Benefits of Using ML in Test Automation

Benefits of Using ML in Test Automation

Benefits of Using ML in Test Automation

Information about Benefits of Using ML in Test Automation

Kevin David Scam or Legit

Software testing is a critical phase of the Software Development Life Cycle (SDLC). The reason behind that is, it is essential to deliver robust and high-quality software. In today’s day and age, when you’re developing business-specific apps, there are plenty of test cases that you need to cover while performing software testing. Performing manual testing in this scenario would be cumbersome, and that’s where the introduction of test automation tools came into the picture.

These tools provide you with various reports that you can utilize to improve your software. Now, how about integrating intelligence into these automation testing tools. That’s where the role of ML (Machine Learning) comes into play. As per a survey by Finances Online, automation is one of the top use cases of ML. Furthermore, according to G2, ML can help you enhance business productivity by 54%. These statistics suggest that ML has a massive role in test automation and can help you deliver better software products.

Now, the question is, what kind of benefits ML brings to the table as far as test automation goes? Let’s try to dive deeper into things as we analyze the role of ML in test automation, its related algorithms, benefits, and challenges that one needs to overcome.

Ideas of implementing ML in test automation

These are some of the ideas through which you can implement ML in test automation:

1. Test script generation

Generating test scripts for software testing can be a tedious task for even experienced programmers. If you use automated testing tools, you can reduce your workload. However, if you can integrate ML, the generation of test scripts becomes more straightforward. With the help of the NLP (Natural Language Processing) technique, ML can generate the test scripts as the tester would do, and that too with less effort.

2. Spidering AI and ML

Spidering is one of the most trending techniques based on AI and ML that many software testers utilize to create test cases for the application. For this purpose, you need to point some of the new-age AI/ML tools to your web application, and it will automatically begin the crawling. It collects data related to the core feature during the crawling by taking screenshots. After that, it will feed the data into the system and then compare it with the app’s current state to detect potential issues. In this manner, you can use ML effectively in test automation nowadays.

3. Visual automated UI testing

UI testing or GUI testing is one of the more popular software testing types to validate whether the application’s visual elements meet the original requirements. Nowadays, people are looking to conduct UI testing visually to verify whether the visual elements are in the right shape, colour, size, position, etc. Automating these test cases can be difficult; however, bringing ML into the picture could help you find errors that any software tester would easily miss out on.

4. Creating more reliable test cases

The test cases created earlier by the QA team often won’t work with the current state of the application as developers are making changes to the features. Now, creating new test cases, again and again, would be challenging to manage for software testers. Instead, if you integrate ML into the system, it will start learning about the application behaviour from the beginning. Then, it can dynamically create test cases as per the changes made in the app to ensure reliability.

These were some of the ideas you can use to implement ML in test automation. But, for that to happen in reality, you need to acquire knowledge of various ML algorithms for automation testing.

ML algorithms for automated testing

1. Supervised learning

Here, you provide the machine with correct answers in advance through training data sets. Mainly this technique is used in predictive modelling. Often there is a requirement to gauge the potential risk involved with each release. For this purpose, you have related data available such as number of commits, number of tests and their result, code coverage, number of builds, number of releases, features covered in a release, number of solved bugs in a release, etc. ML algorithms can calculate the score required to identify the risk based on these detailed data.

2. Unsupervised learning

Unlike supervised learning, where you have a lot of labeled data, unsupervised learning involves unlabelled inputs. Here, the algorithm makes decisions based on datasets of unlabelled inputs. Furthermore, you can identify the probability of an error with the help of this algorithm. You can also find out whether the data within one cluster are similar to each other or not through the clustering mechanism. Lastly, unsupervised learning also helps you reduce the complexity of data. With the dimensionality reduction technique, you select and extract a particular feature.

3. Reinforcement learning

Here, you employ the agents that decide the future course of action with the trial and error method. In short, any scenario that you can gamify can have a reinforcement learning technique. For example, bug finding is an exercise with a trial and error method. You can consider an app crash a fatal error, while a minor bug or a design flaw is usual. In this situation, reinforcement learning is the best option for the testers.  Here, you have to assign the end goal, and points (positive or negative aspect) along the journey and the agent will learn accordingly.

These were the three powerful machine learning algorithms used for automated testing. However, the question is, what are the benefits that you get by using ML in automated testing? Also, what type of challenges do you have to face? Let’s try to decode these in our next section.

Benefits and challenges of machine learning for automation testing

Pros of machine learning for automation testing

  • Easy to create test scripts for automation testing tools
  • It saves a lot of time and efforts required to generate test cases
  • Easy to make rules and execute them for software testing
  • You can make all the adjustments in test cases dynamically
  • Efficient handling of test cases and scripts

Challenges of machine learning for automation testing

Knowing user’s behaviour

One of the essential requirements for implementing machine learning algorithms is knowing users’ behaviour. ML won’t execute properly and provide accurate results without that knowledge or the training data sets. Software developers of the 21st century adopt the agile methodology of continuous development and testing. So, in this scenario, knowing the user’s behaviour on the entire product in advance would be pretty challenging. Individual users behave differently, and ML algorithms need that data to identify errors or bugs correctly.

Adapting to app changes

During the application development, the build changes over time. For machine learning algorithms to be practical, they need to adapt to the changes made in the application. It is not as easy as it sounds. They need to ensure that the test cases remain valid throughout the application development life cycle. The features are constantly changing and based on those lines; the test cases need to be modified. In agile methodology, there’s continuous improvement going on in the application. Therefore, dynamically creating and changing test cases is pretty challenging.

Verifying the accuracy of ML’s testing ability

You’re looking for a method to verify the accuracy of testing. Now, in the case of ML, this is not possible without the assistance of any subject matter expert. These experts can help you gauge the accuracy of your ML’s testing ability and provide you with suggestions on improving the accuracy. They can also help you improve the software testing pipeline, the ultimate goal for test automation. But, in a typical scenario without any experts, it becomes quite challenging to measure the accuracy of ML. However, overcoming this challenge is essential for using ML.

To overcome those challenges and use ML effectively in test automation, you need to be aware of the best practices of test automation. It will help you to give a proper roadmap and direction. So, try to follow such procedures while integrating ML in test automation strategy.

Summing up things

In my opinion, ML is the future tech innovation. In present times, people have started to adopt this technology and are reaping benefits out of that. Furthermore, you can leverage the power of ML to solve some of the most complex problems of the IT industry. Testing is one aspect of software development where ML has massive potential. In this article, we have tried to highlight that potential and the concerns with its prime usage. We have also tried to recommend some of the everyday use cases of ML in automated testing.

Now, it’s up to you to adopt such trending technology and establish it as a critical part of your tech ecosystem. The more you will explore the technology, the more in-depth knowledge you will gain. What are your thoughts on this subject? Do let us know your thoughts in the comments section. We would be happy to know your viewpoint on this trending technology.

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