- - 7 min read
Benefits and Need of Artificial Intelligence in Software Testing
- - posted on February 26, 2019
Quick Introduction to Software Testing
Software testing is an important process that ensures customer satisfaction in the application. It is the planned way in test automation where an application observed under specific conditions where the testers understand the threshold and the risks involved in the software implementation. Testing helps to safeguard and an application against potential application failovers which may turn out being harmful to the application and the organization later on. Software debugging completed, once an application thoroughly tested. Testing, however, is slowly transitioning to more automation to ensure maximum accuracy in the journey towards Digital Transformation.
How AI is changing the Dynamics of Software Testing
In an attempt to make the application safeguard, we are turning increasingly towards Artificial Intelligence (AI). As testing progressively moving towards greater automation, we may be turning over most of it to Artificial Intelligence (AI). This implies that instead of manual testing done by humans, we are slowly moving towards a scenario where machines will take over the execution of test codes. With minimal human input, however, will be required to help machines ‘learn’ and enhance themselves.
It has, therefore, become essential to create an association directly pursuing the Grand Dream of Testing, where all the things are genuinely automated without human intervention and systems convey better testing over current application test teams. Make this thought a step further and imagine a world where software can test, diagnose, and heal itself.
Need for Artificial Intelligence in Software Testing
Software testing is a procedure that constitutes a very fundamental aspect in the area of development. However, many time developers can't do exhaustive testing(test approach in which all possible data combinations are used for testing) of an application because of the lack of resources and time. In that case, we need a requirement for a system that could intelligently recognize regions that will be elaborated and more focused on the aspects that could be taken through automation based on repetitive patterns.
Software testing takes up the most amount of time, human resource, and capital. And with the developers seeking faster deployments with inadequate infrastructure, Artificial Intelligence is a suitable path forward. Since 80% of testing is only a repetition of the checks that the software already possess, Artificial Intelligence will be helpful to automate the processes in an efficient way instead of a human tester which unnecessarily inflates costs and effort. It would be a good practice if human intelligence as well as automation through AI to recognize the application issues by making exceptional and innovative test environments. Consequently, it is ideal to leave the repetitive work to the Artificial Intelligence-powered automation which leaves just 20% of the testing operations to human creativity and reasoning ability. This would ensure more reliable results because hand-crafted testing not only requires extensive human hours but is also prone to inaccuracies and inconsistencies.
Artificial Intelligence algorithms can be tremendously helpful in the testing industry in making a smarter and more productive software for the end-user. It is, however, essential to interpret how to use Artificial Intelligence brilliantly. Algorithms that operate like a genuine user accessing automation. From that point onward, one must identify the areas within the process that can be optimized with Artificial Intelligence and apply the Machine learning and deep learning algorithm. Having a smart algorithm can encourage the process, help testers to find the maximum number of bugs in less time and it will make the application more reliable and accurate. The outcomes after that can be used by the developers to refine the product and learn from trial and error.
Beyond Continuous Testing with AI
Suddenly, a massive number of people jumped straight into a digital world, creating a tremendous demand for more, and increasingly innovative software. A little while ago, most companies were releasing software annually, bi-annually, or quarterly. Now, iterations commonly last two weeks or less. So, we adopted Agile and DevOps to move beyond that acceleration plateau.
Today, many of the organizations are talking about Continuous Testing and trying to implement it. The approach to Continuous Testing is to ensure the best user experience is delivered, free of defects and involves a process to test early and test often. We have turned to Continuous Testing to bridge the gap that we are facing today. But how do we check when, over time, these trends continue, and the gap widens? We will necessarily need to go beyond Continuous Testing.
Nevertheless, when we look into the future, it is clear that even Continuous Testing will not be sufficient. We will need additional support to be able to achieve a positive user experience with high-performance speeds and technical complexity. That’s where AI and Machine Learning enter the competition. They can, and will, take over the complicated phases of software development and testing. Artificial Intelligence is perfectly able to advance software testing by automating tasks that involve self-learning, and which traditionally required human cognition.
As Agile has made 2-week development iterations—but now, even more, is needed to meet today’s requirement for software. Efforts to further quicken the process revealed a gap between Development, Test, and Operations. That gap needed to be bridged with DevOps and Continuous Testing to move beyond that expedition plateau. Today, the huge majority of companies are talking about Continuous Testing and also trying to implement it.
We need “Digital Testing” to obtain further expedition and meet the quality requirements of a future driven by IoT, robotics, and quantum computing. AI follow intelligent human behavior for machine learning, and predictive analytics can help us get there.
Benefits of Integrating Artificial Intelligence in testing
To err is human. Even the most experienced tester is bound to commit mistakes while doing repetitive manual software testing. This is where software automated testing helps by performing the same or repetitive steps correctly every time they are performed and never miss out on recording accurate results. The testers freed from repetitive manual tests have more time to create new automated software tests and deal with sophisticated features.
Going beyond the limitations of manual testing
It is almost impossible for the most significant software/QA departments to execute a controlled web application test with 1000+ users. With the automation testing, one can simulate tens, hundreds or thousands of virtual system of users that can combine with a network, software or web-based applications.
Helps both developers and testers
Shared automated tests can be used by the developers to catch problems quickly before sending to the QA team. Tests can be run automatically whenever the source code changes, checked in and notified the team or the developer if they fail. Features like these increase the confidence of developers and also save their time.
Increase in overall test coverage
With automated software testing, one can increase the overall depth and scope of tests resulting in an overall improvement of software quality. Automated software testing can look into the memory and file contents, internal program states and data tables to determine whether the software is performing on as it is required to. All in all, software test automation can be executed 1000+ different test cases in each test run and provide test coverage that is impossible with manual software tests.
Saved Time + Money = Faster time to Market
With software tests being repeated each time source code is changed, manually happening those tests can be not only time-consuming but also expensive. Interestingly, once created – automated tests can be executed over and over, with zero additional cost at a much quicker pace. Software testing timespan can be decreased from days to mere hours which translates directly into cost savings.
Manual Testing v/s AI Testing
Let us examine the advantages of Artificial intelligence testing in answer to the shortcomings of manual software testing. Manual software testing often relies on the tester role-playing as the end user to ensure correct application behavior. On the other hand, an Artificial intelligence way to testing is based on the solutions for the specific problems that are inherent to manual testing and thrives on change.
|Manual Testing||Artificial Intelligence Testing|
|Manual Testing is time-consuming and expensive.||Artificial Intelligence saved time + Money = Faster delivery to Market.|
|Manual testing requires the presence of the software tester.||Testing using Artificial Intelligence can be done steadily without much human intervention.|
|In Manual Testing, there will be low productivity.||With the help of Artificial intelligence automated tools in software testing, there will be high productivity.|
|Manual testing is not always 100% accurate as it can be exposed to certain errors. Even the most experienced software testers are bound to make mistakes.||Here artificial intelligence tools help by performing the same test steps accurately each time they are executed and at the same time provides detailed results and feedbacks.|
|Some glitches in the software are usually not recognized by the software tester.||Artificial intelligence testing tools can help to increase the scope of tests that result in an overall improvement of software quality. Increases the Overall test coverage.|
|Manual testing is a linear process and happens sequential manner and its difficult to write hundreds of test cases and execute them manually.||AI bots can understand the client’s requirement properly and can produce the code for hundreds of test cases much more quickly than a human tester.|
Concluding AI in Software Testing
We are fast approaching in a time when even “Continuous Testing” will be unable to keep pace with the shrinking delivery cycle times, increasing technical complexity, and accelerating rates of change. We are already starting to use basic forms of AI, but we need to continue the testing evolution to achieve the efficiency required for testing of robotics, Internet of Things, and so forth.
We need to learn how to work smarter, not harder — ensuring the quality in an era where software will be processing an unimaginable no. of data points in real time, like on the IoT and while literally driving “self-driving” cars.
As more and more AI comes into our lives, the need for testing with Artificial intelligence is increasing. Taking the self-driving cars as an example: if the car’s intelligence does not work properly and it makes a wrong decision, or the response time is slow, it could easily result in a car crash and puts human life in danger.