Code Coverage And Software Quality Metrics

Code coverage is a metric that helps teams and leadership personnel to understand how much of a source code is covered by tests. It’s a useful metric that helps assess the quality of test suites at different test levels.

Code Coverage Metrics

The calculation of code coverage is done automatically by using code coverage tools. One or more criteria is applied to determine how source code is exercised or not during the execution of tests. The common metrics include:

• Function coverage - how many of the functions defined have been called.
• Statement coverage - how many of the statements in the code have been executed.
• Branches coverage - how many of the branches of the control structures have been executed.
• Condition coverage - how many of the boolean expressions have been tested for a true and a false value.
• Line coverage - the number of source code lines tested.

There are many types of test coverage and the most interesting are:

• Features coverage - focus on achieving maximum coverage at a product features level.
• Risk coverage - each product requirement has the risk associated with the product and how to mitigate risks. However, some risks cannot be predicted, and different tactics should be applied in case of such scenarios.
• Requirements coverage - the aim is to define the test in a way that they provide maximum coverage for the product requirements described in the requirements documents.

Developers Role In Maintaining Software Quality

A competent software engineer or team is able to contribute high-quality code to the current code base with few errors. Multiple dependencies on resources and integrations make it impossible to achieve zero faults in the present software landscape. The goal is to detect possible deviations during the development process and reduce the impact of new code development on the features that are already in place. Because of this, evaluating the quality of the code is crucial. We can do this by comparing it to coding standards, code reviews, and static code analyzers like SonarQube, which check for security, maintainability, clarity, and code restructuring. With those tools, we can evaluate:

• The structural complexity of the program
• Vulnerabilities found in the repos
• Code smells
• Code coverage
• Code duplication

QA Role In Maintaining Software Quality

The QA team tracks and reviews the software quality through manual and automated testing, including the validity and standard of the source code. Manual test metrics can be divided into two classes: base metrics and calculated metrics. Base metrics are made up on the raw, unstructured data that is collected. Calculated metrics are derived from the data that was collected in the base metrics.

Manual Test Run Metrics

Some of the important manual test metrics are:

• Test case execution.
• Test case preparation.
• Test duration.
• Unit test coverage.
• Pass/Faill rate.

Automation Test Runs Metrics

Test automation helps reduce the amount of manual time spent on evaluating software quality. A few important test automation metrics are:

• Defect density (examine the quantity of defects to confirm if automation is executing high-value test cases).
• Test automation effectiveness (number of defects found by automation suites).
• Test run duration.
• Useful vs.irrelevant results.
• Defect leakage in production.
• Test suites’ maintenance time.

Although they measure distinct things, code coverage and test coverage are both metrics used to assess the effectiveness of software testing. While test coverage evaluates how thoroughly requirements and functionality are checked, code coverage keeps track of how much code is run during testing.