In this fast-evolving world, modern customers expect a frictionless software experience on their terms and in their preferred channels. To meet these increasing demands, enterprises are under immense pressure to continuously adapt, improve, and deliver competitive customer-centric solutions. Many global businesses have come to appreciate the value of software testing and quality assurance (QA) and have begun embedding it in the early phases of the software development life cycle. Early adoption is the simplest, most cost-effective way of ensuring accelerated software release cycles, reducing time-to-market, and delivering high-quality software products. Companies are also facing challenges in software development and testing with the rise of next-gen technologies such as artificial intelligence, cloud computing, robotic process automation, mobility, IoT, and DevOps. To help you handle the current pressures and realities of software development, we have outlined recent software testing trends which may help businesses to accelerate release cycles while maintaining high-quality software products and without compromising on cost optimization.
The rise of artificial intelligence and machine learning in testingBy leveraging AI, software teams can optimize their testing strategies and speed up their release cycles. In the future, we can expect AI to be used in more testing areas related to analytics and reporting, such as:
- Utilizing AI algorithms to identify the test cases that need both manual and automated testing;
- Optimizing test suites by determining and eradicating unnecessary test cases;
- Ensuring optimal test coverage to identify essential keywords from a Requirements Traceability Matrix;
- Predicting key parameters and metrics which are defining end-user behaviour and identifying areas to focus on; and
- Detecting the application areas and any defects associated with business risks.
The significance of QAOps developmentThe digital world now requires a faster release of applications without any compromise in quality. QAOps is the combination of QA & DevOps. In the past, the role of QA has been restricted to application software testing but now it is playing a crucial role in all phases of software development. DevOps is about operations and development engineers working together to service the full software life cycle. It involves a set of software development practices that focus on fixing bugs, building features, and handling frequent updates. QAOps continues to rise in software testing trends because it enables the automation of processes and improves workflows between IT operations, software development, and QA. It allows the software to be tested using the continuous integration (CI)/continuous delivery (CD) pipeline, rather than having the QA team operate separately. In a nutshell, QAOps works on two principles:
- Testing & QA activities execution with CI/CD, aiming to achieve high quality and quick delivery.
- QA engineers working in parallel with the development team for faster speed-to-market.
Internet of things (IoT) testing to boost digitally connected smart devicesIoT is emerging as a dynamic and fast-growing concept in the technology field. It is an integration of giant networks: devices, people, processes, and technologies collecting and sharing data over the internet. According to a research report by Gartner, there will be 25 billion internet-connected devices by 2021, compared to 14.2 billion devices in 2019. These stats clearly represent the rising interest in the IoT field, especially in the healthcare sector. Since the software is included in IoT devices and more devices are connecting and generating large amounts of data, an effective IoT testing strategy needs to be implemented. It will be needed in the upcoming years to protect the software from vulnerabilities and threats and ensure data security. With the frequent releases of new gadgets in the marketplace, innumerable testing combinations will be needed to test devices, communication protocols, operating systems, and platforms. There will be a major demand for software testers to perform testing and maintain security, data integrity, performance, compatibility, ease of use, authenticity, monitor any delays, etc. The QA teams will be required to expand their knowledge and enhance their ability to perform IoT testing. The result will be well-connected, secure, and efficient smart devices for end-users.
The increasing demand for big data testingEnterprises regularly deal with diverse data types and enormous data volumes. Big data is getting bigger and it is playing a vital role in many business sectors, including healthcare, telecom, retail, banking, technology, and the media. The mining of structured and unstructured data requires end-to-end testing. Big data testing assists organizations in enhancing their market strategies and improving their targeting by letting them make informed decisions drawn from precise data validations. Big data testing ensures the quality, accuracy, and integrity of data that is essential for any organization to make informed decisions. Since enterprise data is growing in complexity every year and business strategies are being built using it, big data testing will play a major role in 2021.
The incorporation of QA test automationTest automation services have become a crucial testing component, as more and more businesses are adopting the latest agile and DevOps processes to fulfil the demand for quality at speed. 44% of IT organizations automated 50% of their testing in 2019-2020 and the global automation testing market size is anticipated to reach USD 68 billion by 2025. Automated testing helps teams to execute recurring tasks, identify bugs more precisely and quickly, provide continuous feedback, and execute test coverage. Consequently, organizations that adopt automated testing in their QA processes save a significant amount of time, money, and human resources. Here is the list of few top automation testing trends that will shape the future of the software testing industry:
- Codeless test automation: these testing tools are built using AI and visual modelling to facilitate the automation of testing scenarios without requiring any coding knowledge.
- Robotic process automation testing: this involves automation testing tools that help testers replace regression and load testing, massively reducing the time and manual input requirements; it’s particularly useful for dealing with large and complex data sets.
- Blend of agile and DevOps: this type of automation testing provides high test coverage for agile teams, aiding businesses in faster deployments and cost management.