Selenium is a popular web-based software testing tool that has set the benchmark in the industry for a long time now. Alongside in the recent years the advent of AI and Machine Learning has simplified software automation and brought about major changes in the industry.
There are speculations that the conception of the new technology will affect the future of Selenium test automation. Even when both the solutions have unique features that strongly support their respective existences, the phenomenal leaps made by AI and ML cannot be ignored by the industry.
What Makes Selenium So Special & Popular?
Selenium is an open-source tool that has built a greater following compared to other licensed tools. This portable framework can be readily used for the automation of applications. Some of the features that make it so successful include:
- High Flexibility: It offers excellent flexibility in regression/functional testing.
- Multi-Language Support: It allows tests to be written in different programming languages including Python, C++, Java, and Ruby among others.
- Cross-Browser Testing: It readily supports cross-browser testing. This allows test case implementation across all the different browsers, multiple times.
Impact of Selenium on the Future of Software Testing
Software testing is always evolving, yet it continues to face new challenges. Test automation has proven to be a valuable solution for testers who face multiple challenges with manual testing. Open-source innovation, DevOps, and agile development have played a big contributory role in the continual evolution of the testing environment. In this space, Selenium has built a reputation for being the perfect automation tool for continual development and delivery. It offers many benefits including compatibility, cost-effectiveness, speed, and more.
There are many reasons why Selenium is still considered to be a promising automation testing tool for the future.
- Open-Source & Free: It is based on an open-source framework and can be downloaded by anyone. Besides, there are no recurring fees involved. The free form of this platform makes it a long-term consideration for testers and stakeholders because this cuts down the testing costs. Everyone can access and benefit from it, including small to large enterprises and independent quality analysts.
- Multi-Operating System & Browser Support: Selenium supports all the commonly used operating systems and web browsers. This allows analysts to assess their apps’ performances on all the popular platforms. This includes Linux, Windows, Mac OS, Solaris, HtmlUnit, Android, and iOS operating systems and Firefox, Chrome, IE, Opera, and Safari browsers.
- Flexibility with Programming Languages: Testers can select the language in which they want to write the scripts. Selenium supports multiple languages including Java, Perl, Python, Ruby, C#, and PHP among others. It is possible to select the client API based on the programming language.
- Selenium IDE: Selenium IDE has powerful tools that allow you to record, debug, and edit the tests.
There are many more reasons that give Selenium its edge to make an impact on the future of testing.
What is AI Trying to Achieve in This Space?
AI and machine learning (ML) are in the early stages of providing test automation solutions. The technology’s role is still limited to within the software development lifecycle. There is nothing as 100% automation and similarly there is nothing as 100% AI-based test automation.
But there are different ways in which test automation platforms are using AI-based technologies. Most are currently focused on achieving a single goal – providing valuable solutions to testers in the area of routine, error-prone, and repetitive tasks involved in software testing. Test maintenance is one area AI-based test automation tools are making a huge impact.
Helping with Test Maintenance Reduction
Test maintenance is one of the biggest cost factor areas in running automated tests. According to various surveys involving testers, it has been claimed that test maintenance acts as the greatest bottleneck in the testing process. Now, there are many test automation vendors that use AI to address many of these issues. Machine learning technologies can now function as self-healing systems. They can automatically overcome application changes without the need for human intervention.
When using ML for test maintenance, there is no longer any need for testers to deal manually with code, detect the changes, and update the test flow. AI-based technologies help make tests more resilient. This helps in reducing both the resources and time required for test maintenance. The result is a positive influence that creates more time for testers to address other key bottlenecks.
So, AI-based test automation tools are not about taking away jobs from humans. They are more about making the jobs of human testers easier, faster, and more productive.
What Can AI Do in Testing?
The industry found the value of AI in its promise of handling repetitive and monotonous functions. This helped free up valuable time that could be used for many other valuable activities in the future. AI-based testing addresses writing, execution, and code testing. The process is not just limited to automating routine functions. The processes are continually evolving as AI systems keep learning from human input.
Applications of AI in Software Testing
AI can be used in all software testing scenarios, ranging from functional to non-functional ones. Test errors can be identified and resolved, usability testing can be conducted, and much more can be done in a better way using the more advanced abilities of these technologies. The key applications of AI in testing are as follows:
i. Automation of Test Case Writing
AI allows testers to eliminate inconsistent test cases and maintenance. It handles the following processes:
- Detection of code not covered within current test suites
- Covers the source code’s control path
- Finds the parameters that should be addressed in the method under test
The result is better and improved unit tests for testers.
ii. API Test Generation Automation
One of the challenges that API creates is the requirement for repetitive, sometimes endless, user actions. AI-based solutions can resolve this challenge in the following ways:
- Identifying patterns within the traffic
- Creating a comprehensive data model of observed parameters
- Generating a needle test that works more efficiently
iii. Visual Validation
Selenium has become a valuable tool for addressing this aspect of software testing. It has become so important where it is claimed that visual validation cannot be done without Selenium. Despite this point, Selenium has its unique limitations and challenges. AI and ML algorithms facilitate self-healing during runtime to take care of the common maintainability issues.
Selenium has also been the go-to tool for user interface testing. However, there is still a long way to go before becoming perfect. There have always been challenges with regard to stability and maintainability. AI can enable the self-healing of Selenium test executions. Such tests are sophisticated in that they can find elements from all types of environments. At the same time, they are capable of ‘learning’ from human interventions.
Interestingly, most new AI solutions have been developed around the problem of test scripts maintenance. This seems to be a trend and is expected to continue into the future.
Creating and maintaining test automation and finding test automation engineers is the most common challenge in testing. It happens commonly that test scripts fail because of an update to the application in the test’s element IDs that weren’t updated within the automated test scripts.
Almost all the new AI-based tools help address this issue. So whenever a new UI change is made, there is no need to update the automated test. AI-based tools can self-correct during runtime.
ii. Non-Selenium Tools to Become Popular
Over the last several years, Selenium has been the go-to browser-based automation tool for the testing community. It has become so popular that it is embraced and supported by even commercial tool vendors.
However, more and more testing organizations now realize that automation doesn’t just stop at Selenium. It is most important to use the right tool for the right task within the development process.
This doesn’t mean that Selenium will be fully replaced by AI-based automation testing. Testing organizations and teams have different styles, needs, and preferences. They now have more options at their behest and the trend is expected to grow.
It also needs to be addressed that Selenium is an open-source and free platform. In the AI-based automation testing space, things seem to be going back in time, especially from the commercial perspective. Most big vendors in this space offer only paid solutions. So this does create some space for Selenium in the future.
So any organization that intends to benefit from AI automation will have to budget for it. That is unless a free and open-source AI-based popular tool appears across the horizon, as Selenium did years ago.
iii. Self-Healing Automation Tools
Every time a test is run, a testing tool is going to collect a large volume of data. The data covers much more than test output logs. It can cover:
- HTML data
- Page screenshots at different test execution stages
- Errors from browsers
Such data feeds into the AI-based automation tool’s machine-learning algorithms. Whenever tests are run, the tool will use whatever it learned from the data to study normal application behavior. Gradually, this can help the tool to identify any deviations from these known behaviors as potential problems. The data can also be used for making corrective decisions during runtime if potential problems are identified.
For example, AI-based automation tools can learn to ‘know’ when an expected field element ID isn’t available. They can use another element to interact with the field and ensure that the test continues without breaking. Some AI-based tools are already able to ‘know’ the difference between a minor change to a feature, element, or a broken front end. In addition to tools capable of self-healing during runtime, you should expect to see new solutions that can analyze and report on the test results by making the most of machine learning.
iv. AI Can Run Time-Consuming Tests in Seconds
Handling failing tests can be a time-consuming process in large enterprises. This robs organizations of valuable time and resources that could be invested in innovation, as their teams spend more time checking log files to identify the type and cause of failures. Machine learning has proven to be the perfect solution in such scenarios.
New AI-based tools are helping eliminate the ‘grind’ out of the process of testing. They are able to find solutions at scale. For example, testers cannot check through millions of server logs, but machines can. Machines are now capable of identifying potential issues, as already explained above.
Thus, machines are capable of doing all the hard work. They can provide insights that can be useful and valuable. Eventually, it comes down to a human decision in saying whether the machine’s findings were right or not.
v. Growing Popularity of Record & Playback
Record and playback was a practice that many testers frowned upon. It seems that the trend is making a comeback. This also means the return of tools like Selenium IDE. Most AI-based tools now have a record/playback function integrated into them. These tools use machine learning to enhance runtime reliability. It is expected that Selenium IDE may also see machine-learning-based integration in the future.
vi. Continual Testing
Continual testing is expected to become another big trend in software testing in the future. It has already gained widespread attention among online testing forums and communities. So what is continuous testing?
It is the ability to conduct a quick evaluation of the risks of new releases or changes before they even affect customers. This allows unexpected behaviors to be identified and instantly prevented as they occur. This further helps in reducing the cost and simplifying the process of fixing bugs before they reach the user.
Continuous testing doesn’t begin after completion of everything else. It begins at the very start. Automated tests are executed at each stage of the SDLC, probing the quality of the software. It is all about testing early, testing often, and testing everywhere, using automation.
One area Selenium tests frequently fail is when developers make any changes to the software. These changes could be something as simple as renaming a field ID. Selenium-based solutions are mostly using a single selector or single path that relies on just one method of finding fields in the software. AI-based automated tools can use advanced machine learning to automatically adapt to such changes. The result is that the tests become easier to maintain and more reliable.
Thus, machine learning has made significant improvements in this industry. However, there is still a long way to go before becoming perfect. To deliver the optimal level of accuracy, this technology still requires human intelligence intervention. Even when it has limitations, its future looks brighter compared to Selenium – unless the latter also integrates AI and evolves with time. It may very well be the case where both co-exist and leverage the best. Time will answer how this will evolve in the coming years, but the landscape surely looks promising and exciting.