Elevate Your Code Quality with Harness SEI: Mastering the Art of Software Excellence

Introduction to SEI’s Transformative Power

In the ever-evolving landscape of software development, the significance of producing high-caliber code is undeniable. This is where Harness Software Engineering Insights (SEI) shines, guiding teams toward elevated software quality, enhanced productivity, and overall excellence. Here, we delve deep into the pivotal role of SEI's Quality Module in aiding teams to gauge, supervise, and uplift their code quality.

Understanding SEI's Quality Module

The Trellis Framework: At the heart of SEI's transformative potential is the industry-validated Trellis Framework. This intricate design provides a comprehensive analysis of over 20 factors from various Software Development Life Cycle (SDLC) tools, enabling teams to efficiently track and optimize developer productivity.

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Measuring Product Quality with SEI

Lagging Indicators: Retrospective Excellence

Lagging indicators are retrospective measures, offering insights into past performance. Let's break down these metrics:

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Defect Escape Rate: This metric, crucial for understanding production misses, measures the percentage of defects that go undetected during production and reach the customer. A higher defect escape rate can signal poor quality control, leading to customer dissatisfaction.

Escapes per Story Point or Ticket: This indicates the number of defects per unit of work delivered. An elevated number here can point to quality lapses in development.

Change Failure Rate: This metric measures the percentage of changes leading to failures, indicating the robustness of the product.

Severity of Escapes: This highlights the seriousness of defects, with higher severity demanding urgent attention.

APM Tools - Uptime: Measuring product availability and performance, a higher uptime percentage is indicative of good product quality.

Customer Feedback: Direct customer feedback, both positive and negative, provides valuable insights into product quality.

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Leading Indicators: Proactive Quality Assurance

Leading indicators predict current or future performance. We explore these further:

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SonarQube Issues: This includes code smells, vulnerabilities, and code coverage. Issues flagged here can indicate quality concerns in the codebase. 

Coverage % by Repos: Evaluating code coverage percentage across various repositories.

Automation Test Coverage: A higher percentage here suggests a robust, reliable product.

Coding Hygiene: Measures such as code reviews and comments improve code maintainability and reduce defect risks.

Program Hygiene: This includes acceptance criteria and clear documentation to ensure the product meets requirements.

Development vs Test Time Ratio: A balanced ratio is crucial for product quality.

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Test Metrics

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Automated Test Cases by Type: Categorizing test cases into functional, regression, performance, or destructive types.

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Test Cases by Scenario: Differentiating between positive or negative scenarios.

Automated Test Cases by Component View: Providing a component-wise breakdown.

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TestRail Test Trend Report - Automation Trend: Showcasing the trend of total, automated, and automatable test cases.

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Structured Engineering Improvements

SEI's architecture integrates with CI/CD tools, offering over 40 third-party integrations. This structured approach aids in goal-setting and decision-making, driving teams towards engineering excellence.

Resource Optimization for Efficiency

Beyond metrics, SEI assists in resource allocation optimization, aligning resources with business objectives for efficient project delivery.

Centralized Visibility through Dashboards

SEI’s dashboards provide a holistic view of the software factory, highlighting key metrics and KPIs for better collaboration and workflow management.

Conclusion

Harness Software Engineering Insights, with its Quality module, stands as a beacon for development teams, combining metrics, insights, and tools for superior code quality. To learn more, schedule a demo with our experts.

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