an orchestra performing regularly—a testament to refined synchronization and delivery precision. 4. Mean Time to Detect (MTTD) and Mean Time to Resolve (MTTR): Fast responses to system issues prevent them from spreading like wildfires. Quick action extinguishes challenges before they engulf productivity. 5. Technical Debt Ratio: Like tending a garden, regular maintenance (refactoring) ensures weeds (inefficiencies) don't stifle growth. Pruning fosters a robust environment for innovation. 6. System Performance: Endurance akin to a marathon runner—monitoring response times and throughput sustains peak operational efficiency. 7. Sprint Burndown: A project fitness tracker—consistently meeting targets signifies disciplined progress towards sprint goals. 8. Customer Satisfaction (CSAT): A compass guiding software evolution—high scores validate user-centric enhancements, steering teams towards continuous improvement. Focusing on these metrics empowers engineering teams to cultivate resilient systems, navigate technical debt, and elevate product excellence. What metrics propel your team's success? #EngineeringMetrics #TechnicalDebt #SoftwareExcellence #ContinuousImprovement

🏗️ Why every Civil Engineer should understand descriptive data measures? . As civil engineers, we’re often on the frontlines of solving real-world challenges—designing infrastructure, ensuring safety, and building for climate resilience. But before we pour concrete or draft blueprints, there’s one essential step we sometimes overlook: understanding the data that drives our decisions. . Recently, I reviewed a table summarizing key numerical descriptive measures—and it struck me just how foundational these are to what we do. Whether you’re analyzing soil samples, structural load tests, traffic flow, or climate risks, these statistical tools help us interpret complexity with clarity. . Location metrics like mean, median, and mode tell us where the heart of our data lies—crucial when estimating average daily traffic or water demand across communities. . Dispersion tools such as standard deviation, IQR, or variance show how spread out the data is—vital when assessing variability in material strength, rainfall patterns, or construction costs. . Symmetry helps us detect bias or imbalance—think about load distribution on a bridge or skewed settlement patterns in urban expansion. Measures like skewness and MedCouple allow us to flag inconsistencies early. . Shape tells us how “peaked” or “flat” the data distribution is—important when modeling extreme events like floods or traffic surges. Kurtosis and modality measures (how many peaks in a dataset) help us prepare for the unexpected. . In a time where data is more available than ever—from smart sensors to GIS to structural monitoring—our ability to interpret and communicate it is what sets great engineers apart. . Let’s make data part of our engineering language. . How are you using data in your projects? #CivilEngineering #DataDrivenDesign #EngineeringStorytelling #InfrastructureResilience #ConstructionInnovation #DescriptiveStatistics #SmartCities #EngineeringLeadership #SustainableDevelopment

For a successful 📊 performance test, we simulate the expected usage volume (business metric), measure response times (technical metric), and validate if problem alerting during overload situations (operational metric) works as intended. Technical metrics describe how a system should behave in certain situations. Some examples of technical metrics are: + Response times + Resource utilization + Throughput rate + Error rate + Processing time of batch processes + Software metrics such as code complexity Business metrics describe what a system should support. Some examples of business metrics are: + Number of concurrent users + Number of users authorized to access + Number of transactions under average and peak periods + Service Level Agreement Breach or Compliance + Efficiency of business processes Operational metrics do not directly affect the end-user experience, but in terms of a holistic simulation of production conditions, we include them in our performance test experiments. Some examples of operational metrics are: + Time to start up the application + Time to stop the application + Backup times + Duration of a data restore + Problem detection + Alerting behavior As a performance engineer, you should remember the entire metric family to make your load and performance test successful. I look forward to your questions or comments 😊 #performanceengineering #loadtesting #performancemetrics #performancetesting https://lnkd.in/eaB5rtNk