How AI Is Changing Software Changing Techniques

Still not using AI in testing? It is high time to start investing in this advanced process to ensure that even your complex app testing cycles remain productive, more scalable, and efficient. Leveraging test AI technologies, along with artificial intelligence and machine learning models, you can improve test automation processes, predictive analytics, and smart algorithms.

Don't know how? Don't worry! We are here for you. Our article will help you understand how AI is changing the modern software testing industry to make it more efficient, reliable, and accurate. We will also help you go through some of the best practices and strategies that can help you adopt this process.

Challenges Of Traditional Testing

Before you understand how artificial intelligence can help improve your software testing cycles, it is very important to have a clear idea about the limitations of the current testing architecture. To shed more light over this segment, we have mentioned some of the major challenges below:

• With current software testing, if you're using manual testing, you have to create all the test cases and also run them yourself. This process can be highly prone to human error due to fatigue and oversight. It is also a massively time and resource-consuming process.

• If you have planned to use automation testing, you're probably using traditional rule-based automation, where you still have to create the test scripts manually. This means that you will require enough technical knowledge to understand the programming languages and also create the test scripts.

• Both manual and automated test cases are highly prone to breaking. This means that if you change the placement of any element on the user interface of your application, you have to manually go through all the test cases to update them accordingly.

• It becomes a very time-consuming process to track the metrics across all your test cases. You also have to add various third-party tools to perform proper documentation and test report analysis.

• For automation testing, it is initially a very complex process. This is because you have to set up all the dependencies, libraries, and tools that are required for this process. It will also require a lot of revenue, which is mostly impossible for small companies to invest.

AI in Software Testing

Now that we know all the challenges that you will face with traditional software testing, let's divert our attention to some of the major features that AI in software testing can offer you to overcome these challenges:

1. Intelligent Test Automation

Compared to traditional automation testing, AI-powered tools will help you dynamically create the test cases. In this step, you can also use machine learning models to understand the application’s workflow so that you can automatically create the test cases without any form of manual involvement.

The test cases that will be created by artificial intelligence and machine learning can adapt themselves to all the changing requirements of the application without you having to worry about it. 

2. Predictive Analytics and Defect Prediction

Artificial intelligence can use the historical test data to understand potential defects present within the application infrastructure. Depending on the information that it will gain from this process, it can highlight these areas so that you can take the required actions before they can become a major concern.

To perform these processes, the AI models will use the past testing outcomes, code changes, and the software complexity metrics. 

3. AI-Enhanced Test Maintenance

Usually, it becomes a very resource-intensive process to maintain your test cases as your application continues to grow. But with AI algorithms, you can automatically detect the changes in the software and update your test cases accordingly.

With this effort, you can hugely reduce the maintenance efforts to ensure continuous testing cycles without investing a lot in manual test cases. It also has a very important role in ensuring the proper quality of your application releases. 

4. Visual Testing

Usually, visual testing is considered as a manual-only testing process, as you require a human tester to manually verify the proper placement of all the elements on the user interface of the application. But with the arrival of artificial intelligence, you can use machine learning and computer vision to identify visual changes that even humans can overlook.

With this technique, you can automatically validate the user interface to ensure that the visual elements remain persistent across multiple platforms and resolutions that the user might have.

5. Intelligent Bug Triage

Bug triaging is a very important part of the application testing process, but often consumes a lot of testing resources. You can use artificial intelligence to streamline this entire process, as you can automatically classify, prioritize, and assign the bugs depending on the historical test data and the severity of the bugs. 

If you're properly able to implement this entire workflow, you will have faster test resolution times and will also help improve the efficiency of the resource utilization processes.

Advantages of AI in Testing

Now that we know how you can use AI in testing, let us divert our attention to some of the major advantages that you will have while performing this process:

• As we already mentioned earlier, using artificial intelligence, you can automate the repetitive tasks so that you can perform rapid execution and test analysis. You can also use the additional human resources to focus on the creative and complex areas of the development project.

• Using AI, you can reduce the human error by automating the test creation, execution, and result analysis processes. On the other hand, machine learning algorithms will help you ensure the consistency and precision to reduce overlooked bugs and errors within the application infrastructure.

• It is not rocket science that AI-driven test cases can scale easily as they can accommodate the increasingly complex software ecosystems. So, as AI application continues to grow in complexity, the artificial intelligence problems and smoothly expand the test coverage without massively increasing the cost or the resource requirements.

• In reality, AI-driven test automation will reduce the cost as you will be minimizing the need for extensive human labor. It also cuts the cost in terms of errors and development cycles. Therefore, although it might be a significantly high cost in the initial phases, it will be a massive savings in the long run.

• It is also very important to understand that the exact advantages of AI in testing will certainly vary depending on your unique requirements and preferences. We also suggest you to have a clear understanding of what you want out of this integration. This knowledge will help you guarantee that you are using AI to your full potential.

Challenges Of AI Testing

Although artificial intelligence is such an advanced process, it also has its own share of disadvantages. To ensure that you are not falling into any unwanted obstacles, we have mentioned some of the major challenges that you will face while using AI in software testing:
 

• The training data is very important to ensure the consistency of the model's decision-making process. So, if you do not have high-quality data or you're using incomplete data sources, it will directly impact the functioning of the entire AI-based testing environment.

• In some cases, the decisions taken by the AI model will not be clearly understandable. This is because the AI models often use a black box technique where it never justifies the decisions or methods implemented by it.

• Adding AI to your software testing infrastructure will involve a lot of significant upfront investment. These investments will mostly cover the expenses of all the tools, data infrastructure, and skilled personnel that will be required to ensure the successful integration. If you're a small company with limited revenue, you might not be able to justify this shift.

Best Practices For AI In Testing

Let us now divert our attention to some of the best practices for using AI in software testing. This is because these practices will help you ensure that you are utilizing the entire capabilities of your testing environment with this advanced integration:

• Since the entire concept of AI in testing is very new, we would not suggest you to directly migrate your entire testing infrastructure to it. Instead, a wise choice would be to choose a non critical test and experiment with it. Depending on the results that you achieve in this process, you can scale accordingly to the other aspects of the testing workflow.

• Tools play a very important role to test AI components on your applications. So, it is very important to start investing in high-quality AI testing tools and platforms like LambdaTest. LambdaTest is an AI-native test orchestration and execution platform that lets you perform manual and automation testing at scale with over 3000+ browsers, OS combinations, and real devices.

• Although AI might seem to be the perfect replacement to human involvement, it can never completely eliminate the requirement of a human tester. Instead, the most effective approach would be to maintain a balance between human knowledge and AI consistency. How? Use AI for repetitive test cases and human knowledge for the complex testing scenarios.

• Since the entire AI workflow will require some sort of training to get your testers and developers accustomed to it, you must start investing in high-quality training sessions and documentation. This will help reduce the learning curve and ensure faster adoption throughout the organization.

• You must maintain thorough documentation throughout the test creation and execution process. This documentation will store all the bugs and errors that you will encounter during your testing process. It is also a great idea to store references to all the challenges that you face while using AI in your software testing cycles.

• Finally, you must understand that since it is such a rapidly growing market, there will be many more trends and innovations coming to this industry. It is a very good idea to look out for all the upcoming trends and start incorporating them within your infrastructure from day one.

The Bottom Line

Depending on all the areas that we went through in this article, we can safely come to the conclusion that AI is changing how you test your software. With this process, you must make sure that you are using all the practices and tips that we have given in this article. Why? To make sure the highest level of efficiency, accuracy, and stability.

It is also equally important to understand that although AI has its own share of challenges, with adequate planning and strategy, you can easily overcome them to ensure that you're constantly boosting your testing parameters.