Why is using AI to test AI at the top of every QE engineer’s mind? 

According to a recent report by Fortune Business Insights, the global AI-enabled testing market size is projected to grow from $736.8 million in 2023 to $2,746.6 million by 2030, at a CAGR of 20.7%. As Artificial intelligence becomes more sophisticated and seeps into every industrial sector, the complexity of modern software systems will only increase. Thus, it will not be sufficient for QEs to ensure that complex AI systems run flawlessly and are highly efficient. While conventional software testing techniques are still in use, newer complexities are forcing a drift toward AI-driven testing. 

But why is the role of an AI tester using AI vital for QE engineers today? Let’s dive into some of the reasons for this. 

But before we get into that, let’s look at a case study of a leading bank. 

A prime regional bank was optimizing its Loan Origination and Disbursal process. It was looking to develop an application to score leads daily because customer information was changing. New data points were coming regularly that could change the lead-to-be-pursued or not-to-be-pursued decision, thereby altering the probability of loan sanction approval. The constant influx of new information meant that test cases and scripts were also frequently changing, making it difficult for the team to manage and update them effectively. 

AI-based techniques were invaluable in this case; they would automatically generate test scripts whenever the baseline or the new features were changed. Now, here is what precisely happened: 

 Identify Key Topics and Requirements: AI would automatically analyze the requirement documents to determine which topics and functionalities needed to be tested. 

 Application Code Analysis: Through advanced deep learning techniques, AI scanned the application code for critical data pipelines, modules, and integrations to be tested through its test cases. 

Automated Generation of Test Cases: AI based on its analysis, automatically generated test cases on stable baseline features and newly introduced data points. 

Autonomous Test Script Generation: In this case, the AI created test scripts fully according to the application’s acceptance criteria and KPIs to ensure that every scenario was covered. 

Predict and Prescribe: Predictive analytics helped find bugs early in the process. It also prescribed appropriate self-healing frameworks, which minimized manual interventions. 

This would save the nightmare of frequently updating the test script by the bank’s team to ensure the system adapted to changing data, eliminating several risks and assuring accuracy in loan decisions. This is a small example showing how AI can be helpful in testing AI. 

The Need for AI in Software Testing 

Manual testing is time-consuming and prone to errors, especially in agile and continuous development environments. With AI systems, this challenge is only magnified. AI-driven testing tools like Testim, Applitools, Functionize automate recurrent testing tasks, such as the generation and execution of test cases, which then causes those processes to speed up dramatically without sacrificing accuracy.  

In fact, automating these mundane tasks frees the QE engineers to focus on strategic initiatives than manual testing which increases test coverage and reduces human error. These tools allow running thousands of tests against multiple environments in a single instance of time, which will be impossible with manual testing. 

 Second, AI is fully integrated with CI/CD (Continuous Integration/Continuous Deployment) pipelines to enable continuous testing. This automatically triggers tests with every code change, enabling faster development without compromising quality. 

1. AI’s Predictive Capabilities and Proactive Defect Detection 

One of the most significant advantages of using AI in software testing is its predictive capabilities. AI-driven tools can analyze historical test data to predict which areas of the software are most likely to fail. This allows QE teams to proactively focus their efforts on the riskiest components, significantly reducing the likelihood of post-release defects. 

Predictive analytics also aid in identifying bugs early in the development cycle. By analyzing patterns in previous defects and test failures, AI tools can forecast future issues, enabling engineers to resolve them before they impact users. This leads to faster release cycles, reduced downtime, and improved product quality. 

2. Enhanced Test Coverage and Anomaly Detection 

Subtle defects or edge cases can often slip through traditional testing methods in large and complex systems. One area where AI outperforms human testers is anomaly detection. AI-based tools identify patterns and inconsistencies using a large amount of test data, which cannot be accomplished by conventional test coverage. AI tools also enable real-time monitoring and anomaly detection, which is critical in sectors that require continuity, like finance, healthcare, and manufacturing. This real-time capability allows organizations to catch and resolve issues before they escalate. 

3. Continuous Learning and Adaptation 

Another factor that puts AI at the top of QE engineers’ minds is that AI can learn and improve continuously. AI-driven test tools are in a position to evolve with the application under test. By learning from new data and past testing cycles, these tools can automatically adjust test cases to align with new requirements, resulting in a self-healing automation process. 

This adaptability is critical in fast-evolving development environments where codes are constantly being updated. In such situations, the need for manually updating test scripts after each minor change by QE engineers ceases to exist. AI tools can dynamically update tests so that consistency and accuracy are maintained throughout the software lifecycle. 

4. Overcoming Skill Gaps and Ethical Concerns 

While AI offers significant benefits, its adoption in QA processes does come with challenges. One of the primary obstacles is the skill gap among QE engineers. Implementing AI tools requires specialized knowledge of machine learning algorithms and data analytics, pushing organizations to invest in upskilling their teams.  

Moreover, issues such as bias in AI models or privacy breaches should be addressed. Responsible use of AI in testing processes becomes inevitable, considering that they remain unbiased, fair, and secure. 

Despite these challenges, the long-term advantages of AI-driven testing, including improved accuracy, faster testing cycles, and enhanced decision-making capabilities, make it a worthwhile investment for QE teams. 

5. A New Breed of QE Engineers: AI and Prompt Engineering Experts 

The rise of AI-driven testing has given birth to a new generation of QE engineers who excel in prompt engineering and understand the intricacies of AI applications. These engineers craft precise inputs to guide AI models in generating test cases, simulating scenarios, and diagnosing issues. Their ability to interact effectively with AI systems makes them pivotal in harnessing AI’s full potential for optimizing testing strategies and ensuring software quality. 

This shift is creating more strategic QE roles, where understanding AI algorithms and prompt design is critical to future-proofing testing efforts. 

Final Thoughts 

Using AI to test AI is no longer a futuristic concept; it’s a reality shaping the present and future of software testing. For QE engineers, leveraging AI-driven tools enables more efficient, accurate, and scalable testing processes, helping them keep pace with the demands of modern AI systems.  

From automating routine tasks to predicting defects, improving test coverage, and continuous learning, AI makes quality engineering a whole different ball game. The capabilities of AI are revolutionizing quality assurance processes, allowing high-quality software products to be delivered faster and more seamlessly than ever. As AI technologies continue to develop, their role in testing AI will only rise, challenging QE engineers to rely heavily on these new breakthrough solutions. It is high time for organizations to invest in AI-driven tools and upskill their teams to maintain their lead position in this fast-changing software testing domain. 

Indium is your trusted partner for Quality Engineering Services. With over two decades of experience and a team of 400+ skilled SDETs, we deliver exceptional results using our AI-powered Smart Test Automation platform, UphoriX. As businesses increasingly embrace digital transformation, we ensure that your engineering, user experience, and data assurance are top-notch. From low-code applications to cutting-edge AI and IoT products, we have the expertise to build fast and the right solutions.