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In order to stay at the top of their game and remain competitive, organizations aim at delivering excellent customer experience by leveraging the advancements in digital technologies. In doing so, they envisage a raft of deliverables for the stakeholders. These include deploying superior quality software capable of performing in an omnichannel environment, maintaining strict adherence to industry safety protocols, achieving strong integration with third-party APIs, and implementing quick updates of software based on market demand, among others. QA software testing lies at the core of this vision to ensure the above deliverables are realized to the satisfaction of the stakeholders, including the end-users. However, this is easier said than done, given the plethora of challenges faced by any software testing company.
The challenges include testing the software on multiple devices having different hardware configurations, screen sizes, and resolutions; mandatory adherence to growing industry safety protocols; the need to perform frequent updates; the existence of diverse operating and test environments; and the need to ensure faster release cycles. To address these challenges, enterprises need to rethink and reengineer their test methodologies and systems. To conduct both shift-left and shift-right software quality assurance, enterprises have to adopt the emerging technologies in testing.
Emerging Technologies Shaping the Future of QA Software Testing
The success of any software application in the market lies to a great extent in the ability of the QA testing services to deliver a superior customer experience. Consequently, such services incorporate some of the emerging technologies listed below to remain competitive.
Robotic Process Automation (RPA): Statistically, the RPA market is expected to touch $7.2 billion by 2025, thanks to its ability to automate business processes quickly and effectively. As an extension of automation, RPA can be applied to any structured element with AI wherein machines (read: robots) mimic human actions to complete rule-based tasks. Incorporating RPA in the QA software testing pipeline can help automate large and complex data sets through an easy-to-use interface. RPA has the capability to perform all types of testing that use automation testing tools, thereby eliminating the need to write test scripts.
Artificial Intelligence (AI) and Machine Learning (ML): There is a growing need for businesses to predict what the customers want for strategic reasons. At the same time, the need is to detect errors in the code by analyzing any preexisting patterns. AI involves the incorporation of human intelligence in computer systems. ML, on the other hand, is a subset of AI that envisages systems to learn from data, identify patterns, and take decisions with minimal or no human intervention. AI and ML have a crucial role to play in driving quality engineering where predictive analysis is done on customers’ data to generate targeted outcomes. Here, the QA software testing tools analyze test results to predict vulnerabilities, optimize code, and report defects. AI and ML can be utilized by any software testing company to gain data-driven insights in order to optimize the testing process. Accordingly, the focus is on doing the right kind of testing instead of on testing more. This approach helps to enhance the quality and scalability of the test code, reduce the time and cost of testing, and eliminate deficiencies in old test approaches.
Big Data Testing: Counted among the latest software testing trends, big data testing has caught on across industries. This is due to the fact that most enterprises have to deal with huge volumes of structured and unstructured data and their diverse types. The mining and validation of such huge sets of data lead enterprises to make informed decisions, improve market reach, and draw strategies for business growth. Going by statistics, the big data market is predicted to reach $229.4 billion by 2025, largely attributed to the enhanced usage of IoT devices. Since enterprises depend on data for decision-making and need big data testing to ensure the quality, integrity, and accuracy of data, it is incumbent for any quality assurance company to leverage big data testing and deliver valuable insights about various products and services. These insights can then be used to improve the quality of products and services and deliver better customer experience.
QAOps or DevTestOps: Amidst a growing clamor to develop quality software applications quickly, enterprises are adopting DevOps to enhance collaboration and eliminate silos between the development and operations teams. This has led enterprises to devise a combination of QA and DevOps to incorporate QA at the heart of every software development-related activity. Further, this approach ensures software quality assurance is performed as a continuous activity rather than at specific intervals to achieve faster delivery of quality applications.
Conclusion
The above-mentioned technologies are increasingly being adopted by enterprises to re-engineer their legacy infrastructure and keep themselves updated with the latest software testing trends. These ensure the deployed products or services to be fully functional and deliver excellent customer experiences – underpinnings of being competitive and profitable.