Top 6 Software Testing Trends to Look Out For in 2020
Some of the growing software testing trends that will continue to dominate in 2020, like AI and IoT in testing and increased automation testing in teams.
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We’ve reviewed the prominent trends in the software testing landscape throughout 2019. As the test automation industry is ever-evolving and the new year is all but here already, our team has cast a few predictions on what is going to turn out in 2020.
This blog will walk you through the top software testing trends in 2020. You can leverage these latest trends to come up with better strategies for your testing plan. Check them out!
1. Artificial Intelligence and Machine Learning (AI/ML) in Testing
According to a variety of reports, intelligent automation will continue to be one of the top software testing trends in 2020.
Applications of AI/ML have been leveraged in software automation testing for several years. AI/ML help software teams optimize their test automation strategies, operations, and speed up the adaptation process.
Over 2019, QA (quality assurance) teams have applied AI/ML in predicting test quality, prioritizing test cases, detecting test objects, classifying defects, interacting with AUT (applications under tests), and more.
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Artificial Intelligence (AI) is expected to be omnipresent in all aspects of innovative technology. Investment in this area is forecast to reach approximately $200 billion by 2025. The applications of AI appear in most testing areas relevant to reports and analytics:
- Log analytics: Determine unique test cases that need manual and automated testing
- Test suite optimization: Detect and eliminate redundant test cases
- Ensure test requirements coverage: Extracting keywords from RTM (Requirements Traceability Matrix)
- Predictive analytics: Predict key parameters and specifics of end-users' behaviors and identify application areas to focus on
- Defect analytics: Identify defects or application areas that tie to business risks.
Machine learning (ML) is forecast to reach a new level of maturity in 2020. According to the Capgemini World Quality report, almost 38% of organizations are planning to implement ML projects in 2019. As such, industry experts expect this number will continue to increase in the next year.
What Does This Mean for Organizations?
Despite the promising prospects of AI/ML application in software testing, experts still regard AI/ML in testing is still in its infancy stage. Therefore, it remains numerous challenges for the applications of AI/ML in testing to move on to the maturity level.
The rising demands for AI in testing and QA teams signal that it’s time for Agile teams to acquire AI-related skill sets, including onboarding data science, statistics, mathematics. These skill sets will be the ultimate complementation to the core domain skills in test automation and software development engineering testing (SDET).
Additionally, successful testers need to adopt a combination of pure AI skills and non-traditional skills. Indeed, last year, a variety of new roles have been introduced such as AI QA analyst or test data scientist.
As for automation tool developers, they should focus on building tools that are practical. Companies are utilizing PoCs and reassessing options to make the best use of AI and considering budgets. A good AI-assisted tool has to meet both the requirements of business cost-efficiency and technical aspects such as reading production logs, generating test scenarios, or responding to production activities.
2. Test Automation in Agile teams
Test automation is going mainstream when 44% of IT organizations automate 50% or more of all testing in 2019. We expect that the adoption rate of automated testing will climb to the new level next year.
As more and more organizations adopt Agile and DevOps practices to fulfill the norm "Quality at Speed" of software development, test automation has become a vital component. Test automation supports teams in performing repetitive tasks, detecting bugs faster and more precisely, providing continuous feedback loops as well as ensuring test coverage. As a result, organizations can save a vast amount of costs, time, and human resources if they integrate automated testing in their QA processes.
What Does This Mean For QA Practitioners?
Test automation, however, will not be a complete substitution for manual testing. In fact, robust QA teams must combine manual and automated testing to obtain the most in ensuring software quality. The role of automation in testing is indisputable — but there are many types such as exploratory or usability testing that still need to be executed manually.
QA practitioners are responsible for developing a smart, familiar, and end-to-end environment. Additionally, they should keep in mind that test automation is a full-cycle requirement carried out from build through deployment.
However, test automation process is easier said than done. That's why many businesses have been unable to squeeze the most out of automated testing and received the desired ROI (Return on Investment). The Capgemini World Quality Report indicates that QA teams should look at automation as a broad, smart, and connected platform rather than a capability.
What Does This Mean for Providers of A Test Automation Solution?
Developers of test automation tools should continuously update and upgrade tools to satisfy QA teams' demands. The future of test automation solutions underlies some essential criteria, including:
- Easy to adopt and use for end-users with or without testing experience.
- Support smart frameworks, meaning letting issues resolve themselves
- Maximize test coverage and ensure the quality of bugs detection
- Complete cross-platform testing for web, API, mobile, and desktop automation.
- Integrate with CI/CD ecosystems and allow Continuous Testing
- Integrate with intelligent dashboards and analytics for better insights
3. Big Data
Big data plays an essential role in a variety of business sectors, from technology, healthcare, banking, retail, telecom to media, and so on. There has been more focus placed on employing data to segment and optimize decision-making processes.
With the support of big data, industries can deal with massive data volumes as well as diverse data types. It also helps make better decisions and enhance market strategizing with precise data validations. Therefore, big data is expected to grow at an exponential rate as many industries are shifting toward a data-oriented world.
The need for testing big data applications is expected to be on the new rise in 2020. This trend has been widely adopted, mainly because of the robust processes that many enterprises are following make the most of their marketing strategies.
4. QAOps: Quality Assurance Sees Changes in DevOps Transformation
If you have not heard of the term "QAOps," before, now’s the time.
You might have been familiar with "DevOps," the combination of development (Dev) and information technology operations (Ops). DevOps practices aim to shorten the systems development life cycle (SDLC), and teams can focus on developing features, fixing bugs, and pushes frequent updates that are in alignment with business objectives.
Similar to DevOps spirit, the goal of QAOps is to improve the direct communication flow between developers and testing engineers by integrating software testing into the CI/CD pipeline, rather than having the QA team operate in isolation.
QAOps is defined in two fundamental principles:
- QA activities are incorporated into the CI/CD pipeline
- QA engineers are involved throughout the CI/CD process and work in alignment with developers
One of the best examples of QAOps adoption is Facebook. In 2014, the Facebook team decided to shift to Facebook Graph API version 2.0 and enforce Login Review across all apps. To ensure a seamless migration process, the team tested out this new version on the 5,000 largest apps. Because this work was not possible for in-house testing, the Facebook team chose to apply QAOps through outsourcing. As a result, the team did reach the goal of testing across 5,000 apps in one month and address some critical problems that can not be solved internally.
QAOps practices can be applied not only in giant tech companies like Facebook but also in medium and even small teams. This practice can be up to or scaled down to fit any business size. Together with the growth of DevOps, QAOps is expected to become a software testing trend in 2020.
5. IoT Testing
According to a report by Gartner, the number of IoT devices all around the world will reach 20.5 billion by 2020. These IoT devices must undergo IoT testing for security assurance, trustworthiness, ease of use, compatibility of device versions and protocols, versability of programming items, monitoring connection delay, scalability, data integrity evaluation, device authenticity, and more.
IoT testing engineers have to deal with an overwhelming amount of work in this area, especially in monitoring communication protocols and operating systems. Accordingly, QA teams should broaden their knowledge and upgrade their skills in usability, security, and performance IoT testing.
Another challenge for IoT testers in the upcoming years lies in strategies. A recent survey of the World Quality Report shows that 34% of respondents said their products have IoT functionality, but their team still can not find out the most proper testing strategy.
6. Demands for Cybersecurity and Risk Compliance
The digital revolution comes with many security threats. As such, CIOs and CTOs from almost every enterprise across all sectors continue to recognize the importance of security testing for their software, systems, applications, network. Software teams have to even work with their partners to make their products more resilient to threats, taking the cybersecurity shield to the new level.
Security testing helps secure not only transactions of money or data, but also protection of their end-users. As cyber threats can take place in any form and at any moment, testing for security will be a popular topic the following year.
We hope that this list will provide you with helpful insights on software testing trends in 2020. As the digital transformation is constantly evolving, testing engineers, as well as software product enterprises, must keep themselves updated with the latest changes and innovations. Quality assurance teams, leaders and practitioners should take these trends into account to build the most ultimate strategies, climbing up to a new level in the software testing industry.
Published at DZone with permission of Oliver Howard. See the original article here.
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