With AI emergence people started to investigate and research how to implement Artificial Intelligence in their business to be more efficient and effective. Let’s see how AI can help us during our testing activities.
With the global growth of AI (Artificial Intelligence) in all business sectors and in our daily life, we have witnessed that it can be a valuable ally within the SDLC (Software Development Life Cycle). All kinds of businesses started to explore how to implement and how they can use AI for their testing purposes.
This article will discuss about AI software testing, its benefits and some differences between manual and AI testing. Let’s see how AI can help us in QA, enhance our quality processes and, therefore, the quality of products.
What it AI Testing?
Artificial Intelligence can help us in several ways to improve efficiency and effectiveness. It’s used to automate test processes, generate new test cases and analyze data to predict potential defects.
AI algorithms can simulate user behavior, check test results and identify failure or success patterns.
Human testers are essential for the strategic test planning, exploratory test and for applying their expertise. Implementing AI will enhance our current test practices.
Manual Software Test vs AI Software test
Manual software testing and AI software testing have differences in several aspects like test approach, efficiency, test coverage, learning, adaptation or error prone…
- Approach: Manual testing involves testers executing different test cases and scenarios manually, following predefined steps and verifying the expected outcomes. AI testing uses machine intelligence and algorithms to automate different test tasks like test cases generation and execution.
- Efficiency: AI is generally faster and more efficient than the manual test.
- Test coverage: AI testing can achieve a wide test coverage due to its ability to generate a huge amount of test cases, simulate scenarios and analyze data. On the other hand, Manual testing may have some limitations in covering all possible scenarios and even more with complex systems.
- Learning and adaptation: AI testing can learn from data, analyze different patterns and adapt the test cases accordingly. Identifying risk areas by usage of the AI, prioritizing tests and optimizing the testing process. Manual testers rely on human intuition and may not have the same level of data-driven learning and adaptation.
- Error prone: Manual testing is always susceptible to human errors like inconsistency or oversight. This can lead to miss defects or inaccurate results. AI testing reduces this risk ensuring more consistent and reliable test execution results.
Remember that implementing AI testing does not mean replacing manual testing entirely. AI testing shall be used as a support, a complement, which combination of both ways of testing can give us more effective and efficient testing processes.
Advantages of AI in Software Testing
Let’s see some of the advantages of integrating AI in our Software Testing procedures:
- Test automation: AI can help us automate the process of test case generation, execution and evaluation. This reduces manual effort and makes the testing process faster.
- Increases Test Coverage: AI can analyze test and code data and after that, generate test cases to cover scenarios, boundaries, edge, negative cases etc. This will increase our test coverage also helping us to idenfity potential issues before the software is released.
- Test optimization: Our test processes can be optimized by AI analyzing requirements and test cases. This can give us the best point of view to prioritize the test case, having a better test strategy to focus on critical areas of our application.
- Regression test: AI can obtain the data of the changes made to our software and automatically identify the affected areas to be retested. With this information, we will be more meticulous generating our Regression test plans and having a faster regression test.
- Reduce cost and time: Integrating AI in our daily work can help us to reduce time and cost, helping the team to be more efficient. It allows the team to focus on more critical testing activities.
In conclusion, it seems that Artificial Intelligence has come to stay and help us in our daily work. If we decide to integrate AI in our team, it will help us to improve efficiency and have faster software delivery with the highest quality software.
AI tools can be helpful during testing
There are several tools that can give us extra help to accomplish the commitments in time with less human effort and resources.
Apparently not all the tools are suitable for every project, so we need to analyze which one can fit in our daily work and which will give us better benefits.
Some of the tools that can be used are:
- Selenium with AI integrations: As you may know, Selenium is an open source test automation framework. The integration with the AI can enhance its capabilities by providing intelligent element locators and an improved test script generation.
- Test.ai: This tool uses AI to perform UI testing identifying and verifying the UI elements. It automatically creates test cases based on the UI components of the application.
- Applitools: Provides AI-powered visual testing solutions that can automatically detect UI defects and layout issues across different devices and browsers.
- Diffblue Cover: This tool uses AI to automatically generate unit tests using Java. It uses machine learning to understand code patterns and create tests.
- DeepCode: This AI-powered tool reviews code to find potential bugs, security vulnerabilities and performance issues by analyzing code patterns and best practices
AI has the capabilities of empowering QA teams to focus on higher valuable tasks. This can help us to improve efficiency and provide faster and more reliable software deliveries with the highest quality software.
