Published: 15 Oct 2019
Top 16 Software Testing Trends to Watch Out in 2020
Last Updated: 17 Mar 2022
Enterprises across the globe continue to foray into the challenging and turbulent market space with a need for quality products to gain their market place. Software development of applications and products demands effective software testing that has a shift-left approach today. Software testing is taken up along with the software development life cycle (SDLC) to enable faster releases and deliver quicker ROI. Hence, looking at the criticality of software testing in the SDLC, more and more enterprises are looking out for end-to-end testing cycles and hence continue to invest in next-gen software testing services.
Contents 1. Top 16 Software Testing Trends to Look Out in 2020 2. Test Automation to Ramp Up Quality for Agile & DevOps 3. User Experience to take a Leap with Usability Testing 4. Big Data Testing to Empower Enterprises 5. IoT Testing to Boost Well-Connected Smart Devices Market 6. AI & ML in Testing goes Mainstream 7. Surge in Blockchain Testing 8. Demand for Cyber Security Testing on the Rise 9. RPA Testing is Even Bigger than Automation Testing 10. Performance Testing to Shift towards Performance Engineering 11. QAOps 12. Mobile Test Automation 13. Testing to become effective with Behavior-driven 14. Use of Scriptless Automation Tools 15. Multi-device Testing 16. Integration of tools 17. App penetration testing 18. Conclusion
A recent Nelson Halls’ report states Next-gen testing accounts for 24% of the software testing services spending. It is the fastest growth offering with a 12.8% CAGR for 2018-2023. Growth is driven by mobile testing, which still accounts for ~75% of all next-gen testing spending.
Evidently, software testing continues to play a key role in the emergence of software development methodologies like Agile and DevOps. In addition, with the industry 4.0 in place, the trend indicates a shift towards automation, cyber-physical systems, the internet of things (IoT) and the industrial internet of things (IIoT), cloud computing all around and the booming up of Artificial intelligence with Machine Learning trends. Testing of these technologies demands effective test strategies and testing methodologies to be in place. Interestingly, enterprises should ponder some of the major software testing trends to take the complete benefit of the market segment to derive products of their requirement.
With the latest agile and DevOps processes, faster and quality releases as the underlying motto, most enterprises continue to adopt test automation. Undoubtedly, test automation has already penetrated into the industry in testing repetitive tasks quickly and efficiently. With DevOps substantiating proper collaboration between departments, test automation tools continue to lead the software testing space. The proper usage of test automation tools helps to achieve faster release cycles, better quality, and quicker ROI.
With the rapid influx of mobile and e-commerce applications, the significance of software testing has been witnessed to test the operating systems, platforms, and devices. The mobile apps continue to be the major business enabler for businesses today; an effective and great user interface with a streamlined usability flow is the need of the hour. Along with a quick app loading time, users continue to show preference to apps that have a great Usability embedded in them. Poor usability can affect customer and brand loyalty; hence usability testing identifies all the bugs before the application is released to your users.
Enterprises across industries continue to deal with huge data volumes and diverse data types. The mining of any amount of structured or unstructured data defined as Big data needs effective testing. Big data testing helps to make improved decisions with accurate data validations, and helps improve market targeting and strategizing.
It is expected that the number of connected devices will be more than 20 billion by 2020 when compared to the figure of just 6.4 billion during 2016. These figures represent the massive expansion and the need for an effective IoT testing strategy. It includes the testing of Operating systems, communication protocols, along with software and hardware of the IoT devices. Most enterprises have already started identifying the need for an effective IoT testing strategy to enable efficient and well-connected smart devices.
Especially, testing for vulnerabilities in IoT devices is an emerging business need as IoT typically encompasses all products that are connected to the internet in one way or the other. IoT systems collect data while in usage from various interconnected devices and share information with their manufacturers without the users being aware of it. Further, manufacturers in their haste to get new design features for smart products continue to overlook the complications arising out of security concerns of system’s software and hardware.
There is a possibility of vulnerabilities seen in the hardware (chipset) of many new IoT products which is susceptible for multiple threats that needs to be effectively tested. Even the software that is included in the IoT devices usually does not get any sort of security testing done while at the manufacturers end. Thus, numerous IoT devices continue to get hacked due to the susceptibility affecting the entire network of users. Hence, it is essential to get all the IoT products and devices security tested to avoid threats and vulnerabilities.
According to a leading Research analyst, Artificial Intelligence (AI) will be omnipresent in all spheres of technological innovations. It will become the top investment priority of CIOs by 2020. The market for AI is expected to be around $6-7 billion in North America alone. Machine Learning (ML) and user interfaces such as Speech recognition and gesture recognition will advance in the future. The prediction of various tasks based on complex neural networks and algorithms has literally changed the outlook of technology and these applications also need rigorous testing and validations.
With the world completely moved towards digital transformation, there is much pressure to balance market requirements and build a system which is predictive and scalable to cater future needs of software. Testing needs to embed AI which perfectly imitates human behavior using machine learning and predictive analytics. Going forward for the upcoming latest applications in connected world evidently needs testing to use AI and ML to automate.
There is a rapid expansion of the virtual currency Bitcoin usage. A recent report by McKinsey states that Blockchain is a nascent technology with the potential to bring about step-function improvements in financial transactions. Blockchain testing helps to enable smart contracts and ensures fraud protection.
Undoubtedly, the Blockchain technology has revolutionized the way businesses are dealing with digital currencies such as Bitcoin. These Blockchain applications are not limited to the financial world and its smart contracts are being used in every field of business from energy sector to governmental services. This wide range of applications support brings in new challenges to Blockchain debugging.
Moreover, once the smart contract is implemented, its execution cannot be reversed and hence, smart contract codes define how seamlessly the software performs even with increased workloads. This entire process of Blockchain testing calls for efficient outsourced next-gen testing services, specialized in debugging the code to deliver productive Blockchain applications.
Undoubtedly with the digital revolution, there has been the emergence of various security threats. The CIOs of enterprises continue to realize the importance of security testing of their applications, network, and systems to ensure not only secure transactions but complete protection of end-users critical data. Thus, security testing has gained a lot of importance as it safeguards brand loyalty and prevents economic losses.
RPA can also be named as an extension of Automation as it can be applied to anything which is in a structured form, unlike automation which needs a software product to work upon. RPA can be used with very complex processes that can be automated with AI. Specifically, it is a style of automation wherein a machine, or computer mimics a human action and helps in the completion of rule-based tasks. The Robot led automation has the true potential to change the workplace and does all tasks performed by automation testing tools.
Product performance has earlier been the major segment of testing but now, it is slowly shifting towards performance engineering which is not an easy process. The performance engineering process involves the collaboration of hardware, software, configuration, performance, security, usability, business value and it ensures to deliver the highest value that exceeds end-user expectations.
The digital world is effectively in need of software applications that are released faster with no compromise in the quality of the applications. Earlier, the need for testing teams was limited only for performing application testing, but now the importance of QA is effectively increasing. QA is playing a crucial part in the complete software development.
Similarly, DevOps is another automation approach that has gained crucial importance in order to deliver applications faster. And the combination of these two methodologies, i.e. QA and DevOps brings a new practice called QAOps or DevTestOps.
With this practice, the testing, development, and operation teams can be on the same line by erasing the boundaries. With this latest approach, continuous testing can be combined DevOps and thus assures changes in software are made effective with the practice of Continuous Integration (CI) and Continuous Deployment (CD). Thus, software testing is not practiced at indefinite intervals and the applications are delivered with quality and without delays.