The Future of QA: API Testing With Generative AI

The consequences of poor software quality are increasing, hitting many organizations with the growing costs of functional defects and security vulnerabilities making it into their products and impacting their user experiences. A CISQ report shows that the cost of low-quality software in the United States has grown to about $2.41 trillion. No organization wants to experience the negative consequences of their brand name or consumer experience, let alone the impact that poor-quality software has on their costs.

Unfortunately, the risk of low-quality and problematic software reaching the hands of consumers is increasing, with many companies over the last two years reducing their software development and testing staff sizes. As resources to test the developing software decrease, there’s a higher likelihood that teams under pressure to keep up with their expected development and release velocity will make more mistakes or not have enough time to thoroughly test their code.

In this environment, the question becomes: how can modern QA teams that have experienced staff reductions keep up with the same quality expectations and release velocity?

Generative AI (GenAI) technologies applied to the software testing space present a solution for QA teams to do more with less. While test automation today already helps many companies optimize their testing processes, AI technologies can provide immense benefits by accelerating test creation and helping teams improve the thoroughness of their testing practices. In this article, we explore the role of GenAI in software testing, looking deeper into its benefits, and how to develop an effective QA strategy.

Understanding the Role of Generative AI in Software Testing

At the onset, GenAI was perceived as a subset of artificial intelligence (AI) that specializes in generating human-like content. While GenAI has found its popularity in multiple domains, its role in software testing is becoming increasingly prominent.

With the current state of application teams being required to do more in terms of delivering high-quality software at high velocity while having fewer staffing resources to support these requirements, GenAI is considered a means of optimization. When applied to the software development and testing space, it provides benefits in terms of accelerating processes including the following:

• Creation of test cases
• Understanding requirements
• Test execution
• Defect remediation
• Activity prioritization

While automated testing practices already optimize these activities, the inclusion of AI to further augment software testing provides additional optimization and productivity benefits.

Below are the key roles Generative AI plays in software testing.

• Test case generation. Involves creating step-by-step instructions to verify that a software application functions as expected. GenAI can streamline this process by automatically generating test cases based on requirements and specifications. Utilizing AI in API test case generation expedites the test creation process and expands test coverage by generating test case scenarios that might not have been contemplated by human testers. When integrated into code-level testing, it can augment the precision of existing unit test cases, enrich test cases with parameterization, and simplify the steps involved in maintaining test cases.
• Bug detection. Generative AI can be employed to analyze code and identify potential bugs or vulnerabilities. It can be used to generate test inputs that exploit weaknesses in the software, making it possible for QA teams to be proactive in the bug detection process.
• Static analysis violation remediation. When applied in static analysis workflows, GenAI can accelerate the remediation of found defects by providing recommended fixes for violations and issues that are reported. Accelerating the remediation process for addressing static analysis findings helps development teams ensure that their software is secure, safe, and of high quality with little disruption to the engineers’ day-to-day tasks, allowing them to focus on new code development and increase their productivity.
• Improves test coverage. Ensuring that the application under test has been thoroughly tested before production is essential to identifying potential issues or defects that would have high-cost consequences for the organization. Test coverage is often a big part of determining if an application is ready for release. With GenAI’s capability to rapidly generate test cases for expected and, most importantly, unexpected scenarios, application testing teams can drive deeper test coverage and release with confidence.
• Test data generation. One of the fundamental aspects of software testing is the generation of test data. This data is used to assess how a software application responds to different inputs and scenarios. GenAI can automate this process by creating a wide array of test data intended to exercise edge cases or scenarios likely to contain bugs. By dynamically generating safe test data on the fly based on requirement prompts, agile workflows are enabled, and a higher test velocity can be achieved.

Benefits of Generative AI in API Testing

Generative AI offers numerous benefits for API testing. Discussed below are some of them.

Increases the Speed of Test Generation

GenAI brings a significant advantage to API testing by dramatically increasing the speed of test generation. In traditional testing methods, QA engineers often rely on manual test case creation, which can be time-consuming and limited in scope. GenAI, on the other hand, leverages machine learning algorithms to automatically generate a vast array of test cases, covering a wide range of scenarios and edge cases in a fraction of the time it would take a human tester. This acceleration not only reduces the testing cycle but also enables faster software development and deployment

Reduces the Technical Knowledge Needed for API Test Creation

Creating API tests often requires scripting or development knowledge, and even when an organization has invested in a low-code solution, the tester still needs to understand how the API services are constructed and how they communicate to test API workflows through the application. GenAI’s ability to understand and translate natural language requirement prompts into test cases enables less technical testers to create API scenario tests more easily. This allows application teams to gain more value from their staff and to enhance their end-to-end and integration testing at the API layer.

Enables More Thorough Testing and Increases Software Quality

Thorough, comprehensive testing is one key factor that ensures software quality but can easily be missed in human-centric software testing scenarios. Unlike traditional testing, which may overlook certain scenarios due to time constraints or oversights, GenAI can explore various inputs, combinations, and boundary conditions, enabling the generation of use cases previously not considered by the test engineer. This leads to the discovery of subtle bugs and vulnerabilities that might have otherwise gone unnoticed.

As a result, software applications become more robust and reliable. Moreover, the ability of GenAI to create diverse and complex test cases helps in simulating real-world usage and ensures that the software is well-prepared for a wide range of user interactions and scenarios.

Cost Effective

GenAI’s ability to enhance and optimize API test case creation can have cost-effective benefits for the organization. Its ability to increase test thoroughness and the resulting software quality means a reduction in the risk of defects or issues making it into product releases, impacting end users, and, eventually, the brand reputation.

Finding issues earlier in the testing process means that organizations can also avoid the cost of late-stage remediation, patch creation, or delayed releases due to poor quality. While today test automation tools and frameworks are already heavily used to optimize testing and increase productivity, GenAI can further enhance test automation practices, helping programs to shorten their time to market and ensure their applications have low risk come release time.

How Parasoft Is Incorporating Generative AI in Its Functional Test Strategy

Parasoft SOAtest leverages GenAI into its API test creation capabilities helping application teams in the following ways:

• Accelerate test creation.
• Reduce the technical knowledge level needed to create API scenario tests.
• Increase thoroughness and reduce the cost of testing.

Teams can integrate SOAtest with OpenAI/Azure OpenAI, and it supports the GPT 3.5 and 4 models. This integration empowers users to automatically generate advanced test cases based on API service definitions while filtering out irrelevant results through Parasoft’s proprietary prompt engineering.

Overall, such an approach significantly reduces the time-consuming process of test creation and allows QA teams to focus on the critical aspects of business. SOAtest with GenAI rapidly generates multiple test case scenarios, including scenarios not originally considered by the test engineer, so teams can improve their overall test thoroughness.

Closing Thoughts: Optimize QA Team Productivity With Generative AI

GenAI is a new technology, and we’re only starting to scratch the surface of its potential applications and benefits across software development and testing spaces. Early implementation of GenAI, as we see in Parasoft SOAtest, marks a transformative leap forward in optimizing QA team productivity.

With routine testing tasks being automated with AI, it becomes easier to achieve increased test coverage and accelerate test case generation tasks, thereby helping QA professionals focus on higher-value activities such as strategic testing and data-driven decision-making. This not only enhances the efficiency and effectiveness of QA efforts but also contributes to the overall quality and reliability of software products.

With the ability to adapt and improve over time, GenAI is poised to play an increasing role in the ever-evolving landscape of software testing. Therefore, embracing an AI-driven testing approach is no longer a choice if you wish to deliver superior software products in today’s competitive market—it’s just a matter of when.

Contributing author: Jamie Motheral