How to measure the impact of your automated tests on software quality

Every business investment raises a simple question: what are the expected benefits?

In the case of automated testing, the answer is not always obvious. How do you prove their real impact on software quality? What indicators should be tracked to measure their effectiveness?

This article offers clear guidelines for identifying the right metrics and rigorously assessing the contribution of your automated tests.

Understanding automated testing and its role in software quality

Automated testing represents a systematic approach in which pre-written scripts are executed by automated testing tools to validate an application's behavior.

This definition encompasses all the procedures that automatically check that software meets functional and technical requirements without direct human intervention.

How automated tests work

It works by creating test scenarios that simulate user actions and verify expected results.

These scripts run repetitively and consistently, guaranteeing uniform validation at each development iteration.

Benefits of automated testing

Increased coverage: automated tests can explore multiple execution paths simultaneously, testing complex combinations that manual tests could not cover effectively.

This completeness directly enhances software quality by identifying potential defects in less frequently explored areas.

Reduced human error: each test follows exactly the same protocol, eliminating the oversights or approximations that can occur during repetitive manual testing.

Faster validation: teams can get immediate feedback on code quality, enabling rapid correction of anomalies before they propagate.

In agile and continuous integration environments, this speed becomes crucial to maintain a sustained delivery rate without compromising product stability.

Key indicators for measuring the impact of automated testing 

Evaluating the effectiveness of your automated tests means going beyond impressions and basing your analyses on objective metrics.

These indicators can be used to track progress, identify weak points and demonstrate the added value of automation.

Coverage rate

The coverage rate measures the proportion of code or functions executed by your automated tests.

It gives an indication of the extent of your testing strategy, but does not in itself guarantee the absence of defects.

For example, 80% coverage means that the majority of the code is executed during testing, but the quality of assertions and scenarios remains decisive.

Fault detection

This indicator assesses the ability of automated tests to identify anomalies before they are put into production.

It can be measured by comparing the number of bugs detected during the development/test phases with those discovered in production.

Rather than aiming for a fixed percentage (such as 70%), the important thing is to monitor progress over time. Your automated tests should help detect an increasing proportion of defects upstream.

Average detection and correction time

Automation can often reduce the time between the introduction of a fault and its identification.

Whereas a manual campaign can take several days, an automated run triggered by each commit or build can report an anomaly in a matter of minutes.

Combined with efficient ticket management, this also reduces total correction time.

Reducing regressions

A reduction in regressions between successive versions is a strong indicator of the value of your automated tests.

By quickly validating existing functionalities after each modification, automation limits the reintroduction of old bugs.

Each regression avoided represents a time saving for the team and a direct improvement in stability as perceived by users.

Test suite execution time

An automated suite needs to deliver results quickly to be useful in an agile or continuous integration development cycle.

Too long an execution time can slow down teams and discourage frequent test execution.

Ideally, the main suite ("smoke tests" or "critical tests") should run in a few minutes.

Test stability and reliability (unstable or flaky tests) 

Unstable tests produce variable results (pass/fail) without modifying the code under test.

A high rate of flaky testing is detrimental to confidence in automation, as it generates false alarms and time wasted on verification.

Monitoring and reducing this rate is essential to maintaining a credible test suite.

Suite maintenance costs

A useful indicator is the time spent by teams on maintaining automated tests, compared with the time invested in running and analyzing them.

A good strategy should strike a balance between broad coverage and maintainability.

Rate of failed tests not related to real bugs

A test may fail for external reasons (unstable data, unavailable dependencies, misconfiguration).

Measuring the proportion of these "false positives" enables us to assess the relevance and robustness of the test suite.

A low false-positive rate boosts developers' confidence in the results.

Complementary measures to assess the overall performance of automated tests

Beyond the traditional metrics focused on fault detection, automation assessment becomes more relevant when it integrates economic, operational and human indicators.

They are the real added value of test automation.

Resources saved

One of the most tangible benefits is the reduction in human time spent on repetitive validations.

By automating regression testing, teams can reallocate a significant proportion of their efforts to higher value-added activities (exploration, design, innovation).

Substantial savings can often be seen in terms of testing time and the costs associated with late bug fixing, although the gains vary greatly depending on the context and the maturity of the automation.

Test execution frequency

Regularity of execution is another key indicator.

Automated tests run on a daily basis, or even with every commit in a CI/CD chain, enable problems to be detected very early on, whereas more spaced-out validations (weekly, for example) allow risks to accumulate.

For this pace to be truly advantageous, the test suite must be fast and stable enough not to slow down the pace of development.

Team satisfaction

Developers' and testers' perception of the value of automation is a valuable qualitative complement.

Regular surveys can measure :

• confidence in automated results,
• ease of use of the tools,
• the perceived impact on daily workflow.
  

Lasting adoption depends on teams who are convinced of the usefulness of the automated suite.

The importance of a combined approach with manual testing

Automation and manual testing are not mutually exclusive! They complement each other.

Automation brings speed, reproducibility and broad coverage, ideal for checking technical regressions and repetitive scenarios.

Manual testing remains indispensable for exploring complex scenarios, evaluating ergonomics, judging the fluidity of a user path or analyzing aesthetic and contextual aspects that a script cannot capture.

This hybrid approach exploits the best of both worlds:

• automated testing guarantees continuous, systematic validation,
• manual testing provides qualitative analysis and indispensable flexibility.
  

The measurement of overall impact must therefore reflect this collaboration, taking into account metrics from both practices.

Analysis of the impact of changes on test coverage and effectiveness

Code is constantly evolving, and each change can affect the relevance of the test suite.

New features, refactoring or even a minor correction can create uncovered areas or render certain tests obsolete.

Test impact analysis quickly identifies which parts of the code are affected by a change, and which tests need to be adjusted or added.

By comparing coverage before and after each change, we can detect loss of relevance and anticipate potential regression.

This proactive maintenance (updating assertions, adding new cases, deleting tests no longer needed) is essential if the automated suite is to retain its value over time.

Mr Suricate - leader in no-code test automation

Test optimization requires continuous monitoring of these metrics and their adaptation to changes in the code.

Solutions like Mr Suricate perfectly illustrate this global approach, offering a no-code platform that simplifies test automation while providing real-time monitoring of insights.

Our platform makes it easy for teams to master their user journeys without in-depth technical expertise.