Over-Testing and Under-Testing: Avoiding the Pitfalls

Over-Testing and Under-Testing… how to avoid the Pitfalls?

At this point, it is obvious to everyone that it’s clear to everyone that Software testing is an essential part of the software development lifecycle (SDLC). It is not just a necessary evil, but a vital and strategic process intended to check and verify that a software program works as intended and meets all the requirements.

It is an investment that pays off in the long run. Furthermore, it helps ensure you release a high-quality product that meets user needs and avoids costly problems down the road.

However, deciding what to test is a complex task involving balancing thoroughness and efficiency.

Finding the right balance in testing is essential. While testing everything is virtually impossible, opting for no testing presents significant risks. Teams must carefully prioritize their testing scope to achieve sufficient coverage and avoid the pitfalls of either over-testing and under-testing.

While thorough testing is crucial, over-testing software can be detrimental. Over-testing occurs when testing efforts exceed what is necessary to ensure the quality and functionality of the software.

Here are some factors contributing to over-testing:

Over-testing can lead to wasted resources, increased costs, and delays in the development process. It also causes tester fatigue, where repetitive or redundant testing can result in decreased vigilance and motivation. Moreover, beyond a certain point, additional testing yields fewer new insights or defect discoveries.

Under-testing in software testing occurs when the software is inadequately tested. That can result in potential issues or defects going unnoticed before deployment. Such oversight can greatly impact the quality and reliability of the software product.

Below are several factors that lead to under-testing:

Under-testing can have significant impacts on both the software product and the organization deploying it. It not only leads to undiscovered bugs in production but also lowers customer satisfaction and damages reputation. Additionally, it incurs higher maintenance costs because bugs discovered after release often require more resources (time, effort, and money) to fix compared to those caught during the testing phase.

It is essential to conduct meticulous test planning and methodically prioritize and select test cases within the suite. Aiming to strike a delicate balance between comprehensive coverage and the effectiveness of the testing process, while being mindful of resource limitations and the evolving nature of the software under test. 

In the list below, you’ll discover various methods to prioritize test cases. All intended to strike a balance between comprehensive coverage and reducing execution time:

To ensure testing planning remains effective, it is recommended to regularly update and refresh the test suite by incorporating new test cases, prioritizing those most pertinent, and removing obsolete tests.

To effectively address this task, teams can closely monitor real-world usage patterns and behaviors in production. This approach enriches the diversity of test cases and helps identify redundant tests, obsolete tests, and gaps in test coverage.

By closely monitoring real user interactions and behaviors in the live production environment and comparing them with tests conducted in testing and staging environments, testing teams gather insights that go beyond written requirements.

This approach helps avoid relying solely on written requirements or internal biases and assumptions about what is critical. That enable the establishment of test coverage that balances comprehensiveness with testing effectiveness.

Gravity is a unified platform designed to help testing teams monitor and leverage insights from both production and testing environments, enhancing the efficiency of the test planning and preventing over/under-testing. It consolidates key data and insights into a single solution for easy access and analysis.

Its primary function is to produce “Quality Intelligence” by processing the ingested data through Machine Learning algorithms and Generative AI. This involves translating raw data into meaningful insights using techniques such as pattern recognition, trend and correlation analysis, anomaly and outlier detection, and more.

Gravity’s ability to monitor production and testing environments allows it to conduct a comprehensive coverage analysis. By comparing the paths taken by real user interactions in live production with the tests executed in testing environments, Gravity generates insights to enable testing teams to spot gaps in coverage, identify features that are either over-tested or under-tested, and recognize redundant testing efforts in less critical areas. 

Gravity utilizes pattern recognition and AI (Artificial Intelligence) to automatically generate test cases for areas lacking test coverage, whether they are manual tests or automated scripts for test automation tools like Cypress, Playwright, and others. This feature not only reduces the burden of test case creation but also leads to a decrease in maintenance overhead.

Since it relies on real usage data collected from production environments, this approach enables data-driven test case prioritization. It focuses test coverage on high-impact areas that directly affect the end-user experience.