The speed at which software is developed is increasing, which means automated testing methods need to keep evolving and adapting to new technologies. The move away from waterfall and into agile methodologies and the expansion of DevOps within organizations has necessitated the need for faster development cycles, which means tests need to be done quicker and with the same or better levels of accuracy.
Artificial intelligence (AI) is ingrained in our daily lives though most people are unaware of its impact. The benefits of AI in automated software testing are seen in its predictive nature and machine learning capabilities. AI, as used in automated testing, learns from real-world user data and then turns that data into test scripts that can be deployed quickly with resultant reporting being very accurate. The algorithms that run AI can help in determining not only what needs to be tested, but also can prioritize those tests based on objectives and deadlines. Additionally, AI is expected to make more headway in predicting user behavior and advising on areas of focus, eliminating unnecessary testing and identifying which types of automated tests are best for each situation.
The current investment in AI is expected to be at about $6-$7 billion in North America with levels globally expected to hit $200 billion by 2025.1 Several technologies are helping in this effort and are driving future automation. Gartner predicts that “by 2024, organizations will lower operational costs by 30% by combining hyperautomation technologies with redesigned operational process.”2
Intelligent Automation (RPA)
RPA was developed about two decades ago as a solution for automating repetitive and tedious tasks for the banking sector. As the technology was refined, a place for it was found in the software industry where many of these data-driven types of tasks lie. The Automation Market size of the Internal Robotic Process is projected to hit $7.2 billion by 2025. Companies can adopt RPA quickly and without large out-of-pocket investments.3
At a basic level, RPA uses bots to carry out a series of commands that are learned at the end-user level by watching the user perform the tasks in the graphical user interface (GUI). The GUI can be programmed to capture business processes such as data entry which can then be used to set up a testing environment that is codeless. Additionally, RPA can be applied to a variety of products and processes, and it needs only a single production environment. As well, test frameworks and test scripts can be reused for intelligent automation, as maintenance for key processes is done with core units instead of separate departments. Even though the business need for RPA and test automation is fundamentally different, the technology behind them works very similarly. This gives enterprises the possibility of having the same engineers perform both disciplines.
Codeless Automated Tests
Upper management, operations, and marketing teams have always had a stake in software development, yet they could only get so close to the process because of the need to know how to code. However, as organizations move more toward DevOps, codeless, or no-code, tools are becoming more popular. DevOps teams can now work together with internal stakeholders in expanded capacities that include software testing. Codeless testing tools are built for team members who do not have coding experience, so the learning curve is relatively quick and you can set up cross-functional teams that move in and out of testing depending on schedules and interests. Involving teams outside of development in codeless testing helps in scoping, pricing, and scheduling projects and also acts as an all-hands approach that covers communication gaps that can occur between account and test automation teams.
Performance testing is a measure of how well software will perform in terms of speed, stability, and responsiveness under a heavy load or extreme conditions. Load, volume, and stress are just a few of the tests that measure the quality of the user experience under real-world scenarios. Since performance tests are conducted right before the software goes live, fixing issues at this point can cause launch delays which can be frustrating given the anticipation that sets in as the project nears the finish line.
As a natural fit with agile methodologies, CI/CD, and the consistent use of automated testing, performance engineering is emerging as a way for teams to use testing methods and processes to prepare the software for better performance testing outcomes. Through the adoption of performance engineering, teams are making a concerted effort to anticipate and plan for performance issues early in the software development life cycle so that bottlenecks can be avoided, and teams can deliver a stable release on or closer to scheduled launch dates.
Two very important components of testing not to be overlooked are that of minimizing risk and maximizing security. With a stronger emphasis on security being implemented into the development and delivery process, the role of developer has expanded to include security operations. InfoSec and developer teams are starting to work more closely in identifying the best security tools and processes in hopes of delivering and maintaining secure applications. This is a radical change, as in the past, these teams functioned mostly independently. Their cooperation now helps in effectively dealing with the current security challenges.
Together, these teams are also focusing on designing secure architecture— specifically, designing it for failure. Failures that were once limited to issues such as broken features now involve an ever-changing landscape of security vulnerabilities and breaches that could result in keeping the user from safely accessing certain functionalities.
Automation, not only in the form of identifying and testing, but also in fixing vulnerabilities is helping development and InfoSec teams deliver more secure applications quicker. With the ever-increasing role of compliance, compliance automation tools are also being routinely added to testing tool suites.
“Artificial intelligence (AI),” “robotic processes,” and “codeless” are a few of the words heard frequently around technology innovation. And, big strides have been made in these areas in relation to automated software testing. The move to agile along with quicker development cycles has driven a need for these technologies. What does automation and the future of work look like? Where once automated testing worked in a silo, now it is aided by predictive analysis, deep learning, and robots that have made the prospect of automated testing very attractive. These technologies have also had an effect on organizations as a whole as codeless testing allows more people to get involved in the process and performance engineering provides a way for teams to anticipate issues early in the process. Future automation can only make automated software testing faster, more accurate, and more user-friendly.
Automators is proactive in identifying and learning about new technologies that can add value in the way of quality and efficiency. Learn more about future automation in software testing.
1. KKL Research, Industry Research, 2021, p. 10
2. Ibid., 16
3. Ibid., 14