Generative AI in Software Testing

How does this affect the software testing field?

The software testing field has also evolved with the evolution of technology. In fact, we can say it has transformed dramatically with the advent of AI. Generative AI testing is the most promising and exciting technique, and it has spearheaded a revolution.

The Generative AI testing approach is groundbreaking and has opened new ventures in software quality assurance. It boasts of unprecedented effectiveness and efficiency in software testing.

The Generative AI approach goes beyond traditional automation and embarks on a new journey of producing clear and valuable outputs autonomously instead of executing predefined steps.

In this article, we will discuss various concepts related to Generative AI in software testing, focusing on the testRigor tool for automated AI testing.

What is Generative AI?

Generative AI is a system that can create novel and valuable content. This content is complex and can also fit specified parameters. Generative AI generates various types of content such as audio, code, images, text, simulations, and videos. By adapting various evolving techniques and utilizing Large Language Models (LLMs), Generative AI generates information.

The concept of Generative AI works on the same principle as that of machine learning (ML) models. The ML models train on huge amounts of data and generate unique outputs from the specified user input.

So, what is in it for quality assurance (QA) or software testing?

Well, Generative AI technology, when used in QA, has improved efficiency, accuracy, and the ability to handle complex, large testing scenarios without Herculean efforts.

Imagine a situation where you have to write a test case. Using traditional automation, you will write a detailed function or procedure instructing what to do when testing a particular scenario. This is a very cumbersome technique. As the scenarios become complex, the test cases grow, making them difficult to maintain.

Instead, imagine using Generative AI for test case creation. It would be easy to feed the test case description to the system and then click a button. Next, you can see a comprehensive end-to-end test, and that too within seconds!

This is precisely what the #1 Generative AI-based Test Automation Tool, testRigor, achieves.

Generative AI in software testing uses an advanced testing approach and successfully minimizes the efforts of human testers. This makes the testing process faster and more efficient, and it also significantly improves the quality of software tests.

Brief history of QA

The field of QA has been evolving continuously since its inception. With changes in the software development field, the QA world has also undergone considerable changes to meet the demands of rapid technological changes.

This evolution started with manual testing and then developed further into scripted automation. Then came the days of data-driven testing, and now it is a Generative AI revolution with advanced LLM models. Follow the diagram below to understand this journey of QA better. Let us briefly discuss each of these QA stages.

Manual Testing

To start with, QA began with manual testing. Manual testing is a process in which:

● A human tester evaluated and tested the software repeatedly to find defects and bugs

● The scope is limited as the process is time-consuming, and there is a possibility of human error and defects being overlooked.

● Test cases are manually written, executed by following the steps, and the results are recorded.

Manual testing is a labour-intensive process that presents challenges, ranging from the high risk of human error to difficulties with comprehensive software test coverage.

Scripted Automation

Scripted automation is a significant leap from manual testing.

In scripted automation:

● Repetitive test cases are written into scripts that can be executed using automation tools

● Significantly reduced manual efforts and also improved efficiency

● Facilitates the testing of complex systems.

● Lacking adaptability, and cannot cope with unexpected variations in test scenarios.

Data-driven Testing

Data-driven testing kind of provided a solution to the limitations of scripted automation. The process of data-driven testing:

● Uses predesigned test scripts to perform tests using data sets

● Can create multiple test case scenarios from a single script

● It can perform more comprehensive testing and provide enhanced test coverage.

● Requires a considerable amount of manual input

Generative AI in Testing

Generative AI testing is seen as a game-changer in the software testing industry. It uses LLMs to generate outputs such as test cases or data without human intervention.

Generative AI in software testing:

● Revolutionizes the quality assurance

● Utilizes Natural Language Processing (NLP) and deep learning algorithms

● Autonomously generates highly effective test cases

● Incorporates predictive analytics for optimized testing that goes beyond automated testing.

● Combines intelligence with automation to create efficient, accurate, and reliable test results.

Benefits and Challenges of Generative AI in QA

As more organizations use Generative AI in QA, evaluating its benefits and challenges is imperative. Generative AI offers numerous benefits that enhance the overall testing process.

The key benefits of Generative AI in QA are shown in the following table.

With these advantages of Generative AI come the challenges and concerns to which attention should be paid.

Some of the significant drawbacks of Generative AI are summarized in the following table:

Types of Generative AI Models in Software Testing

In this section, let us discuss popular Generative AI models used in software testing.

Generative Adversarial Networks (GANs)

GANs generate new data using AI that closely matches to input data. In the QA realm, GANs generate testing scenarios depending on existing data and closely resemble realistic conditions. GANs consist of a “generator” that creates new data and a “discriminator” that evaluates the generated data to determine its authenticity.

With this dual structure, GANs generate highly effective test scenarios, although, may be complex to train and require considerable computational resources.

Transformers

Transformers is another form of Generative AI; its most common example is GPT-4. Such models are primarily valuable for code completion or generating tests given a description. In general, transformers have expertise in understanding context and sequence within data. The general idea behind transformers is that they read and analyze the input data before generating output.

Recurrent Neural Networks (RNNs)

RNNs have internal memory that they can use to make predictions or classifications. RNNs are suitable for speech recognition, time series analysis, and NLP.

Despite being powerful, RNNs have limitations, such as the inability to capture long-term relationships and vanishing gradients.

Diffusion Models

Diffusion models help you create high-quality test data with diverse characteristics. A simple distribution is converted to a complex one using reversible operations. The model is better fitted for image and text generation.

Variational Autoencoders (VAEs)

This Generative AI model uses probabilistic modelling and autoencoders. VAEs can compress data into a lower-dimensional latent space, generating new data points.

In QA, VAEs can create synthetic test data and analyze variations in existing test cases.

Develop a QA Strategy with Generative AI

A sound and structured approach is necessary to use Generative AI efficiently in software testing. The following steps can be incorporated to develop a detailed QA strategy.

1. Step 1: Define Clear Goals: Clearly identify what you hope to achieve by switching to the Generative AI testing approach. The goals can be reducing manual efforts, enhancing test coverage, improving speed, or enhancing performance.

2. Step 2: Evaluate Testing Needs: For a Generative AI testing approach to be successful, you should first evaluate your testing needs. Not all applications can benefit from using a Generative AI testing approach. Consider factors such as data availability, test complexity, and so on to decide if this approach can add value to the QA.

3. Step 3: Assess Infrastructure and Expertise: Generative AI models require high-performing computational resources and skilled personnel. Hence, the available infrastructure and expertise should be assessed.

4. Step 4: Select the Right Tools: The Generative AI models and tools you choose must align with the goals set and resources available. Each Generative AI tool is unique and exhibits varied strengths and limitations. The appropriate tool that is in sync with the organizational objectives should be selected.

5. Step 5: Train the Team: The QA team should be trained with adequate skills to use the Generative AI testing approach. This should include teaching the team to use specific tools and processes, and basic training in Generative AI.

6. Step 6: Implement and Monitor: Once the goals are defined, various needs assessed, tools selected, and personnel trained, it is time to implement the testing strategy. Gradually introduce and switch to a Generative AI testing approach. At the same time, track its performance and adjust as needed. Employ a continuous monitoring process to assess performance.

Using testRigor’s Generative AI in Software Testing

As mentioned, testRigor uses a Generative AI approach in software testing. Using generative AI, testRigor allows you to generate two test cases.

● Generate a sample test with AI.

● Generate an actual test with AI.

In either case, you must specify the test case's title or description and click the respective buttons to download the test cases.

For a sample test, a series of steps is generated in the correct format, which can then be tailored to your specific needs to create a test case.

If the chosen option is to generate an actual test with AI, testRigor generates a comprehensive test based on the application details provided.

Here, we will show you how to generate an actual test as an example. When you click “Generate actual test using AI,” let us show you the steps.

Once the “Generate actual test using AI” button is clicked, testRigor AI creates a complete end-to-end test, showing detailed screenshots for each step. Take a look at the screenshot below.

These screenshots taken at every step make it easy for the user to understand the stepwise process. You can see the status of the test case (Passed or Failed) along with the time taken to complete the execution. The generated output is indeed fast and also fully customizable.

The generated test case can be modified as per the requirements and re- run.

With testRigor’s AI-based test generation features, QA professionals can accelerate and simplify the test creation. This considerably frees up time and resources required for test creation, allowing attention to more complex and strategic aspects of QA.

Conclusion

With the Generative AI testing approach, it is clear that we are embarking on a new revolutionary era. Generative AI promises context-specific, automated tests that considerably reduce human intervention and enhance test coverage.

Although various challenges are associated with using AI models and integrating them into existing QA workflows, the benefits offered clearly outweigh them.

Incorporating Generative AI in QA signifies a paradigm shift in the testing approach. As we embark on this new revolutionary era, it is important to carefully define goals, understand QA needs, and adopt a strategic approach that will ultimately benefit the organization.