Since 2020, many software testers have gone from struggling to keep pace with remote work conditions to adopting an agile approach – fueled by AI, ML, and data analytics. In 2021, digital transformation initiatives have already accelerated the pace and scope of QA innovation. For instance, considering the recent sudden spikes in customer demand – many businesses have opted to ensure superior product performance through load testing techniques. They have also undertaken risk-based automated testing to prepare for foreseeable scenarios and adapt to unexpected events.

In the field of software testing, however, the advent of disruptive technologies is not exactly unprecedented. Even in recent years, there has been a surge in the adoption of cutting-edge testing methods, such as hyper-automation, mobile test automation, multi-device testing, IoT testing, etc.

Bearing that in mind,

let’s look at the top 4 trends that are changing the software testing landscape while impacting product development. 

Big Data and Analytics Testing

With each passing year, the continuous flow of data in even an average-sized company has been on the rise. Hence, there are more opportunities to transform unstructured information into actionable business insights, which could fuel enterprise-wide performance. But without proper data integrity – it turns into a road that goes nowhere. Thus, as 2022 looms around the corner, big data and analytics testing are soon becoming a top priority. And it requires a roadmap for execution so that the software testing process can be further aligned to company goals. Today, the importance of leveraging user data insights has also gained prominence, as more user analytics are getting incorporated during the planning and execution of software testing protocols.

Agile and Continuous Integration Testing

As earlier mentioned, remote work scenarios have caused agile development methodologies to increase momentum rapidly. Companies realize that an agile testing approach helps advocate a culture of transparency and flexibility that, ultimately, benefits the users. It also amplifies the pace at which multiple applications can interact with each other – thereby ensuring faster deployment, reduced downtime to market, and higher product ROI. According to a recent ‘State of Agile’ report, 87% of organizations have said that they use “agile methodologies to accelerate software releases.”

When backed by Continuous Integration (CI), the developers can swiftly integrate their code into a centralized repository, as well as identify bugs and defects during the software iteration process. In addition, it means that serious issues can be resolved during the initial phase of the software development lifecycle – raising the quality of codes and the performance level of the workforce.

AI and ML-driven Testing

AI and ML-driven workflows have heralded a new era of process efficiency in software testing. When they first showed up, it was all about cutting down the dependency on manual users to carry out testing-related tasks. This led to a significant boost in workforce productivity and operational efficiency – with testing teams being given the freedom to focus on strategic activities. So is it any surprise that a study has shown that “86% of CEOs say AI is mainstream technology in their office in 2021”?

But, with automated software testing gradually becoming the new normal, AI/ML has gone a long way further. Now, more companies are using them to improve error detection, optimize and increase testing suite coverage, and make smarter decisions with real-time data, and keep up with software releases. AI technology has also led to the development of self-healing frameworks, which can lower the cost of automated testing and enhance the ability to overcome test script challenges.

Cybersecurity and Compliance Testing

The need to stay competitive through business modernization has caused companies to use many connected applications and devices. Inevitably, it left them more open to cybersecurity threats and attacks. Factor this: A recent study showed that “Malware-related expenses for companies increased by 11% since 2019 and now reached $3.2 million.” Given the importance of data protection, as indicated by customers (and expected by the law), any personal information leakage or improper user access may trigger irrevocable financial and reputational losses.

As we march ahead into 2022 and beyond, more companies are likely to move from legacy to fully cloud-hosted environments. During the transitioning period, they must embrace modernizing testing practices, powered by agile, AI, ML, and data analytics, to ensure compliance and safeguard their technology infrastructure.

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Kripaa Krishnamurthi