Software Testing Trends to Survive and Grow in the Post-Pandemic Age

 

software-testing-trends-to-survive-and-grow-in-the-post-Pandemic-age

During the pandemic, many businesses embarked on a digital transformation journey. From online education to traveling through virtual reality, telemedicine services to online gym training, and work-from-home in the IT sector - technology is at the heart of this revolution. And such a landscape demands out-of-the-box ideas.  

QA testing lies at its core for validating these ideas and driving further innovations. Global Market Insights predicts that the software testing market is about to grow at a CAGR of over 6% from 2020 to 2026. 

In this article, we will explore some important software testing trends or strategies to look out for in contemporary times and beyond. 

Important QA Trends to Consider 

#1 Machine Learning and Artificial Intelligence

The usage of Artificial Intelligence and Machine Learning will grow in every aspect of technology in the coming years. In fact, by 2025, the global investment in AI is expected to reach approximately USD 200 billion (source: Markets and Markets). The implementation of AI and ML will significantly increase software testing services. Here are a few instances: 

§  Predictive Analytics - Explore crucial parameters of the end-user’s behavior and underline the specific areas in an app to focus on. 

§  Defect Analytics - Analyze application defects that may bring potential risks to the company. 

§  Log Analytics - Determine exceptional test cases that demand both manual and automated testing.

§  Test Suite Optimization - Identify and remove irrelevant test cases.

QA teams can leverage Artificial Intelligence and Machine Learning to upgrade their automated test strategies. For instance, AI algorithms can give them insights into what to prioritize for additional testing. Further, ML-enabled analytics can help them detect underexplored aspects of any application. As a result, the app can be optimized properly. 

#2 IoT and Big Data Testing 

Connectivity is a major concern in today’s fast-paced technology realm. The Internet of Things has already set a new standard for numerous gadgets and applications to meet. Naturally, QA testing companies need to pay attention to testing performance, compatibility, security, usability, data integrity, and more to ensure a seamless user experience.  

A similar thing is witnessed in the case of Big Data. The increase of IoT applications has paved the way for an extensive amount of data generation. And it demands Big Data testing to validate information, create data-driven analysis, and improve market strategies, audience segmentation, and targeting. As data is key to the contemporary strategic market, Big Data testing is going to expand in the years to come. 

#3 Automated Testing 

Artificial Intelligence and visual modeling technologies have facilitated codeless test automation. Using these tools, the experts of software testing services can create test case scenarios without spending a long time. The advantages of automation tools include the following: 

§  The generation of test cases does not demand any complicated syntax. Therefore, test case formation is rapid and effective. 

§  The learning curve of the QA engineers is relatively low as they need not require extensive programming language skills. Therefore, any software testing company prefers to employ automated testing. 

§  The test cases can be easily reviewed. It will help in reducing time and thus advance the progress of device launching. From the manufacturers’ perspective, it will provide a competitive edge.

#4 Performance Engineering 

As customer experience becomes the driving force for the success of any application, a short development cycle and more frequent releases have become mainstream for manufacturers.

Consequently, software testing companies are adopting a consumer-focused approach in every phase of the Software Development Life Cycle (SDLC). It mainly aims at resolving performance-related issues in the early phase of the product’s life cycle. 

Conclusion

Performance testing goals such as stability, speed of the app under diverse situations, scalability, and more are coming to the forefront. Performance engineering encourages QA testers to develop relevant performance metrics by analyzing users’ expectations and the application’s purpose. They assist manufacturers in driving value for the target audience as well as the business by evaluating every aspect of the system. 

Resource

James Daniel is a software Tech enthusiastic & works at Cigniti Technologies. I'm having a great understanding of today's software testing quality that yields strong results and always happy to create valuable content & share thoughts.

Article Source: medium.com


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