Software testing is making many moves. From AI to ML, it is continually innovating and advancing with the shifting technology landscape. Also, the software testing market is growing rapidly. Did you know that the Software Testing Market size exceeded USD 40 billion in 2019? And is expected to grow at a CAGR of over 6% from 2020 to 2026?
Because software testing is so important, every enterprise needs to stay on top of their software testing game heading into the next decade, wondering how to do that? Well, to take your software testing game to another level, check out these 12 exciting software testing trends that we predict will be big in 2021.
Now we know predicting the future is far from simple – 2020 has taught everyone that. However, looking ahead with renewed optimism, we’re anticipating the most important software testing trends for 2021, just like we predicted them last year for 2020.
Let’s find out.
Here are the top 12 software testing trends you need to look out for in 2021:
Codeless test automation tools are built on Artificial Intelligence technology and visual modeling, enabling the accelerated formation of test cases that cater to test automation. Using such incredible automated testing tools, QA engineers can create test case scenarios with zero coding knowledge and reduce the time spent on recurring test cases. The growing adoption of codeless automated test tools will be one of the software testing trends you need to look out for in 2021.
Some of the advantages of codeless automated testing are as below:
- Simple to Review: Because these test cases are generated without any code, these are clear and readable for folks who do not understand how to code. Therefore, such test cases can be reviewed effortlessly by even non-technical stakeholders in the project.
- Low Learning Curve: With automated codeless testing, test cases can be generated even while the user has entirely no familiarity with programming languages or coding. Therefore, it doesn’t take extra time and effort to learn and start building the test cases.
- Save precious resources: With codeless automated tests, the QA engineers do not require learning new programming languages and do not necessitate a new person to be employed for coding skills. Therefore, resources, costs, and time can be effortlessly saved.
- Effective: As the learning curve is steady and slow, and the generation of test cases doesn’t require complicated syntax, the test case formation is rapid and accelerates the overall automation process’s effectiveness.
It is projected that AI usage will continue to grow in simply about each facet of creative technology because of the growing number of apps we use on our interconnected planet. Current investment in Artificial Intelligence is anticipated to be USD 6-7 billion in North America alone. By the year 2025, Artificial Intelligence overall global investment is expected to reach almost USD 200 billion.
We will expect to witness apps of Artificial Intelligence in more testing zones — most of which will apply to analytics and reports:
- Test Suite Optimization: Determine and eradicate unnecessary and redundant test cases.
- Log Analytics: Discover exceptional test cases that necessitate both manual & automated tests.
- Defect Analytics: Detect app areas and defects that tie to company risks.
- Predictive Analytics: Estimate key parameters and specifications of end-customer’s behaviors and discover app areas to concentrate on.
- Confirms Test Needs Coverage: Taking out essential keywords from the RTM (Requirements Traceability Matrix).
Software testing and QA teams can leverage Machine Learning (ML) and Artificial Intelligence (AI) to improve their automated test strategies and keep pace with recurrent releases — with the aid of analytics and reporting. For instance, software testers can use AI algorithms to discover and prioritize the scope for additional automated testing. Besides sorting out software tests’ workloads, AI-powered test apps can optimize test suites after identifying unneeded test cases and ensuring optimal test coverage by inspecting keywords from an RTM.
The pillar on which smart automation rests is ML. As per the Capgemini World Quality report, 38 percent of companies have planned to execute Machine Learning projects in 2019. Business experts forecast that this number will increase in the upcoming year. Even though projecting end-customer behavior patterns is still a tough job for human intelligence, Machine Learning-enabled foretelling analytics can strengthen human intelligence by detecting underexplored sections in apps. These insights can be utilized to predict likely parameters of user behavior using accessible historical data. While Machine Learning in software testing is, at the moment, an electrifying prospect rather than a broadly-accepted practice, in the upcoming years, we can anticipate analytics-centric ideas to gain traction in determining possible challenging areas to cover up with testing.
Test automation is the one solitary method to adequately test high test coverage in every sprint and give the superior quality and instant feedback and response that we look for when working in Agile. The Agile project with no automated testing is effectively a waterfall project in stages. Automated testing is going mainstream when 44 percent of IT companies automate 50 percent or more of all testing in the past year of 2019-2020. We estimate that more and more test automation adoption will persistently be on the rise in 2021.
According to a recent report of MarketsAndMarkets, “the global test automation market size is anticipated to rise from USD 12.6 Billion in 2019 to USD 28.8 Billion by 2024 at a CAGR (Compound Annual Growth Rate) of 18.0 percent during the prediction period”. Automated testing support teams to execute recurring jobs, identify flaws rapidly with extra precision, persist feedback loop, and test coverage. Consequently, companies can easily save enormous manpower, time, and cost if they integrate test automation in their QA processes.
As per a Gitlab survey, automated testing is gaining immense popularity, and 12 percent of companies entirely automated tests efforts. Such latest trends in automated testing make Quality Assurance extra productive and support teams early on bug detection, executing recurring jobs, constant feedback, and test coverage.
The IoT is a fast-growing concept in the technology field. Shortly, the Internet of Things will accept the 5G standard. It releases several new gadgets in the marketplace, and combinations for testing between protocols, devices, platforms, and OSs are countless. The software testing and QA market will raise the demand for performance, security, compatibility, usability, and data integrity testing. Only a small number of companies implement the Internet of Things testing strategies. However, this trend is projected to grow in the upcoming decades.
Currently, companies are slightly behind the curve — 41 % have an adequately mature Internet of Things tests strategy in place, while 30% of participants intended to put the Internet of Things functionality into their products. A similar thing proceeds with big data. The increase of IoT apps has paved the way for added varied data volume generation, which created the requirement for big data testing, for example, big e-commerce companies like Amazon. Accordingly, big data testing confidently impacts enterprises’ capability to validate info, create data-driven verdicts, and improve market strategizing and targeting.
Another big thing that makes big data testing a gradually more common practice is that data is the new ruler to build marketing strategies. Company processes are becoming highly complex every year, so big data testing requirements would not be going anywhere in 2021.
As the number of platforms on which any application is obtainable defines the captured market, customer experience carries extra credence. It becomes the driver for fast-changing needs, shorter development cycles, and more frequent releases.
In response to this trend, IT and software companies have started reconsidering their priorities in favor of a consumer-focused approach for quality standards on every single phase of the SDLC — chiefly to solve and prevent likely performance problems at the very initial phase of the product’s life cycle. As a result, performance testing goals, like stability, scalability, and speed of the app under varied situations, have transformed into examining the system’s insufficient performance and knowing where it is rooted in the development procedure.
When done right, performance engineering enables QA engineers or testers and developers to build necessary performance metrics from the initial design. Being more of corporate culture than a series of practices, performance engineering anticipates teams to move precedent running checkbox testing scripts to examining every single section of the system, counting customers and business value.
The performance of any app can significantly impact the organization’s bottom line. Any sort of crash can cause thousands to millions of dollars loss—while identifying the source of the bug in an increasingly complicated system can take additional time as well. This, in turn, means user experience and the maintained performance of your app should be incorporated right through the application’s lifecycle, not merely when it is first introduced. With an increasing number of DevOps teams constantly deploying apps, performance engineers should regularly test to confirm additional integration stability and quality.
The practices of DevOps and Agile have emerged as the most preferred for several organizations. Want to know the reason why? Mutually these methodologies are perfectly designed to favor swift deployment and strong teamwork between developers and QA engineers. Agile is a constant procedure of development and testing, and on the flip side, DevOps is well-known for the association of cross-departments.
Owing to this, Agile and DevOps facilitate top-quality products at a fast rate, and several enterprises will adopt the techniques in the imminent future. Agile is rising as the big trend, as it can bring various advantages to the enterprise, and the key benefits are as follows:
- Speedier time to market
- Highest productivity and efficiency
- Reduction of Rates
- Identification of bugs in the primary phases of the product life cycle
- Accomplishing the highest levels of quality standards of product
Even though there are subtle dissimilarities between DevOps and Agile Testing, persons working with Agile will discover DevOps a slightly more easy and familiar to work with (ultimately adopt). While principles in Agile are applied effectively in the development and Quality Assurance iterations, it is a different story altogether (and sometimes a bone of disputation) on the operations part. DevOps intended to fix this gap as DevOps includes “Continuous Development.” The code was written and dedicated to Version Control, would be built, employed, tested, and set up on the Production environment that is all set to be consumed by the end-customer.
Progressively leading enterprises worldwide, such as automotive and financial service providers, are always facing the necessity for platforms to converse and store heaps of data securely, and blockchain-powered services seem to be the best fit. Even the adoption rates support this report — global expense on blockchain solutions is likely to hit USD 11.7 billion by 2022. It denotes a five years CAGR (compound annual growth rate) of 73.2 percent. The decentralized data structure that can be extended, however, not changed makes scams tremendously hard.
But, reliability and safety come at a price. Blockchain technology challenges companies with the highest adoption costs, privacy and compliance issues, and legacy system integration inconsistencies, to name a few. The complicated transaction procedure includes encryption, validation, transmission, decryption, and a solo hitch that can cause the system to discontinue working. Thus, comprehensive tests become business-critical.
The industrial revolution introduced higher cybersecurity threats. With the growing technology sectors moving rapidly, the amount of info keeps rising, making security tests a top priority for industries that care about data flow and not including any holes, code errors, and leaks. In total, the prior introduction of a security test proves to be lucrative in almost every case. CTOs and CIOs from almost every company across all verticals continue to acknowledge the significance of security testing of their systems, network, applications, and software. The developer group can still work with the test teams and their partners to keep their products protective of risks, taking the security guard to a new height.
In the year 2020, though, security tests became the key emerging trend in QA and software testing. The report summarizes the three key goals that explain its addition as a separate topic: Increasing awareness of the significance of security among all verticals, increasing product and software security, and executing security checks earlier in the SDLC. According to the recent report, 52 percent of respondents report facing security support challenges among different technical problems in the present app’s development. Looking at this report, it is clear now that security will be the biggest concern in 2021.
The damage associated with cybercrime is estimated to hit USD 6 trillion yearly by 2021, as per the Cybersecurity Ventures. To give you a good view of the present state of overall security, we have collected five essential statistics about hacking, data breaches, industry-specific stats, and also costs and spending.
The Big Five
- Spending on cybersecurity globally is going to hit USD 133.7 billion in the year 2022. (Gartner)
- Data breaches depicted records of 4.1 billion in the 1st half of 2019. (RiskBased)
- 68% of company leaders feel their cybersecurity threats are rising
- 71% of breaches were motivated financially, and 25 percent were enthused by espionage. (Verizon)
- 28% of breaches involved malware, 52 percent featured hacking, and 32–33 percent included social engineering or phishing, correspondingly. (Verizon)
As per the new studyfrom cloud company Iomart, major breaches are mounting in intensity and frequency in the year 2020-21, with the number of breaches rising 273 percent in the 1st quarter, in contrast to the same time previous year. During times of disturbance or alteration, cybercriminals capitalize on doubt and confusion. As per industry experts, different sorts of attacks are at the peak.
Companies, both small and large, individuals, and governments are all targets. Among the common kinds of cyber attacks observing an uptick are ransomware, harsh attacks, and island hopping. Ransomware, wherein criminals encrypt files and then claim a ransom to bring back access, is up 90 percent, as per the current VMware report. Destructive hits or attacks wherein networks or data are destroyed are up 102 percent. Whereas Island hopping (where criminals take over the digital revolution efforts of organizations) utilizes their networks to attack partners and users, it is hit by 33 percent.
Testing for cybersecurity helps secure not just transactions (be it data or money) and their end-customers’ security. As cyber risks could take place in any shape, at any moment, security tests will continue to be a hot matter in the following year. Here are some key reasons why you shouldn’t ignore this:
- Security tests offer you an understanding of your company’s weak points before the attackers do.
- Penetration testing saves a lot of money — data breaches increasingly aggravate the already susceptible position companies find themselves in the middle of the 2020 pandemic.
- Security testing aids to spot areas susceptible to cyber attacks or threats
- Regular pen tests contribute to companies’ good reputations and help win greater trust between companies and their partners, third parties, and clients’ partners.
- Cybersecurity tests ensure that if downtime occurs, it isn’t as expensive and damaging as if you were not ready.
At present, the role of QA and tester isn’t just restricted to software testing. They are also involved in each facet of the Software Development Life Cycle (SDLC). QAOps is a great practice to bring operations, testers, and developer’s altogether. Test deeds, along with Continuous Integration/Continuous Delivery (CI/CD) pipelines and QA engineers working in parallel with the developing group, are the two crucial holders of QAOps.
The main objective of DevOps is to cut down the SDLC (systems development life cycle). At the same time, teams can concentrate on fixing bugs, building features, and pushing regular updates aligned with company objectives. DevOps bridges the relationship between business operationalists and developers. In a similar spirit, QAOps helps enhance the straight communication flow between developers and testing engineers by incorporating software tests into the Continuous Integration/Continuous Delivery (CI/CD) pipeline, rather than having the Quality Assurance team operate in seclusion. In a nutshell, QAOps is defined in two main principles:
- Quality Assurance actions should be integrated into the CI/CD pipeline.
- Quality Assurance testers should work aligning with software developers and be engaged right through the Continuous Integration/Continuous Delivery process.
QAOps can be applied not just in big tech enterprises yet also in small and medium-sized teams. This QAOps practice can be flexibly scaled high or down to fit any organization size. As progressively more organizations are inclined towards DevOps, this puts QAOps on the hike in 2021.
What Is QAOps? And Why It Matters For Your Web Application? Check it here
Test Automation is an attempt to save precious time and effort by fast-tracking the software testing cycle; however, the single way to unleash maximum profits is through parallel test execution. The capacity to run a test script over numerous test environments simultaneously or test multiple test scripts concurrently. Just like this year, in 2021, organizations will look forward to implementing automated tests through frameworks like Selenium Grid that supports parallel testing.
The DevOps testing groups will look forward to auto-scaling Continuous Integration/Continuous Delivery (CI/CD) runners to match the speed of agile as the tests unit will benefit from the capacity to test cases in parallel. Such autoscaling runners will handle the ups and downs over several servers on their own to handle queues professionally in case of parallel testing. This way, teams can opt for optimum resource utilization without having their developers linger for builds.
GitLab offers an autoscaling runner (study about it in their official documentation). LambdaTest provides integrations with GitLab for continuous integration as well as project management.
- Integrate LambdaTest with GitLab Continuous Integration
- Integrate LambdaTest with GitLab
With time, RPA related tools keep becoming the mainstream, covering broad facets in the sector of QA and software testing. For this reason, Robotic Process Automation brings a noteworthy reduction in the time-lapse in software testing and downsizes the funds appreciably. Therefore, companies can think about accomplishing more comprehensive test ecosystems to allow companies to make and retain high stability.
Companies can adopt this orientation immediately and without inviting essential out-of-pocket investments. Accordingly, it gets predicted that companies will keep accepting the Robotic Process Automation procedure to greater extents. These days, improvements in the Artificial Intelligence and software testing ecosystem have covered the path for RPA. Emerging and newest technologies like Artificial Intelligence (AI), the Internet of Things, Cognitive Computing, and ML transform the sectors. RPA is the cutting edge technology that can reinvent the business process management part.
Companies are even projected to use a blend of Artificial Intelligence and Robotic Process Automation for automated testing and scrutiny of automation reports. Hyper automation is the automated test trend in the year 2021 that will be in great demand. Hyper-automation is considered a tool formed by the blend of AI, RPA, ML, and intelligent business management software to reduce humans’ involvement in digital and physical tasks.
But, RPA does not require anyone to write code. In its place, the RPA system generates a list of activities by watching a customer perform tasks on an application’s graphical interface. It can then repeat these steps automatically, freeing persons of otherwise time-consuming and repetitive assignments.
Thanks to such current breakthroughs, public RPA enterprises, like Blue Prism, have thriving stock values way beyond their yearly revenue estimates. Gartner foretells the industry will grow speedily to $8.75B in 2024 from $443M in 2017. That is a remarkable growth rate in just seven years. The future of the digital workforce will entirely rely upon us, so we have to be ready.
This is one of those latest trends in automated testing that does not need much description. The usage of mobile gadgets and apps is growing rapidly. Millions of varied mobile applications are listed on diverse play stores of distinct OSs (operating systems).
Mobile App Market Statistics – By the Year 2026
- The mobile app’s global market size was valued at USD 106.27 billion in 2018 and estimated to hit USD 407.31 billion by the year 2026, rising at a Compound Annual Growth Rate of 18.4% from 2019 to 2026.
- As smartphones are becoming extra powerful and capable, global mobile application development is also reflecting extraordinary growth, anticipated to produce a market value of USD 14 billion by the year 2023 growing at a Compound Annual Growth Rate of 22 percent during the 2018-2023 period.
This makes mobile app tests truly significant as applications have to be tested across various operating systems and devices. The trend for test automation for mobile applications is on a hike, mainly driven by short time-to-market and advanced methods for mobile automation testing. Although well-known mobile app automated test tools such as Appium are utilized as a great tool in the DevOps procedure, the current utilization of such tools for app tests is not encouraging.
There has been the introduction of more and more complicated apps capable of handling distinct user needs. This requires comprehensive tests. So, the trend of mobile app test automation continues to be the same in 2021.
Check how LambdaTest can help you with mobile automation testing with its Appium Mobile Web Automation Grid.
So, there you have it – the 12 most important software testing trends you need to know heading in 2021. Now we know a lot of the stuff we mentioned above isn’t much talked about, but it is the future, and you have to adapt for them. As we all know, change is the law of life, and it is going nowhere, so it is crucial to stay ahead of it.
If you don’t want to get left behind, it’s essential to embrace these new trends. So which of these software marketing trends will you try first?