Top Software Testing Trends to Focus in 2021
The importance of software testing has escalated worldwide due to the Covid-19 pandemic forcing people to shift most of their work dealings from home. Therefore, the need to upkeep applications, we need software testing that directly maximizes business operations.
In the earlier days, software testing was limited to finding errors and offering product improvement recommendations. These days, the impact of technological advancement has transformed the approach abided for testing and quality assurance. The usefulness of various tests across the SDLC (Software Development Life Cycle) indicates this process’s extensiveness.
Why is Software Testing Gaining Importance?
The count of applications used by organizations is growing which is further fueling the cost related to safety and security measures. This is the reason why software testing is under the spotlight for all good reasons. As per the World Quality Report, 60% of the organizations agreed that cost is the biggest test environment challenge. Furthermore, testing finances are now merged together with software engineering finances since QA is actively implanted in the development cycle. As a result, the majority of the businesses have started acknowledging the importance of quality assurance and have put their trust in software testing companies for offering specialized assistance.
“The principle objective of software testing is to give confidence in the software.”
This year has witnessed noticeable changes within the software testing arena. Several major players have broadened their operations to adapt to the remote working model. Based on all such tweaks and upgrades, the latest software testing trends already in motion and the ones set to impact in 2021 are cleared discussed in this blog.
Fresh Opportunities with Artificial Intelligence (AI) and Machine Learning (ML)
The primary purpose of incorporating Artificial Intelligence in the software testing model is to achieve a smarter testing approach. The enhancement in reasoning and problem-solving skills is a result of AI & ML, which automate and upgrade the overall software testing process. Furthermore, the dependency on manual testing is reduced with the availability of artificial intelligence.
In 2019, AI & ML together worked suitably to speed up the test automation process. Moreover, these technologies’ combined effect was also reported to improve test quality, classification of defects, and interaction with an application under test (AUT). It is expected that in the following year, AI & ML will take charge of more complex tasks within the SDLC and further encourage test creation and test execution.
Transformation to QAOps
The precise meaning of QAOps is the integration of quality assurance (QA) and CI/CD (Continuous Integration/Continuous Delivery) channel. In simple words, this model stresses on integrating the software process with the CI/CD pipeline. Based on this arrangement, the QA team must close work with the operation team and development team.
Under QAOps, DevOps is merged with continuous testing to ensure any software changes are readily forwarded to the CI/CD pipeline’s production phase. It prevents the hassle of conducting software testing at vague intervals without any clarity on quality issues.
Performance Testing Driven By Performance Engineering
The performance testing of applications has reached a higher level where security, business value, configuration, and usability are vital benchmarks. Different software testing service providers have revamped their priorities inclined towards a user-centric approach to match up with this tendency. It includes the prevention of potential performance defects at the initial stages of the product’s life cycle. The advantages of performance engineering and testing engineering have transformed performance objectives for an application, including speed, stability, and scalability under varying circumstances. The system is now analyzed for discrepancies with the search operation thoroughly conducted to find their root causes starting from the development process.
Popularity of IoT & Big Data Testing
At present, the demand for the Internet of Things (IoT) has transformed into a global situation. We are living in a connected world that is filled with millions of devices that are directly responsible for increasing testing activities. As we can see, the components to be tested vary between platforms, devices, operating systems, and protocols; the overall software testing & QA market is likely to experience escalated demand for performance, security, usability, and compatibility testing.
A similar situation can be seen in terms of Big Data. With the rising demand for IoT-enabled applications, there are better opportunities focused on widespread data volumes required and accumulated for larger projects. Therefore, testing of big data certainly improves an organizations’ capability to confirm data-driven decisions. Furthermore, its effect can also be seen in refining market strategies with a broader perspective.
The above-mentioned software testing trends are aligned with growing business needs to bring positive outcomes. The proliferation of AI, ML, and IoT has already created a major breakthrough and prominent upgrades related to software testing are likely to occur in 2021. QAOps and Performance Engineering are the promising segments gaining higher traction from leading software testing companies like ImpactQA, to reap bigger and better benefits in the coming years.