Applications of Generative AI
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Introduction
What is the latest buzzword you see everywhere- articles, social media, news, television?
Right, that is ChatGPT! Everyone is talking about ChatGPT, its benefits, use cases, implications, and whatnot.
ChatGPT is a generative AI (Artificial Intelligence) system under the broad ML(machine learning) category, wherein GPT stands for – Generative Pretrained Transformer. Generative AI is on the rise, and all big players are incorporating applications based on generative AI into their ecosystems. Every market domain is touched by it, benefiting everyone involved.
Can generative AI also support software testing specifically to help QA professionals and organizations?
According to Gartner, by 2025, 30% of enterprises will have implemented an AI-powered development and testing strategy, up from 5% in 2021.
Software testing solutions are available in the market, harnessing the power of generative AI. There are numerous benefits, where enhanced user experience, higher productivity, and speeding up the whole development cycle tops the list.
Let us dive deeper into its applications and details.
Applications of Generative AI in Software Testing
Below are some brilliant applications of generative AI that aid the software testing process immensely.
Test Data Generation
Generative AI trains itself first on data and then provides the required output. It can generate enormous amounts of test data quickly that is diverse, realistic, and consistent. Generative AI provides the below benefits through test data generation.
Scalability: Generative AI can help you achieve test data scalability at a manually impossible rate. Also, these benefits save effort and time lost in the arduous task of manual test data preparation.
Confidentiality: Use generative AI to mask sensitive and confidential actual user data successfully. Generate synthetic data, which is statistically perfect for testing but hides the sensitive information.
Improved Test Coverage: Uncover the potential issues and improve test coverage drastically with a diverse and huge dataset, which is practically impossible manually.
Rare Test Data Generation: You can create synthetic test data covering various scenarios, including rare and unusual test conditions.
Example: Consider an example of an e-commerce application under test, which recommends products to users based on their search/browsing history. Generative AI can quickly generate data for diverse scenarios:
- Diverse user geographies, languages, preferences, etc.
- Users with no browsing history at all
- Edge cases, such as users with unusual purchases
Automated Test Case Generation
Test generation is the best feature of generative AI-based software testing tools. When using traditional automation testing tools, a significant chunk of test effort includes test case scripting. Using automated test case generation features, you can save enormous test effort. See here how to create an automation test script with testRigor in seconds. You just need to provide the test case title, and AI generates the script in seconds.
You can modify or customize the generated test or use it as is. Use the saved time and effort to contemplate more varied scenarios and improve the test coverage. You need to think and pass on the test case description to testRigor, and it does the rest of the automation scripting in no time.
Example: Consider an example of an e-commerce site, and you want to write a test script to search for a Kindle and add it to the shopping cart. Using traditional test automation scripting methods will require hours of coding to write all test steps.
With testRigor, all you need to do is write the test description in the tool, and that’s it. Simply write “search for Kindle and add it to shopping cart” in the test case description, and you’re done! testRigor will generate all the custom test steps within seconds. Does it sound like a dream? While this technology is still under active development, it promises to save countless hours.
Scenario Generation
Use generative AI to simulate user activity patterns and generate real-world scenarios. Generative AI models, such as recurrent neural networks (RNNs) or transformers, can be trained on substantial real user data. Then generative AI uses this data to create varied test scenarios such as:
- Stress and load conditions
- Usability and user experience
- Rare conditions
- Regression tests
- Errors and system failures
Generating diverse scenarios helps increase test coverage manifolds, covers unusual scenarios, and improves test efficiency.
Example: Consider a ride-sharing application and generative AI simulating surge pricing scenarios with high demand and fare increase accordingly. This simulation helps to evaluate surge price calculation, user notifications, and application performance due to high usage.
Test Environment Generation
Use generative AI to create personalized test environments such as network topologies, user preferences, device configurations, security conditions, storage, fault injection, etc. Generative AI helps replicate the production environment and provides accurate test conditions to simulate the real-world environment. Eventually, this helps in better test coverage, user experience, and early identification of potential defects, thus improving product quality.
Example: Consider a cloud-based e-commerce web application that works on multiple server instances. You can train the model using generative AI on real-world server configurations and usage. Generative AI models can now generate synthetic server configurations such as the number of server instances, CPU, OS, memory configurations, etc.
It can also inject faults such as server crash to test the disaster situations. Such features help testers validate the application’s functionality and performance under varied server conditions. Manually it is impossible to achieve such perfection in test environment generation.
Test Case Maintenance
Generative AI learns the patterns and characteristics of the existing test cases, applications, test data, defects, test execution details, etc. This training helps it in test case maintenance for:
Test Case Updates: When developers introduce new features into the application, generative AI models update the relevant test cases.
Test Data Refresh: Generative AI models automatically update the test data based on the new constraints and data distribution.
Regression Update: Generative models analyze the modified code, identify affected functionalities in the application, and update the regression test cases accordingly.
Test Case Augmentation: Intelligent generative AI models analyze the existing test cases, tweak them by making small changes, and create new augmented test cases for better test coverage.
Test Case Optimization: Test requirement overlap and relationship analysis of test cases help generative models to identify redundant test cases. Further, it eliminates duplicate test cases to keep the test suite as optimized as possible.
Test Case Prioritization: Generative AI trains on historical defects, code changes, and execution data. This analysis helps to rank the test cases based on criticality, business priority, impact, complexity, etc. This information allows testers to streamline the test execution based on priority and business impact.
Example: Consider an e-commerce website that only allowed Cash On Delivery (COD) purchases till now. Now the application has implemented credit cards and other payment methods as well. Generative AI automatically analyzes the new features and updates the existing test cases with new payment method flow.
Defect Detection
Generative AI trains on bug-free artifacts such as code or specifications and creates a standard behavior model. By comparing the behavior of the generated normal model with the actual system behavior, system anomalies, potential bugs, problem areas, etc., can be identified. This comparison helps testers identify and raise defects based on this data by generative model, further improving the software quality.
Example: The generative model highlights potential memory leaks, race conditions, or improper error handling of an application based on comparative analysis, leading to defect detection.
Read an article here to find out how to uncover masked defects.
Defect Analysis
Once testers log the defect, generative AI can support them in analyzing its root cause effectively. Generative models leverage machine learning algorithms and natural language processing (NLP) techniques to analyze defect reports, user data, system behavior, etc. This analysis highlights the defect causes, and stakeholders can take corrective action accordingly.
Example: Generative AI analyzes various data sources, like bug reports, user feedback, or customer support cases, to identify keywords associated with reported defects for an e-commerce application.
One of the keywords derived from the analysis is “Remove from Cart”. Then stakeholders use the keyword “Remove from Cart” to identify one of the major problem areas of the software application and update the test cases accordingly. If new test cases are required, they are added to cover the defect analysis keyword in the test suite.
Find here the strategy to handle defects in an Agile environment.
Conclusion
Generative AI has brought a force of change in software testing with its unwavering capabilities, tools, and techniques. Generative models help testers do their job more efficiently and faster. They accelerate the test case creation process and reduce the testing team’s efforts and associated costs.
AI generative models complement human expertise and creativity immensely. The collaboration between AI models, such as generative adversarial networks (GANs) and human intelligence, offers capabilities that enhance test case generation, test data creation, scenario generation, defect detection, and test case maintenance.
The change has arrived, and it is here to stay. The key is to leverage the incredible capabilities of generative AI for the benefit of the application, developers, testers, and most importantly, users and their user experience.
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