7 red flags in software testing process you should never ignore
Best practices Management Agile
8 mins read
April 18, 2024

5 red flags in software testing process you should never ignore

Roman poet Juvenal would have certainly wondered,  “Who will test the testers themselves?”. Quality assurance verifies that the software works well, but is your testing running well? Read our list of common testing process mistakes and learn to overcome them.

Denis Matusovskiy

1. Not involving developers early enough

QA testing problems are everyone’s problems. Product Owners can’t get the features they need fast enough. Project Managers have to change their timelines to the frustration of Product Owners. Devs need to spend time fixing their old code rather than writing new lines or refactoring. Ultimately, you delay the glorious moment when your business starts to make more money and you need more money to make it happen.

Unit testing is a great option to prevent a lot of bugs before a build even makes it to QA. Developers create tests to validate small chunks of code, run the tests, and fix any detected issues. The less developers and QA have to talk about trivial issues, the faster a feature will pass the QA check.

That being said, utilising devs does not mean leaving them alone. Seniority and past experience will make it likely that you developers will not just appreciate, but require the help of QA to create unit tests. The time investment will certainly pay off, and in more than just better code. Working in someone else’s shoes does make you appreciate their job.

2. Testing to “improve quality”

It may sound odd, but a big problem in software testing is testing so your software has higher “quality”. Quality is subjective, and even then, chasing something without a threshold gets you into the diminishing returns territory pretty fast. Ask yourself: do you even know the level of quality required in your domain? Are you trying harder than needed and if yes, why?

Now that I got you thinking, I will point out that there is a measurable answer. Quality assurance metrics turn abstract “quality” into numbers that you can actually prioritise, track, and improve upon. Just like in business analytics, figures do not always tell the full story and sometimes mean nothing at all. Overthinking QA metrics is the other end of the spectrum. 

The best way to track QA metrics is to create dashboards and reports. The better you organise a wide range of data, the more actionable insights you can draw from it. The aqua testing tool is perfect for this job. The Reports Wizard lets you make in-depth custom reports but also comes with a neat template library. Dashboards come with KPI Alerts so you are notified about important trends without even opening the dashboard.

Get an Enterprise-grade tool to monitor your QA metrics

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3. Viewing QA as a blocker

While not strictly a software testing process mistake, the wrong attitude can very well affect it. 

Quality assurance is effectively the last hurdle before you can release a new build or even launch a product. A lot of people have done their best to get there. The product team came up with requirements, designers made some sick visuals, copywriters wrote colourful texts, and devs provided well-structured code. Hopefully, there is a passionate founder around as well. Now, all these people are waiting for the QA check to end.

And QA checks do not always end fast, especially for a new product and/or companies that did not start testing early enough. There will be bugs, at times painful troubleshooting, a fixed build, and probably some old or even new bugs in it. This nature of the game is all too familiar, and it can drive some people more impatient than others.

While obviously biassed from their line of work, here’s a quote from somebody that has seen it all:

“The problem is not that testing is the bottleneck. The problem is that you don’t know what’s in the bottle. That’s a problem that testing addresses.“

Michael Bolton, Co-creator of Rapid Software Testing

4. Dismissing test automation

Test automation has not always carried the positive sentiment it has now. Even looking through early 2010s materials, you can still see people that struggle to justify the time and resources needed to make it work. Good automation engineers command a high salary, and it can be hard to justify before an automation engineer explains their value to you.

That being said, problems with test automation and modern QA are not the same as they were 10 years ago. There is a booming market of affordable automation solutions. As user expectations go up, you do run into situations when there is too much to test. Sticking to manual testing only will make your QA a bottleneck, and Michael Bolton won’t be able to back you up here. 

If you’re looking for inspiration, UI testing is a great area to automate things. Our website currently has 5 various layouts depending on the reader’s screen. Testing if the same thing works properly on 5 different screens introduces a very repetitive routine. Solutions like Applitools and Testim will both reduce the time and increase the precision for such tests.

testing strategy template

Get a testing strategy template that enables us to release 2 times faster

5. Avoiding artificial intelligence

Artificial intelligence in software has been a buzzword for so long; hearing “AI” made me tune out even before I switched to IT. This time, it is different.

While people still haven’t created actual artificial intelligence, there has been a lot of progress on individual traits. Ideally, we want AI to make an informed decision based on knowledge and past experience just like a human would. This is what puts intelligence in the term artificial intelligence.

Large language models have been the greatest contributor to actually valuable AI in software development. While not always powerful, low-code coding solutions have been reducing the time and knowledge required to get engineering tasks done. Analysing patterns and acting on them is another trait of AI, and UI testing solutions that I mentioned earlier do that well too. 

The biggest impact, however, comes from generative AI. ChatGPT has proven to be surprisingly capable at coding tasks. It is, however, still limited by the amount of context you can realistically feed it. Also, while not a major issue now, being months and years behind on software development events will be a problem. You can’t realistically ask it to do management of the testing process

Luckily, OpenAI allowed other companies to develop GPT-based solutions even earlier than they presented ChatGPT. You can find really impressive solutions for the entire product lifecycle, from making requirements to testing software and promoting it. AI-powered quality assurance that can be personalised to your test suite is as powerful as it sounds. The benefits are much easier to reap compared to setting up test automation, too. 

Red flags in software testing


Quality assurance needs transparency and validation of its own, too. While every project has its unique needs, you will save yourself a lot of pain if you spot these red flags early.

Speaking of traceable QA, aqua has you covered. It is an Enterprise-grade tool that also provides innovative features to companies of any size. The list includes test generation and prioritisation with AI, while traditional QA functionality has been matured over 10 years. 

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What is an error in testing?

In testing, an error refers to a discrepancy between the expected and actual behavior of a software system or component. It occurs when the actual output or behavior of the software deviates from what was predicted or specified. Errors can result from defects in the software code, incorrect assumptions, misunderstood requirements, or flaws in the testing process. Identifying and resolving errors is a critical aspect of software testing to ensure the reliability, functionality, and quality of the final product.

What are the common mistakes in performance testing?

Common mistakes in performance testing include inadequate planning, which can result in unclear objectives and unrealistic workload scenarios. Additionally, insufficient environment configuration may lead to inaccurate test results, and a lack of monitoring system resources during testing can make it challenging to identify performance issues. It’s crucial to address these mistakes to ensure accurate performance assessments and reliable software performance.

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