In the highly-competitive modern marketplace of web applications, it’s hard to get to the top and even harder to stay there. This is especially true if you’re just entering the market. Hundreds of web apps are released every day, and their inventors strive to grab the attention of new users and potential customers, trying to outshine the others.
The level of competition is high, and there’s little room for error. To ensure an (almost) error-free launch, companies turn to web app testing to detect any issues before their products hit the market.
While this solution isn’t perfect, it does provide a required level of quality to web app owners, allowing them to release products that aren’t only workable but are also prone to less system faults. At the same time, however, this branch of IT isn’t perfect and constantly seeks improvement.
In this article, we’ll discover more about the benefits of web app testing, its issues, and the ways natural language processing (NLP) can give it a necessary boost.
The Problem with Testing
Although there’s a trend towards facilitating software development with newer, more efficient tools, it’s surprising that test cases are still mostly created manually, which is time-consuming and challenging. This raises the following problems:
- Those who write the cases are people, and that means there’s always room for human error. This is particularly true for inexperienced testers who may fail to meet functional requirements, as their tests are overly ambiguous or simply incorrect.
- It’s often impossible to use the same tests in regressive testing if application requirements change, meaning that a lot of test cases have to be written from scratch or, at the very least, heavily amended.
- As testers are time-limited in creating or amending existing tests, it’s impossible to conduct regressive testing with 100% efficiency. This results in possible bugs and system failures, risking a negative first impression regarding the released application.
Each of these drawbacks may affect the competitiveness of a product among end users, resulting in revenue loss.