Top 5 Software Testing Trends to Review From 2019
Top 5 Software Testing Trends to Review From 2019
Check out how certain DevOps trends grew and evolved in 2019.
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Over the period of 2019, a massive wave of new approaches and innovations came to software testing landscape at an exponential way. Along with those new introductions is the continuation of technological improvement, evolution, and reinvention.
As we are heading into 2020, let’s review the top software testing trends and the achievements we have obtained in test automation after one year.
This blog will walk you through the five most influential software testing trends over 2019. Check them out!
1. Continuous Testing Became More Popular
The concept of continuous testing was coined back in early 2010. Over the past decade, continuous testing has gradually moved into the mainstream and now, it is one of the most used methods of software testing in 2019.
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Continuous testing enables a software product to be tested early and frequently, so that teams can receive feedback immediately throughout the entire process of continuous delivery. This method means a faster and more efficient way to eliminate bottlenecking between different departments. However, to ultimately leverage continuous testing, teams must obtain at least 85% automation — and this is expected to happen in 2019.
As the number of companies adopting Agile and DevOps practices continues to increase, continuous testing is even more prevalent. This method makes the concept of “quality at speed” no longer new in today's software delivery. Therefore, this software method was anticipated to exert an enormous impact on achieving both the “quality” and “speed” factors of this puzzle.
2. Artificial Intelligence and Machine Learning (AI/ML) in Quality Assurance
The State of AI/ML in Software Testing In 2019
Over 2019, we’ve witnessed more artificial intelligence and machine learning (AI/ML) applications in quality assurance (QA), such as quality prediction, test case prioritization, defects classification, computer vision, interaction with an application under test, and so on.
Organizations have put in their best efforts to develop technological advances so that they can fulfill fast-paced releases and frequent changes, mass operating environments, and others operating in a state of flux. Therefore, more test cases have to be created, more test scripts have to be composed, more test data have to be collected, and more test reports have to be evaluated.
With such an enormous amount of workload and information to handle, organizations must find out the best way to optimize the execution process, process all the data, and provide feedback in a fast and accurate fashion.
AI/ML is considered one of the promising solutions. Plenty of breakthroughs have been introduced to help users generate better test cases. Predictive modeling is leveraged to help decide where, what and when to test. Additionally, smart analytics and visualization will help teams understand the big picture of their test scenarios and make decisions faster, better.
Challenges and Opportunities of AI/ML in Software Testing
However, the growth of these technologies still takes place at a slow and insignificant rate. Budget allocations for AI projects have declined, compared to 2018, according to Capgemini World Quality Report 2019-2020). Similarly, feedback on AI project commitment dropped in several scenarios. It results in assumptions that organizations are still not confident enough to invest in AI or they may feel that the maintenance cost is higher than their desired level.
In contrast, adoption levels of ML-driven projects seem more positive in 2019. They are applied to predict defects and prioritize which test cases to employ. Huge collections of data need to be gathered, and the ML mechanisms need to prove their effectiveness — but the anticipation for this technology is, no doubt, growing.
We are living in the renaissance era of AI/ML. These two notions are widely applied to most aspects of our lives, and software testing is not an exception. Besides, an increasing wealth of available data and technological advancements have opened up more opportunities for AI/ML in testing.
3. Intelligent Automation
The next trend in the list today is about employing smart automation frameworks, tools, and techniques.
In early 2019, it was anticipated that the number of organizations applying automation to their software testing projects would significantly increase. This mainly results from the shift toward software testing approaches like Agile and DevOps. Quality at speed stimulates organizations to automate their mundane activities, so that they can focus on two more important tasks— strategic planning and evaluating decisions.
Automation, if applied properly, can enable software development teams to increase test coverage, enhance test efficiency, receive immediate feedback, reuse test cases, detect bugs early, and more. Accordingly, organizations can satisfy both requirements of high-quality product and fast-paced delivery.
According to a study of test automation trends, about 44% of organizations expect to automate 50% or more of all testing in 2019. When they can reach this level of automation, they can reap numerous benefits and competitive advantages.
Promising Adoption Rates of Test Automation
We can see that the adoption rate of automation progressed in 2018, and it kept escalating over 2019.
Research indicates that test automation brought plenty of benefits to software organizations in 2019. More and more teams have applied automation to their SDLC. The results are better control of test activities, more transparency, and more accurate detection of defects. They also reported that automation helped them minimize unpleasant outcomes, such as test costs, test cycle time, and overall security risk.
Challenges of Test Automation
However, automation also comes with a number of challenges for software organizations. Almost two-thirds of the respondents in a recent study found it difficult to adopt automation as their software applications change too much with each release. Besides, testing skills and appropriate resources also remain major obstacles for them to handle when applying automation.
What’s Next for Software Test Automation?
Moving forward, the concept of automation testing has been popularized for about 20 years now. However, there are many dilemmas still left in the picture.
A common reason why teams fail to obtain their desired outcomes of automation is that most automation frameworks were created to automate only manual tasks. Instead, we need an automation framework that fulfills several following factors:
- Significantly reduce the effort put into the programming, especially for teams with less programming expertise
- Intelligently decide when to implement a particular task such as execution, without human interference.
- Dynamic enough, such as utilizing cognitive computing techniques to determine test objects and screen elements effectively.
- Prioritize, identify, and execute the critical test cases from the automated suite.
- Provide its own test data.
4. Test Data and the Internet of Things (IoT) Testing
The continuous expansion of the IoT has immersed throughout the past years. As per one research of Gartner, the number of connected things has been increased to 14,2 billion in 2019, expected to reach 20,4 billion in 2020, and keep going up to 25 billion by the year 2021. More IoT devices means more online connection and data exposure, and also more risks.
As IoT technology is continuously evolving, testing is compulsory to validate the performance of an IoT system. The major types of IoT testing include:
- Usability testing: test the usability of IoT systems
- Performance testing: test the performance of connected devices in an IoT network
- Compatibility testing: checks the compatibility of devices in IoT systems
- Security testing: validates user authentication processes and data privacy controls
- Data integrity testing: verifies data integrity
- Reliability and Scalability testing: sensors simulation using virtualization tools
The progress of IoT systems is closely connected to the growth of applications of AI/ML to generate more test data and data projects. The automation industry also expects to witness an escalation in the usage of cloud-based and containerized test environments, and solutions for the lack of test data. QA teams of organizations should step up their game to ensure the security in IoT systems.
QA teams are suggested to take on three following steps: applying continuous security testing, carefully planning what needs and does not need to be tested to be operationally efficient, and performing service virtualization as part of their automation strategies.
5. Behavior-Driven Development (BDD)
The last item of our list today is Behavior-driven development (BDD), which has achieved the next-level maturity over the year. The 12th Annual State of Agile report indicates that only 16% of organizations employ BDD in 2018, but we can expect this number to have a significant increase in 2019. Alongside that promising prospect, more and more software teams expect to flow through BDD maturity model. This model involves five levels of maturity:
- Embrace BDD collaboration
- Implement BDD tools and frameworks
- Connect systems for development and automation
- Standardize continuous integration and systemic collaboration
- Report on BDD success
These five trends are among the latest trends in the software testing big picture. We do hope that this recap can serve you better insights on what took place in software testing trends over one year. As 2019 is almost over, let’s reflect on reinforcing your testing strategies. And stay tuned to watch out which trends will be dominant in software testing in the next year 2020.
Published at DZone with permission of Oliver Howard . See the original article here.
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