Automated Testing: Your Team’s Safety Net

In Agile and DevOps environments, automated testing serves as the team’s safety net by offering a dependable and consistent method for rapidly and efficiently validating code changes.

When teams adopt test automation, it greatly accelerates the speed and frequency of testing in comparison to manual methods. This capability allows teams to identify issues earlier and perform more thorough testing as the complexity of the application increases.

By automating repetitive, time-consuming, or monotonous tests, manual testing resources can be redirected toward exploratory testing, usability testing, and other higher-value testing activities.

In addition to faster feedback loops, running automated tests early and frequently during the development process helps detect and address issues sooner. This approach is more cost-effective than identifying problems later in the development cycle.

Automated testing allows for the creation and execution of a broader range of tests than would be practical with manual testing alone. This results in improved overall test coverage.

On top of that, automated tests execute the same steps uniformly each time. That eliminates the risk of human error or inconsistency that can arise with manual testing. This consistency ensures more reliable and repeatable results.

However, implementing test automation does present its own set of challenges.

Implementing thorough test automation requires a considerable initial investment, advanced programming skills, and significant time for developing and maintaining automated test scripts.

To address these challenges, major test automation tool vendors are investing in low/no-code solutions. Why? To bridge skill gaps. They are also increasingly incorporating artificial intelligence advancements to simplify test creation and maintenance.

However, test automation involves more than just developing and maintaining automated test scripts. 

Despite many advancements in test automation tools, a key issue remains: human involvement is still necessary for some tasks.

Manually analyzing requirements and designing test cases to achieve balanced test coverage, while avoiding both over-testing and under-testing, is a challenging, time-consuming, and error-prone task.

While test automation can act as a safety net to ensure that requirements are covered, it is not foolproof. 

There may still be unexpected gaps in the requirements that could result in defects making their way into production.

You can tackle these gaps by reviewing and updating the requirements and using a Requirement Traceability Matrix (RTM) to make sure all requirements are covered.

However, test coverage gaps frequently arise due to the insufficient representation of real-world user behaviors and preferences in the requirements. 

Anticipating and accounting for user interactions and behaviors in written requirements is challenging for testers, product owners, and business analysts. This difficulty stems from the unpredictability of how users will behave in real-world situations.

To increase test coverage and align testing with real-world usage, testing teams can analyze:

All of that, aiming to bridge the gap between specified requirements and actual user behaviors in the real world.

This type of analysis facilitates the recognition of usage patterns, common user journeys, and frequently accessed features, effectively addressing gaps left by potentially incomplete, poorly defined, or ambiguous requirements. 

Gravity is a platform that aggregates raw data produced by different tools throughout the development and testing lifecycle, as well as live user behavior data from production usage collected through Real User Monitoring (RUM) traces, with the goal of generating “Quality Intelligence” models.

“Quality Intelligence” is produced by processing the ingested data through Machine Learning algorithms and Generative AI. 

This involves translating raw data into meaningful insights. To achieve it we use techniques such as pattern recognition, trend and correlation analysis, anomaly and outlier detection, and more.

Gravity helps testing teams to:

With these data-driven insights, teams can more effectively ensure focused coverage in crucial areas of the system under test and enhance the efficiency of regression testing, without relying on guesswork or poorly written requirements.