By Ravi Pulle
Prior to the internet era, the concept of technology and granting it the power to think had always been considered science fiction. However, the advent of the internet has flattened every aspect of our life unprecedentedly. According to the Ministry of Electronic & IT and Skill Development & Entrepreneurship, with over 837 million internet users, India has become the largest connected nation in the world, with more than 800 million broadband users. Out of which 600+ million are smartphone users.
Today, for most of the generation, the smartphone has become the gateway to the internet, whether for entertainment or work. This is where, with rapidly changing customer expectations across the landscape, the need for high-performing apps has gained center stage. Nowadays, every business requires apps that perform seamlessly to attract new customers and retain existing users. Thus, to provide uninterrupted service to the customer, the brands must ensure that the software performs adequately and must be tested rigorously.
Though testing is time-consuming, and doing it repeatedly can become ineffective in the long run. This is where new-age technologies such as artificial intelligence (AI) and machine learning (ML) appeared as a dynamic duo and a silver lining in the dark clouds to increase the efficiency and effect of performance testing for brands. In fact, with the traction, the industry is growing to a great extent; according to IMARC, the artificial intelligence market of India is expected to reach $3,935.5 million, growing at a CAGR of 33.28% between 2023 and 2028.
So let’s dive into some of the key benefits of using AI-ML in brand performance testing. But before that, get into the details about why testing is essential for brands.
Performance Testing: Need and Importance
Performance testing evaluates the speed, responsiveness, stability, and scalability of a software application under varying workloads and conditions. It is a crucial step in the software development lifecycle, especially for brands that rely heavily on technology to deliver their products or services to customers. Some of the app issues uncovered through this type of testing include runtime errors, performance optimization difficulties, latency, throughput, response times, load balancing, and so on.
There is no denying the fact that performance testing is essential for brands to grow to a greater extent. As an umbrella testing methodology, it aids in raising an app’s responsiveness, decreasing the loading time, and giving the app any-context stability. Thus, it is also vital for brands to do performance testing to ensure that the system will satisfy the desired service levels in production while also providing a great user experience.
A continuous or continuing performance testing method is also recommended to minimize the exorbitant expense of resolving a problem in production performance. In fact, performance testing is essential for brands that rely on technology to supply their products or services. It helps enhance the user experience, prevent revenue loss, increase customer loyalty, support scalability, and mitigate risks.
Benefits of AI-ML in Performance Testing for Brands
Improved Accuracy and Efficiency: Compared to traditional manual testing, AI-ML algorithms can provide more accurate and efficient results. It can simulate thousands of users and transactions to detect performance bottlenecks, lowering the risk of downtime and enhancing user experience. According to MarketsandMarkets, the market for automated testing will grow at a 16.4% CAGR from $24.7 billion in 2022 to $52.7 billion by 2027. This growth is partly fueled by the growing demand for faster, more accurate performance testing.
Cost Reduction: The testing process is, without a doubt, iterative, which implies that each trial-and-error attempt necessitates code reconfiguration and new testing. However, due to humanity’s impatient nature, this results in longer product cycles and resource waste. This is where leveraging the power of AI-ML-based performance testing brands can significantly reduce manual testing costs. It reduces the requirement for traditional testing’s human resources, hardware, and infrastructure expenditures.
Scalability and Better User Experience: AI-ML algorithms can scale up and down quickly and simply based on load needs, ensuring that application performance is assessed under realistic conditions. Furthermore, AI-ML algorithms can simulate real-world user interactions and behaviour, providing valuable insights into how the application performs under various conditions. This can help to improve user experience and customer happiness.
Early Detection to Gain Competitive Edge: AI-ML algorithms can uncover performance issues early in the development cycle, allowing developers to make necessary adjustments before deploying the program to production, saving time and resources. In fact, brands that employ AI-ML in performance testing can get an edge over their competitors by delivering high-performance applications with a better user experience, resulting in improved customer loyalty and revenue.
It’s Time to Get More Competitive!
To sum up, ever since digitalization has taken center stage, a slew of new-age technologies have emerged, with AI and ML being a dynamic duo in mitigating challenges and providing benefits to brands in an ever-changing era of application. In today’s modern era, there are millions of web and mobile apps in the market; with fierce price rivalry, brands are enticing customers with high-performing apps. Low-performing apps are known to harm a brand’s reputation and customer experience. Thus, there is a dire need for brands to leverage the benefit of AI-ML in performance testing to gain a more competitive edge over others.
The author is sr member IEEE, MBCS