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The Coming Divide in Defense: Federation or Fallout Under DoDI 5000.97

Share: The defense industry is entering a pivotal phase of transformation. With the release of DoDI 5000.97, the U.S. Department of Defense (DoD) has made

Top 12 Software Quality Metrics that Actually Matter (Expert Insights)

Read this blog to get an in-depth understanding of the right software quality metrics to measure and improve your software quality.

August 28, 2024
Muskaan Pathak

Introduction

The software industry is experiencing unprecedented growth, driven by digital transformation. Software quality has thus become a strategic imperative.

The 15th World Quality Report underscores this shift, highlighting the growing emphasis on quality engineering and its integration into core business operations.

With a focus on delivering value rather than volume, 67% of companies are prioritizing quality assurance (QA) as a cornerstone of their operations.

To thrive in today’s quality-first software landscape, a lot comes down to setting the right benchmarks for measuring success.

In this blog, we take a deep-dive to bring you up to speed with the software quality metrics that you should essentially measure – and the critical need for Quality Gap Intelligence to maximize your end-to-end SDLC potential.

The 3 Cs of Software Quality

The three core dimensions of software quality are:

Strategies to Improve Software Quality

To achieve optimal software quality, organizations must adopt a holistic approach that incorporates the following strategies:

Test Early (Shift Left): Incorporate testing early in the development cycle to prevent defects from propagating downstream.
Integrated Quality Gates: Establish checkpoints throughout the SDLC, CI/CD pipelines to ensure quality standards are met at every stage.
Risk-Based Test Optimization: Prioritize testing efforts based on potential impact and risk to enhance efficiency.

Pro Tip: To achieve a truly holistic view of software quality, it’s essential to embed quality metrics directly within existing development and work management tools. By visualizing quality data alongside development progress, teams can proactively address quality issues from the get-go.

Core Software Quality Metrics You Need to Measure

1. Test Coverage

What is it

The percentage of code executed by test cases.

Calculation

Number of lines of code covered by tests / Total number of executable lines of code

Interpretation

Higher coverage generally indicates better test effectiveness, but it doesn’t guarantee quality. Aim for high coverage in critical areas.

Improvement Strategies

Prioritize test case creation for uncovered areas, use code coverage tools, and refactor code for better testability.

Pro Tip: Measure test coverage for new or modified code. This targets testing efforts on areas most likely to introduce defects, reducing overall test execution time as well.

2. Defect Density

What is it

The number of defects found per file/module/feature.

Ways to measure

Defect density can be calculated per kilocycle of logic (KLOC) or per thousand lines of code (LOC). For example, if a software module has 1,000 lines of code and 10 defects, the defect density is 10/1,000 = 0.01 defects per KLOC.

Industry standard for defect density:

1 Defect/1000 Lines of Code.

Interpretation

High defect density indicates potential issues with requirements or testing. It helps product teams determine which features to release based on risk.

Improvement Strategies

Clarify requirements, enhance test case design, and conduct early defect prevention activities.

Pro Tip: By incorporating work item IDs into commit messages, software teams can trace the number of times a file and a line was touched for ‘Bug’ type workitems.

This practice establishes a direct link between code changes and the corresponding defects or user stories, allowing for efficient root cause analysis.

3. Defects per Software Change

What is it

The number of defects introduced per code change.

Calculation

Number of defects / Number of code changes.

Interpretation

High defect rate per change indicates potential coding or testing issues.

Improvement Strategies

Conduct thorough root-cause analysis, increase unit test coverage, and adopt intelligent impact analysis to identify change-caused defects early.

Pro Tip: Get defects per change by team, module, developer, reviewer, etc. The best way to ensure traceability is by linking work items to defects on a project, portfolio level.

4. Test Effort and Reliability or Cost per Test

What is it

Test effort refers to the overall resources (time, personnel, tools) invested in the testing process. It encompasses activities like test planning, design, execution, and analysis.
Test reliability means the consistency and dependability of test results.

Calculation

Average time per test case, test case pass rate, average bugs per test.

Interpretation

High test effort or low reliability indicates potential inefficiencies or test case issues.

Some questions to ask to measure test reliability:

Are test results consistent across multiple executions under the same conditions?
Do failures in certain test cases affect the reliability of other tests?
Are test cases prioritized effectively based on risk and coverage?
Is the test suite execution time within acceptable limits?
Is test data relevant, accurate, and easily accessible?

Pro Tip: With Requirement-Test traceability – Measure test effort and test reliability by product modules, key functionalities, by product teams etc.

5. Test Case Effectiveness

What is it

The ability of test cases to identify defects.

Calculation

(Number of defects found by test cases / Total number of test cases) * 100

Interpretation

Low effectiveness indicates poor test case design or inadequate test coverage.

Improvement Strategies

Improve test case design, enhance test case review, and incorporate user feedback.

Pro Tip: Analyse test case history to understand how often test cases are revised, how often new test cases are added, etc.

6. Defect Leakage

What is it

Defect leakage is the percentage of defects that are not caught by the testing team but are found by end-users or customers after the application is delivered.

How to calculate defect leakage

(Total numbers of defects in UAT/ Total number of defects found before UAT) x 100.

Main causes for defect leakage

Insufficient code coverage, generic pass/fail tests, cutting corners while testing, missing test cases.

Improvement Strategies

Strengthen test coverage, improve test environment management, and conduct thorough production monitoring.

Interestingly, a study by IBM shows that the cost of fixing a defect multiplies as it progresses through the development lifecycle.

Design phase

The cost to fix a defect is typically around $1.

Testing phase

The cost to fix a defect jumps to over $10.

Post-release

Fixing a defect after the software is released can cost over $100.

This further emphasizes the critical importance of early defect detection and prevention.

Pro Tip: Analyze historical data to pinpoint areas where defects consistently slip through the testing net. Also, calculate the percentage of defects found in production compared to those discovered in pre-production environments.

7. DORA Metrics

DevOps Research and Assessment (DORA) has established a benchmark for measuring software delivery performance. Its four key metrics — deployment frequency, lead time for changes, change failure rate, and mean time to restore service — provide insights into a team’s speed, stability, and ability to recover from failures.

Deployment Frequency: The rate at which changes are deployed to production. This metric measures the team's ability to deliver value to customers frequently.
Lead Time for Changes: The average time it takes a code change to go from conception to production.
Mean Time to Recover (MTTR): The average time it takes to restore a service to its operational state after a failure. A lower MTTR indicates a more resilient system.
Change Failure Rate: The percentage of deployments that cause service failures requiring remediation.

Pro Tip: Make DORA metrics actionable by tracing lead time for changes, change failure rate and mean time to recover back to assignee, teams, product areas etc. Adjust risk indicators for tests and source code areas depending on history of changes that contributed to increased failure rate.

Advanced metrics you can measure to maximize software quality

8. Change Impact Analysis

Change impact analysis refers to how code alterations affect the system. It helps identify risks, manage dependencies, and ensure smooth deployments minimizing disruptions. Developers and testers can give an objective spin to test planning if they know how a change impacts existing functionalities.

9. Code Coverage for Impacted Functionalities

Change Impact focuses on ensuring that changes to the codebase do not negatively impact existing functionalities. To prioritize risky areas by measuring the coverage of impacted functionalities – teams can prioritize potential regression areas and reduce testing efforts accordingly.

10. Scope Churn for Release

Scope churn refers to the instability or frequent changes in project requirements or features during a release cycle. High scope churn can negatively impact project timelines, budgets, and quality.

11. Code Override Between Work Items

Code override occurs when multiple developers modify the same code section. This can increase the complexity of code changes and the potential for introducing defects.

12. Technical Debt Prioritization

Measures source code areas that are changed frequently, has long dev and test cycles, is touched by multiple developers, has history of high product defects and so on. Using this metric ensures you are investing in the most painful technical debt and prioritizing resources effectively.

Quality Gap Intelligence

Unified Software Quality Metrics to Improve Code Quality, Optimize Test Cycles, and Release Faster.

To effectively harness the power of these metrics and transform software quality, organizations need a unified, intelligent platform. This is where Quality Gap Intelligence (QGI) solutions like OpsHub Insights come into play.

OpsHub Insights empowers teams to:

Optimize E2E Test Coverage

Insights provides granular visibility into code changes and their associated impact. By measuring test coverage for both the entire codebase and specific modifications, teams can proactively mitigate gaps in test coverage.

Minimize Defect Leakage

By analyzing defect trends and historical data, Insights pinpoints functionalities prone to defects. It correlates defects with specific code modules to help teams minimize defects slipping through to production.

Identify and Mitigate Risks

You can identify potential risks associated with code changes, based on Insights’ ability to analyse code overlap or high-risk modifications. This allows for risk-based test optimization and targeted efforts.

Reduce Scope Churn

With Insights, teams get real-time visibility into project progress and changing requirements. This allows for informed decisions and better scope management.

Accelerate Test Execution for Faster Releases

Insights reduces over-reliance on test automation and subjective test reviews. It replaces opinions with actionable data on change impact and product health – all without disrupting existing workflows.

Minimize Defects, Accelerate Releases

Muskaan Pathak

Muskaan works as a Content and SEO Strategist at OpsHub. Her interests include devising content marketing strategies for SaaS enterprises, brand strategy and the convergence of product-first thinking with emerging tech and communication.

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Top 12 Software Quality Metrics that Actually Matter (Expert Insights)

Introduction

The 3 Cs of Software Quality

Strategies to Improve Software Quality

Core Software Quality Metrics You Need to Measure

1. Test Coverage

2. Defect Density

3. Defects per Software Change

4. Test Effort and Reliability or Cost per Test

5. Test Case Effectiveness

6. Defect Leakage

7. DORA Metrics

Advanced metrics you can measure to maximize software quality

8. Change Impact Analysis

9. Code Coverage for Impacted Functionalities

10. Scope Churn for Release

11. Code Override Between Work Items

12. Technical Debt Prioritization

Quality Gap Intelligence

Unified Software Quality Metrics to Improve Code Quality, Optimize Test Cycles, and Release Faster.

Table of Contents

Muskaan Pathak

Curious to know what Insights can do for your teams?