Regression testing is complex when the interface is reformed. It is possible that many of the current test cases cannot be performed with the new interface. This can bound automation in regression testing and therefore considerably increase the cost of regression testing.
That’s where artificial intelligence in software quality assurance can be introduced.
The regression test cases that are affected by the change in the structure are automatically regenerated and the interface structure is modelled, where possible. It is extremely useful to have knowledge regarding where faults are likely to occur while organizing testing, this can be fed into decisions regarding how much testing effort is consumed on each module.
However, Artificial intelligence methods in software testing methods can be used to calculate which modules are likely to be specifically susceptible to faults.
Use of Metaheuristic Algorithms
While this blog covers the role of AI (Artificial Intelligence) within the different phases of software testing, I was a little shocked that there was no material on one of the most important applications of artificial intelligence in software automation testing, the use of metaheuristic algorithms in test case generation. Despite the lack of material on using metaheuristic algorithms, it has the power to change software testing considerably.
When manually making a test suite, the tester is more absorbed in how to test a function. By using a planning system, the tester can focus on what function to test and the path is taken care of. An extra benefit of this is that a planning system is not limited in any way when creating the path and may regularly find a path that was never expected by the tester. This problem is a very important one to battle.
Use of Genetic Algorithms
Another technique of creating GUI test cases fakes a beginner. An expert user of a system inclines to follow a more predictable and direct path through a GUI, whereas a beginner would follow a more random path. A beginner is then likely to search more probable states of the GUI than an expert.
The difficulty lies in creating test suites that fake novice system usage. Genetic algorithms have been proposed to solve this problem. Beginner paths through the system are not random paths. First, a Beginner will learn over time and usually won’t make the same mistakes continually and secondly, a beginner is following a plan and possibly has some system or domain knowledge.
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Genetic algorithms work as - a group of genes are formed randomly and then are subjected to some tasks. The genes that don’t complete the task best are the ones that are discarded and those which complete the task best are kept. The process is again repeated with the surviving genes being duplicated and the rest of the set packed in with more random genes. In the end, one gene (or a small set of genes if there is some threshold group) will be the only gene in the set and is naturally the best fit for the given problem.
In the case of GUI testing, the method works as above - Each gene is basically a list of random integer values of some fixed length. Each of these genes signifies a path through the GUI. For example, for a given tree of widgets, the first value in the gene (which will be called an allele) would select the widget to activate on, the following alleles would then fill in input to the widget liable on the number of possible inputs to the widget (for example a pull-down list box would have one input; the selected list value). The success of the genes is scored by a criterion that rewards the best ‘novice’ behavior.
Also Read: 5 Ways AI Will Change Software Testing
Challenges with Artificial Intelligence in Software Quality Assurance
There are many estimated kinks where AI-powered test automation needs to be worked out by the software testing company. Here is a list of challenges and problems which you can face while implementing AI in test automation,
• It is difficult to identify every algorithm and perfecting them.
• You need an extraordinary amount of data to train a bot to perform test automation.
• You can only anticipate how your bot will behave from the high-volume input data.
• Bots tend to repeat tasks even when the data feed is new.
• You will always need to train your bot and update its algorithms. It is a never-ending process indeed.
There are also one or two techniques that can be used in AI & ML Testing.
1) There is a Monte Carlo method. In this, we look together at a simple example and plan to use it straight in web traffic.
2) We have a close look at the key features of the idea of Artificial Neural Network (structure, human brain inspiration, learning vs testing set, activation function) and talk over some current applications. Then we propose possible new applications like Graphical User Interface visual testing and memory/bugs usage/cross-module distribution check or traffic density.
Benefits Of Using Artificial Intelligence In Software Testing
Since we have already talked about the technicalities related to AI in software testing, the challenges and the methods to introduce Artificial Intelligence into the test scenarios, let us quickly go through the benefits that using Artificial Intelligence that software testing companies could yield.
Since manual work is labor intensive and usually gets consuming at times, occurrence of errors is very natural. And it works pretty much the same way for all, no matter how much experience the tester holds. Use of Artificial Intelligence into automation testing helps eliminating mistakes and adds precision in the outcomes. Besides, AI could even help testers to create more sustainable solutions with more proportioned time space to meet the QA goals.
2. Advanced Support
The next big benefit for which artificial intelligence is gaining so much popularity in the world of testing is the support that testers tend to have with automation. From computing the data to monitoring test cases, AI could easily detect any code alterations that may negate the success rate.
3. Improved Scope
AI could help testers to improve the depth of testing adding to the quality factor. This is because AI has the potential to ascertain that the software under test should perform well on the defined lines of document content, data tables, and other benchmarks. Besides, AI into Automation testing could allow testers to run multiple tests at the same time widening the scope for software deliveries.
4. Saving Resources
Last but not least, using artificial intelligence into software testing can transform the entire test environment, resisting any repetition in the process. This means rapid alteration of code for detected errors, saving time, expenses, and of course no resource fatigue. Moreover, AI and ML technologies could be put together to reduce days of testing procedures into hours.
Software Testing Now VS The Future
As long as it is concerned with introducing technologies like AI and ML into the world of software testing, the now Vs then scenario seems to bring amalgamation of best technologies into one picture. Putting it all together, the future of software testing in comparison to the practices that are used presently, here are few things that will transform the dynamics of software testing:
AI & ML will lead to Hyper Automation in software testing.
Introduction of advanced agile approach I.e. incorporating DevOps and QAOps to fasten deliveries.
Use of Edge computing, IoT, and Bots in software testing to take the examination process to the cloud.
Leaning on Blockchain testing for added security on software related to banking, finance, cryptocurrency, and automotive.
Mesh testing in cybersecurity for improved perimeter security, higher scalability, and overall flexibility.
Summing It Up
When it comes to the future of software testing and progressing on the concept of digital transformation, AI seems to be the most significant contributing factor that will lead the industry. Especially, with all the benefits of AI in software testing, the software testing companies who are integrating the technology well into their process will eventually experience more opportunities.
Above all, the collaboration of AI technology with everything aforementioned coming for the future of software testing is likely to multiple the efficiency and revenue. Though complete automation is not the solution for all cases, bracing artificial intelligence into software testing would definitely call for an evolution in the world of testing.
All the best!
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