Watch out for these software testing trends in 2021

Modern clients demand a seamless software experience on their terms and through their preferred channels in this fast-changing environment. To satisfy these rising demands, businesses are under tremendous pressure to adapt, develop, and offer competitive customer-centric solutions constantly. Many worldwide companies have realised the importance of software testing and quality assurance (QA) and have begun to include it in the early stages of the software development life cycle. Early adoption is the most straightforward and cost-effective method for assuring faster software release cycles, shorter time-to-market, and high-quality software products.

With the emergence of next-generation technologies like artificial intelligence, cloud computing, robotic process automation, mobility, IoT, and DevOps, businesses are now experiencing problems in software development and testing. We’ve detailed recent software testing trends to help you deal with the current challenges and realities of software development. These trends could help organisations speed release cycles while retaining high-quality software products and without sacrificing cost-efficiency.

Risk compliance and Cyber security testing

Security testing has seen a substantial rise in 2021,  and the global tech community has recognised its value when it comes to protecting brand loyalty and avoiding economic losses. It’s no surprise that COVID-19 brought with it a sea of security concerns. End-user privacy and safer transactions are on top of everyone’s list. Wherever this digital transition takes us, testing engineers will undoubtedly see a lot of changes. The good news is, QA and Software testing are constantly evolving. AI is poised to be at the forefront of discussion and it won’t be long before the manual testing process is mostly automated. Businesses have to keep up with the latest in software testing and QA trends to stay ahead of the curve. 

AI and Machine Learning on the rise

Software developers can improve their testing techniques and shorten their release cycles by using AI. We can expect AI to be used in more testing areas related to analytics and reporting in the future, such as: 

• Identify test cases that require both manual and automated testing with AI algorithms
• Removing unnecessary test cases with optimized test suites;
• Identifying essential keywords from an RTM ( Requirements Traceability Matrix) for optimal test coverage
• Identifying defects and application areas related to business risks.
• Forecasting important characteristics and metrics that define end-user behaviour and suggest places where improvements could be made.

Aside from this, AI is being used to build automation testing tools. These will aid QA teams with creating new tests, resolving issues, and reducing the need for human participation in the testing creation and maintenance process. Machine learning is also used in automation. Complex neural networks and algorithms can help anticipate how particular activities will turn out. We anticipate analytics-centric concepts employing machine learning to gain momentum in tackling future difficulties since apps require ongoing testing and validation.

Enhanced QAOps development

In today’s digital environment, apps must be released more quickly without sacrificing quality. QAOps is a hybrid of QA and DevOps. QA’s function used to be limited to application software testing, but it now plays an important part in all phases of software development. DevOps is the collaboration of operations and development professionals to support the whole software life cycle. It entails a set of software development methods centred on bug fixes, feature creation, and handling frequent upgrades.

• Execution of testing and quality assurance tasks using CI/CD, to achieve high quality and rapid delivery.
• For speedier time-to-market, QA engineers collaborate with the development team.

It’s simple to understand why QAOps is gaining popularity; it combines continuous testing with a DevOps methodology. We can assume that incorporating QAOps will assist organisations in bringing properly tested goods to market without accepting compromises when it comes to time. 

Smart devices and Internet of things (IoT) testing

In the realm of technology, the Internet of Things (IoT) is emerging as a dynamic and rapidly increasing idea. It’s a collection of massive networks that collect and share data through the internet, including devices, people, processes, and technology. According to a Gartner research estimate, there will be 25 billion internet-connected gadgets by 2021, up from 14.2 billion in 2019. These figures demonstrate the growing interest in the Internet of Things, particularly in the healthcare industry. Because software is embedded in IoT devices, and as more devices connect and generate huge quantities of data, an efficient IoT testing approach is required. In the coming years, it will be required to defend software from vulnerabilities and attacks, as well as to maintain data security. Innumerable testing combinations will be required to test devices, communication protocols, operating systems, and platforms as new gadgets are released regularly. Software testers will be in high demand to do testing and ensure security, data integrity, performance, interoperability, simplicity of use, authenticity, and the monitoring of any delays, among other things. It will be necessary for QA teams to broaden their expertise and improve their abilities to do IoT testing. End-users will benefit from well-connected, secure, and efficient smart gadgets.

Big data testing in high demand

Enterprises are frequently confronted with a wide range of data kinds and massive data volumes. Big data is growing in importance in a variety of industries, including healthcare, telecommunications, retail, finance, technology, and the media. End-to-end testing is required for mining organised and unstructured data. Big data testing helps businesses improve their market strategies and target audiences by allowing them to make educated decisions based on accurate data validations. Big data testing guarantees that data is of high quality, accuracy, and integrity, which is necessary for any company to make educated decisions. Because corporate data is becoming more complicated every year, big data testing will play a significant role in 2021.

QA test automation

As more organisations use the newest agile and DevOps techniques to meet the need for quality at speed, test automation services have become a critical testing component. In 2019-2020, 44% of IT firms automated 50% of their testing, with the worldwide automation testing industry expected to reach USD 68 billion by 2025. Automated testing aids teams in performing repetitive activities, identifying issues more accurately, providing continuous feedback, and completing test coverage. As a result, companies that use automated testing in their quality assurance procedures save a lot of time, money, and human resources. Here are a few key automated testing trends that will impact the software testing industry’s future:

• Codeless test automation: These testing solutions use artificial intelligence (AI) and visual modelling to automate testing scenarios without the need for coding expertise.
• Robotic process automation testing: this entails the use of automation testing technologies to assist testers in replacing regression and load testing, therefore drastically decreasing the amount of time and manual input required; it’s especially effective when dealing with big and complicated data sets.
• Agile and DevOps combined: this form of automation testing delivers excellent test coverage for agile teams, allowing organisations to deploy quicker and save money.

A move away from performance testing and toward performance engineering

Performance testing and performance engineering are becoming increasingly important as the need for quick loading and high-performing apps grows. Because performance testing only detects performance bottlenecks after development is complete, the trend is moving toward performance engineering, in which QA teams design the application from the start of the software development life cycle. Early in the development cycle, the major aim is to fix and prevent performance concerns. This can help save a lot of time in the long run by decreasing the need for troubleshooting and rewrites. Performance testing involves the collaboration of software, hardware, performance, configuration, security, usability, and business values, among other things. It is critical for fulfilling rapidly changing demands, shorter development cycles, and frequent application releases.

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SANTOSH KOSHY