Models are all around us and in technical terms, it helps us make informed choices by simplifying complex situations. For instance, when we use a model to simplify the appearance or the usage patterns of a complex system, we obviously get an idea of what needs to be implemented and what needs to be removed. Developers can significantly reduce expenses because the model is used and not the actual software solution or product. But many people wonder, if the actual product is not used, then will the results be accurate? Yes, it will. And that’s what makes Model-Based Testing so much important. It not only reduces expense but also saves time because the company will not have to wait for the actual product to get completed and delivered but can work on a model with the same specifications.
Let us make the process simple for you to understand.
Would a million-dollar hypercar manufacturer, the Koenigsegg actually crash their $2 Million cars for a collision test? Of course, they won’t! It’s way too expensive to actually crash a hypercar. So how do they do it? They crash a model that has the same specifications and features. And that is exactly how model-based testing works.
So how does it enhance software and application testing?
Let’s find out.
Why Do Software Developers Need Model-Based Testing?
The COVID-19 pandemic significantly increased the demand for software applications all around the globe. Software developers were not only challenged with prompt product delivery but they also had to ensure that the solutions are working effectively under every real-world condition.
Performing effective software tests and waiting for test data is one of the most time-consuming processes for software developers. Especially if it is a large enterprise application; it will take more time and resources. Also, another significant challenge that testers faced is the development of ambiguous test cases that lack sufficient detail or are constantly being changed by clients.
All this could lead to poor quality test cases which could delay test design and further result in sub-par code. In such instances, testing will not be conducted in alignment with development, so it is much more difficult to determine test and automation coverage. Errors are often found late in the development cycle which results in more expensive modifications.
Thankfully Model-Based Testing can help testers solve these traditional challenges that most software development companies face. This testing strategy not only makes the life of testers easier and clients happy but also provides a better return on investment to software development companies.
Benefits of Model-Based Testing
Every software solution or product has multiple features and system requirements, also each feature is expected to behave entirely differently from the other. Rather than creating multiple test scenarios or test cases for each and every feature, model testing enable the automatic generation of test procedures with a single model that is capable of generating multiple test cases.
It significantly empowers developers and testers to collaborate and work together to prioritize, strategize and predict how the actual product will perform and what are the essential areas to test. Because testing everything continuously during the process could be quite challenging and it would be highly beneficial to test just the areas that the models specify.
The level of automation such model testing provides for testing indicates that they can do more, with less time, expense, and resources. It also tremendously increases test flexibility, decreases test suite maintenance, and incorporates quality at every stage of the software development life cycle.
Challenges of Model-Based Testing
Of course, similar to every test automation service, model-based testing also has a few challenges. Oftentimes, model-based testing demands a lot of upfront work in order to significantly save time and costs, in the future while tests are conducted. Also, not every tester or software developer will be completely aware of the processes required in model-based testing, therefore, it would require a crucial employee upskilling with a steep learning curve, and also it is imperative that the developers must understand testing.
Also, the complete software development life cycle has different phases and different types of resources. Building a model that all these resources across every phase can understand will be extremely challenging.
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There is no doubt that when it comes to model-based testing and test automation, the benefits to end-users or testers far outweigh the initial challenges. It reinforces the innovative concept that quality analysis of a software solution or product should be incorporated from the initial build itself and not during the final stages. It enables quicker error detection in design and spec which results in fewer errors during the development process and deployment. The increased automation of course lightens the stress on testers such that the QA teams can focus on other core functionalities in the software, which could lead to a better, more efficient software solution.
Ricky Philip is an industry expert and a professional writer working at ThinkPalm Technologies. He works with a focus on understanding the implications of new technologies such as test automation services, artificial intelligence, big data, SDN/NFV, cloud analytics, and Internet of Things (IoT) services. He is also a contributor to several prominent online publishing platforms such as DZone, HubSpot and Hackernoon.