All companies, whether they are a startup or an established company, should invest in some form of testing. Since QA engineers are trained in destructive engineering practices, investing in testing allows companies to be aware of the risks up front and get the right coverage. Everyone has their own opinion of what QA for a startup should look like, however, and the amount of differing opinions gives rise to many misconceptions. Here we’re clearing up 6 of the most common misconceptions about startup QA.
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Venture capitalists just want the focus to be on development
When venture capitalists fund a startup, they are taking a risk in the hope that its products will be adopted and the company will grow 10x with as little of their investment as possible. That will only happen when startups themselves invest in proactive QA that locates and deals with as many bugs out as possible before the product goes to market. Bringing in experienced QA engineers as early in the process as possible will allow thorough testing of products and applications before customers ever see them, ensuring the highest possible quality.
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Startups can’t have automation because they don’t have enough bandwidth
Startups often feel they need more time, people, and other resources to automate tests because they are trying to get new features out of the door as quickly as possible. This, however, is different. One option a startup has when they feel they do not have enough bandwidth for test automation is outsourcing their automated testing to a QA team with Artificial Intelligence experts. Outsourcing test automation gives startups access to experienced automation engineers. It helps a startup release its features on time (or earlier), allowing its in-house team to focus on other aspects of the project. With the help of Artificial Intelligence Tools, the velocity of automating tests enhances at a faster rate. A startup can also build automation into its QA process. If you build automation into your process ahead of time, you can have automation at the beginning of the software life cycle.
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Startups don't have the right processes for QA
It takes some time and investment to create and implement good processes for QA. At QASource, we’ve implemented simple guidelines for automation testing. We’ve found that planning up front keeps us from having to go back and review, update or change engineers’ code. We’ve also created a simple template for how to report, replicate and troubleshoot bugs, which saves the developers’ time in finding and fixing the bug. You don’t need to have a lot of documentation, but you should have some processes put in place. Creating and implementing processes isn’t that time-intensive, and you will actually save time in the future.
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Startups need features fast; they don’t have time for QA
Startups actually need to set aside time and budget for QA so their products can be adopted and scaled to multiple customers. Often, when companies get a big enterprise customer —or many customers — and haven’t adequately invested in QA, they encounter many more challenges than if they had built in time for startup QA. Startups in particular are subject to issues: They are constantly updating their company website or application, or are adding new code under deadline. Doing so brings with it an increased risk of the new code breaking the current code (or making the code act unexpectedly). If it's not tested, buggy systems and products will lower your customer retention over time. It is crucial that startups make time for QA.
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There's no money to test
Actually, startups can’t afford not to test. They need every advantage they can get — like having a higher-quality product, staying on budget and meeting deadlines. Testing is essential for established organizations to retain clients and for new organizations to gain new clients. Not testing will lower customer retention over time, and who can afford that?
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AI can’t be applied to speed up Quality Assurance Process
There is a common misconception that Artificial Intelligence (AI) is not helpful for Software Quality Assurance (QA). However, the truth is that AI can significantly expedite the QA process by automating test case generation, execution, and regression testing. AI-driven tools analyze code for defects, predict potential issues, and optimize test case selection. NLP aids in generating test cases from requirements, while AI-generated test data facilitates load and performance testing. NLP and analytics offer insights into testing progress and defect trends.
Additionally, AI assists with environment setup, improving efficiency and accuracy. AI is a best practice for enhancing QA automation, accelerating testing cycles, and ensuring faster, high-quality software delivery.
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Startups don’t have to worry about QA until the very end
This is both a common and a dangerous misconception. The cost of fixing a bug is much higher the later it is found in the process. Good QA engineers will be able to help identify bugs in the design stage, drastically reducing its potential cost. If QA is left until the very end, testing and the subsequent coding fixes will create bottlenecks and delays. If QA is brought in early, however, deadlines and budgets can stay on track.
Now that we’ve cleared up these misconceptions, you should have a clear idea of what startup QA actually is and how it can help you. If you are ready to begin startup QA, visit qasource.com today to get started.
Why Choose QASource?
QASource is a one-stop solution for all QA needs for startups. We have a team of experts in all domains, including AI skills and AI-powered services, who can satisfy your QA requirements. Our expert QA team will help you design the right test strategy and approach for your product. This will help in eradicating your problem to build a quality product. We will help you with optimizing QA services and making a reliable product.
