The latest advent of automation tools alongside Low Code / No Code, Agile / Scrum tools and adoption of Cloud and DevOps have created a tectonic shift towards the software development and testing lifecycle over the past decade. It is inevitable for most organizations that are undergoing digital transformation to follow a certain order that leads through some evolving testing practices known as Quality Engineering.
Evolution of Software Testing
During the nascent age of software development, the primary objective of quality testing was to identify bugs that affected the outcome of a program. The past two decades have seen a paradigm shift in testing methodologies around the globe.
The Monolithic architecture which was more of an inside-out approach was pretty close-knit and was resistant to major changes. When the architecture based on micro-services stepped in, it enhanced adaptability and contributed to the evolution of testing as well. While the age-old waterfall model followed a restrictive pattern of interaction between the developers and testers, the introduction of DevOps and Agile practices induced a collaborative practice.
This resulted in the collapse of the Chinese wall between the developers and testers as it laid down a new scope of communication between the two worlds. Shift-Left Testing became the new normal, which led to the overhaul of many traditional testing approaches. The CI/CD method that we follow today even owes much of its dominance to the effective utilization of automation through the various developmental phases of an app.
The testing methodologies that are in use these days are both well-conceived and innovative to suit the needs of a dynamic IT world. While businesses are picking up AI as a part of their tech stack, AI-based testing has left its footprint in every sense. Hence, in an attempt to test such software applications accurately, most enterprises are engaging their tools using ML and AI algorithms.
Futuristic Trends that Shape Up the Quality Engineering Landscape:
- Robotic Processes with ML and AI
Software testing now caters to a more complex arena while leveraging Machine Learning, Artificial Intelligence, and Robotic Process Automation. The focus is more intense on implementing test automation more efficiently besides extending exhaustive test coverage. RPA curbs much of the human effort and time through a smooth execution of repetitive tasks. Deep learning algorithms are now teaming up with AI to turn defect tracking more effective.
- Getting Script-less
Gone are the days when writing code was unavoidable. Organizations are now using automation solutions in the form of scriptless automation tools. Automating the test cases prevents wastage of human effort; going scriptless minimizes the tech-talent dependency and allows a business to concentrate more on its transformation journey.
At Yogiti, our in-house framework for Low Code / No Code test automation has not only proved effortless, but it has delivered a higher ROI as well.
- Integrating DevOps
Integrating DevOps with QA has shown us a new high in building up a robust feedback loop thereby improving the overall flow of software testing. This, in turn, has helped new businesses in launching their apps with new features within pre-set deadlines. DevOps automation forms a vital segment of the sprints that are involved in the continuous testing architecture.
It enables your organization to test your apps more quickly, uniformly, and throughout the SDLC. It leaves a positive impact on the testing landscape by improving the market time alongside the app quality.
- Security Testing
Security Testing has always been a necessity for most organizations owing to the occurrence of cyberattacks all around. Identifying and resolving these issues caused due to vulnerabilities and risks is truly essential for every organization. With the increase in digital adoption the new threat patterns, the different vulnerability issues and various malware introductions have pushed the think tanks to redefine security test strategies.