Background of Software Testing:

Software testing is necessary to ensure customer satisfaction in an application. Testing involves observing the application under certain conditions, testers already know the threshold and the risks involved in the implementation of the same. Testing helps to safeguard the application against potential failures which may prove to be harmful to the application and the organization in the future. The next step to software testing is software debugging, which can be carried out only after the software has been thoroughly tested.

What is AI?

Any complex task which a human being can solve without consciously taking decisions is a candidate for Artificial Intelligence. The general concept of AI is the ability of a machine to understand the environment and process the input data to perform an intelligent action and learn how to improve itself automatically.

Image source: www.thoughtworks.com

Why do we Need AI in Software Testing?

Nearly 80% of testing activity is a repetition of the checks the software already has, which
consumes a lot of human effort and time. It is very common in software testing, that as a project expands, the parameters also increase, resulting in extra workloads for the testing team, which may be already be constrained in their ability and the number of hours they can productively work. Manual testing also faces scalability issues, where several machines have to be managed. AI can solve these issues in the following ways :

• With AI-powered machines, 80% of the repetitive tasks can be done by AI Bots, leaving the rest 20% work to be done by humans who can make use of their creative and reasoning ability. Thus AI can be made to do repeatable tasks like the population of test data, regression testing, etc. while testers can concentrate on working on creative and difficult tasks similar to the integration of systems.

• Using an AI robot, the testers can reconstruct the tests to incorporate new parameters, and the coverage of the testing can increase without adding extra workload to the testing team.

• AI can create test cases automatically. This reduces the level of effort (LOE), with built-in standards.

• AI produces test code or pseud code automatically by understanding the user acceptance criteria. Test Automation saves time and cost.

• AI can also do codeless test automation, which would create and run tests automatically on your web or mobile application without writing any code.

• As AI bots can work 24/7, they can help in debugging projects as often as needed, thus tests can be run for a longer time, without the need of human intervention.

Image Source: www.agiletestingalliance.org

The AI algorithms behave and operate just like an actual user performs automation. So it is always necessary to identify those areas that can be optimized with AI-powered algorithms. A smart algorithm can help testers find the maximum number of bugs. The results thereafter can be used by the developers to refine the product further.

Example - Startups like DiffBlue is using AI to automate developer’s tasks considered too repetitive or time-consuming.

How can you train the AI bot?
Testers generally ask lot of questions. AI test bots must be trained to process input data by asking questions that lead to intelligent action, just like Android Auto Google Assistant. As we go on strengthening the underlying algorithms of the bots, to recognize input patterns and behaviors, their efficiency will improve.

Pros and cons of AI :

Pros: AI has the advantage of being able to carry out repetitive tasks 24 hours a day without getting tired and it has a lower error rate than a human when spotting bugs in code. AI bots can understand the client’s requirements properly and can produce the code for hundreds of test cases much more quickly than a human tester.

Cons: Humans are complex and unpredictable, and AI is not yet sophisticated enough to replicate a human user’s experience with all the complexities that come with it.

Studies show that 85% of customers are likely to stop working with a company after a poor mobile app development experience. So getting it right the first time is very important.

AI has a long way to go before it can accurately replicate and test for every scenario and environment in which an app or website may be used. The variations are – internet speeds, local weather, infrastructure, time of day and so many other factors.

Challenges with AI-powered Applications

The challenges and possible problems you may face when attempting to build AI-powered applications for testing are:

• Identifying, perfecting all the algorithms needed
• Collecting lots of input data to train the bots
• How the bots behave from input data
• Bots can repeat tasks even when the data inputs are new.
• The process of training your bot will never end, as we’re continuously improving algorithms.

In many ways, AI testing is a lengthy process but can be useful if done properly.
There is a low-cost solution to this problem, such as crowd testing, which can be predicted to remain crucial for businesses wanting to offer the best digital experience to customers until AI offers a viable alternative.

What is crowdtesting?

Crowdtesting is a method of putting digital assets (such as mobile apps, websites, IoT, and connected devices) into the hands of people who represent your customer group.

It creates a test environment that closely mirrors the way the app/website will work on each device and in specific locations.

Image source: www.strata9.com

Businesses get a holistic view of their digital experience through crowdtesting. Either you invite a community of experienced quality assurance (QA) professionals who can find any software bugs that an internal team may have missed, or you give it to people without a Quality assurance process background, in order to focus on the distinctiveness of the solution.

Contrary to popular beliefs, AI assists and does not replace peoples’ jobs. Human intervention is still needed in the entire software development lifecycle. The role of AI is at the budding stage in the software testing scenario.