When I first started my professional career as an architect more than two decades ago, the technology landscape was simple. The demands were straightforward and continuous testing was something quite unheard of. Then as I changed my roles over the years, and took up different responsibilities in global business development, sales, and business leadership, I witnessed how the technology landscaped gradually evolved. With an unstoppable wave of digital transformation, the IT landscape grew more complex with new innovations and solution available to us. The constant demand for better and easy-to-use software solutions from businesses grew gradually that led to the evolution of quality and testing approaches, methods, and expertise over the last few years.
As every organization today aspires to deliver faster and more valuable IT solutions, they have been leveraging agile and DevOps methodologies and using smarter automation technologies and as-a-Service solutions to deliver IT solutions faster and with greater flexibility. There is an increased dependency on IT solutions today, with the integration of front-office and consumer-facing apps with back-office core systems, leveraging cloud and microservices, and the integration and use of IoT. And, on top of that, AI is emerging to make these solutions autonomous and self-learning. As we work our way around to deliver innovative solutions in this complex IT landscape, we cannot ignore the risk of failure. While some fears are real and unavoidable, there are other situations where the risk can be bypassed through continuous and automated testing.
What the industry is thinking
Today businesses are more customer centric and are determined to deliver solutions with greater speed. In fact, we have seen in the recent times how companies have leveraged “speed” as a key differentiator.
However, in this “Need for Speed”, there is a high possibility of quality getting compromised. Companies that consider quality as an afterthought is bound to fail which eventually will hit its bottom line.
So, what is it that is going to move the needle? The answer to this is Agile and DevOps ready tools that enables continuous testing. To meet this demand of faster releases with better quality, teams need modern, scalable, and most importantly flexible tools. No one tool can perform all the tasks. Hence, different tools need to seamlessly integrate with each other in the DevOps (CI/CD) pipeline to achieve the Continuous Testing “High Speed with Quality” goals.
I believe that AI/ML is going to play a crucial role in upcoming years. The use of data intelligence, artificial intelligence, and machine learning hold great promise to make testing more intelligent and automated while dramatically reducing manual effort and allowing enterprises to balance innovation and risks. Few of the use cases of what we can achieve through AI and ML techniques could be – Creating actionable insights, reducing duplication of data, increasing reusability of assets, less and less maintenance of tools, autonomous compliance workflows.
I also see that there is a huge demand for bringing in efficiency by reducing duplicity and increasing reusability. Lowering the maintenance cost and being compliant to various industry standards are other important factors that businesses are focusing on.
In a recently conducted survey by Sogeti revealed that among 500 senior decision-makers in the large and enterprise-level organization in North America and Europe, 55% of the population have already adopted continuous testing. In contrast, 40% of respondents measure CT effectiveness via user feedback and adoption and business KPIs. However, the gradual rate of increase in maturity demonstrates that the path to continuous testing is not an easy one. The fact that 68% of respondents say designing and maintaining meaningful test cases that align with end-user expectations is challenging or extremely challenging affirms that cultural, process and technological challenges remain a barrier to success.
Is continuous testing going to be a game-changer in a post COVID world?
Of course, there is not an inch of doubt in it. At the beginning of 2020, none of us would have imagined that our lives will change forever. While work from home was still a known concept, no one has thought about working from home for an indefinite time. Today work from home is no longer a luxury but a necessity. And with continuous work from home, businesses are faced with new challenges. How do they bring the team together when they are in a different time zone and geography? The need of the hour is more and more Cloud-based solutions and SaaS products that can facilitate teams to better collaborate.
This means software companies must rigorously test and release new products to bring the teams together, improve communications among the virtually dispersed team members, and build a sense of trust and responsibility.
However, time to market is a crucial differentiator of service and customer expectations, which means you need to be fast and agile. Then how do software testing teams operating remotely deliver on this pressing challenge?
The answer lies in continuous testing that seamlessly integrates into the continuous delivery pipeline that provides quality outcomes at all stages. A few of the best practices to ensure successful continuous testing are achieving maximum coverage through test automation, integrations with CI/CD tools and code repositories such as SVN, Git, Bitbucket, etc., creating a constant feedback loop to find bugs/defects faster, and putting remediations out quickly.
How is QMetry prepping up for the future?
QMetry has been one of the early players who has identified the opportunity and has been building up its portfolio around intelligent testing through AI and ML techniques. AI and ML will continue to dominate the software testing scene as the process of quality assurance transforms into a self-adapting and self-learning activity. Foreseeing this early, with features like QQBot, QMetry Test Management leverages AI and ML to reduce duplication of test assets and thereby ensure increase in reusability.
On the automation side, as I pen down this article, another QMetry’s product QMetry Automation Studio has already deployed this feature of “Self-Healing” where objects automatically update themselves, reducing the maintenance cost and efforts. With this I also think that there will be lot of companies who would still be using legacy tools and for them migrating out of those old systems is going to be the only solution.
Modern tools such as QMetry Test Management are scalable, secured, and future ready. Team friendly process and ready-to-use migration utility will help many companies to easily move out of legacy ALM products and into the new world of modern tools.
I am delighted to share that 2021 will see further investment and opportunity created by AI and ML led test management and automation, resulting in the following outcomes:
- Configurable, scalable, adaptable micro services based QMetry Digital Quality Platform (QDQP)
- Multi-level approval workflows
- End-to-end BDD execution capabilities from automation to management
- Integrated workflows between Test Management, Test Automation and Test Execution
- Cognitive test authoring
- Advanced actionable insights through Predictive and prescriptive analysis