From Lab to Laptop- The Science of Testing

When I was in high school, I majored in Biology, we went to the university labs several times to conduct experiments who took long hours of planning and executing.

Why am I telling you this?

Because this week, I realized how similar the QA work to the work in a Lab.

Just like scientist’s form hypotheses, every QA process begins with assumptions about how a feature or product should perform. We predict outcomes and set up tests to prove (or disprove) our hypotheses, refining them based on results.

Proper planning of the test steps will yield accurate and reliable results while reducing the risk of human error. Any scientist will agree.

In both labs and QA, creating controlled conditions is key.

Testers establish environments that mirror real-world conditions as closely as possible, ensuring accuracy and consistency. By defining operating systems, device types, network conditions, and software versions, we eliminate external variables, isolating factors that could affect test outcomes.

With the environment set, we define our expected results, aligning with the hypothesis. This could mean predicting specific outputs for a particular user action or expecting certain performance metrics. Clear expected outcomes enable us to objectively assess results, as any deviation will indicate a potential issue.

Documenting every step is crucial in both QA and science. Accurate test cases, detailed bug reports, and thorough documentation allow others to understand, reproduce, and learn from our findings.

The experimentation phase in QA is our actual testing. Much like running a series of experiments, we methodically execute test cases to observe how the software behaves under various conditions. We perform functional tests, regression tests, and other cases to validate the system’s behaviour, with each test providing additional data points about the software’s reliability.

Once testing is complete, we compare the observed results to our expectations. Did the software perform as hypothesized, or were there discrepancies? Each variation, each "unexpected result," gives insight into potential issues.

Finally, both fields require a love for problem-solving. The ability to ask questions, probe deeper, and explore alternative explanations fuels innovation and helps deliver better outcomes.

Just as in the lab, QA testing is a process of discovery, driven by curiosity and precision. It’s rewarding to bridge these worlds, knowing that both paths are about uncovering insights and ensuring quality.

P.S. Those who use QC already know this.😉