Quick Results, Smart Data – AI for Testing 

We know how challenging and time-consuming your work can be. The constant need to create detailed test cases, manage endless amounts of test data, and ensure everything runs smoothly. Add in the pressure to deliver high-quality products quickly – it’s A LOT.  

In our recent webinar, “Gen AI That Obeys, Testing That Stays: Transforming Testing Paradigms”, Meher Bondili, Head of Quality Engineering at Prolifics, explains how generative AI is changing the game in software testing by automating the creation of detailed test cases and managing extensive amounts of test data. This automation accelerates the testing lifecycle, reduces operational costs, and significantly enhances the overall quality of software products. 

As products evolve and market demands increase, traditional testing methods often struggle to keep up. Fortunately, there are “AI for Testing” solutions that use generative AI and are designed to make your work easier, more efficient, and more accurate. Plus, you can be confident that generative AI applications play by YOUR rules so you can trust the results and focus on what really matters. 

Kiran Bhashyam, Solution Directory of Quality Engineering at Prolifics, adds, 

Generative AI is not just about automating tasks; it’s about transforming the entire testing lifecycle, making it more efficient and robust.

Key Benefits for Testing Teams

• 70% Increased Efficiency: AI-driven automation tools can generate and execute test cases rapidly, leading to a 70% increase in efficiency. This allows testing teams to focus on more complex and value-adding activities. 
• 20% Improved Test Coverage: AI’s ability to analyze large datasets enables a 20% improvement in test coverage, ensuring that all edge cases are identified and addressed, thus minimizing the risk of undetected defects. 
• 50% Time Savings: The automation of test case generation, execution, and result reporting can save up to 50% of the time traditionally required for these tasks, allowing faster time-to-market. 
• Consistency and Accuracy: AI ensures consistent and accurate results, eliminating the variability and errors associated with manual testing, thereby ensuring higher quality outputs. 

With AI, we’re not just increasing speed; we’re enhancing the accuracy and reliability of our tests, ensuring higher quality software. – Meher Bondili, Quality Engineering Lead at Prolifics 

AI-Driven Test Data Management

Prolifics has developed a comprehensive test data management platform powered by AI. This platform streamlines the creation and maintenance of test data, ensuring high quality and relevance. Here’s how it works: 

• Pattern Recognition and Categorization: AI analyzes existing sample data to identify patterns and similarities. This forms the basis for generating new, relevant test data that adheres to recognized patterns. 
• Rule-Based Data Generation: For scenarios where sample data is insufficient, AI employs a rule-based engine to create data based on predefined rules, ensuring that generated data is both relevant and accurate. 
• Validation and Deduplication: The platform includes a robust validation process to ensure the accuracy of generated data, along with deduplication mechanisms to eliminate redundant data entries. 
• Continuous Learning and Adaptation: The AI continuously learns from new data and user feedback, refining its data generation processes to ensure ongoing relevance and accuracy. 

Our AI-powered test data platform is designed to handle the most complex data challenges, ensuring that our test data is always high quality and relevant. – Meher Bondili 

Ensuring Reliability and Fairness in AI Systems

Testing AI systems, particularly Generative AI, requires rigorous validation to ensure reliability, fairness, and security. Prolifics has developed a comprehensive framework for testing Generative AI systems, encompassing multiple dimensions of AI evaluation: 

Key Considerations for AI Testing 

• Improved Reliability: Ensuring that AI systems perform consistently as intended, reducing the risk of unexpected behaviors and enhancing trust in AI-driven processes. 
• Enhanced Fairness: Mitigating biases and promoting ethical AI usage to ensure fair and equitable outcomes, critical for maintaining regulatory compliance and public trust. 
• Increased Security: Identifying and addressing potential vulnerabilities to protect AI systems from cyber-attacks and other security threats. 
• Greater Transparency: Providing clear insights into AI decision-making processes to enhance transparency and accountability, building trust among stakeholders. 

Prolifics’ AI Testing Framework

In today’s AI-driven world, ensuring the reliability and fairness of our AI systems is paramount. Our comprehensive testing framework is designed to meet this need. – Kiran Bhashyam

Prolifics’ AI testing framework is inspired by established industry standards and incorporates several key components: 

• Trained Models: Leveraging advanced models trained on extensive datasets to provide accurate and reliable AI evaluations. 
• Communication Engine: Facilitating seamless interaction between the testing framework and the AI system, ensuring smooth data flow and real-time feedback. 
• Target Gen AI Platform: Integrating with the Generative AI platform to conduct comprehensive tests and evaluations. 
• Scoring Engine: Utilizing sophisticated algorithms to assess AI performance, providing actionable insights for continuous improvement. 

Generative AI is transforming software testing, offering significant advancements in efficiency, coverage, and accuracy. Prolifics’ innovative solutions in AI-driven test data management and comprehensive AI testing frameworks empower organizations to leverage these advancements while maintaining the highest standards of reliability, fairness, and security. 

By embracing Generative AI, organizations can significantly enhance their testing processes, ensuring faster, more accurate, and reliable software delivery. – Robert Gormley, Account Executive at Prolifics 

Are You Still Doing Dumb Testing?  

That is, testing without automation and AI through the overall testing lifecycle? Most testing service providers say they have automation and “AI tools,” but it’s only used in certain instances and not part of the entire project. Is this what you’re experiencing? Read this recent case study and get smart testing for your organization. 

Have any questions? Book a FREE discovery session today. Let’s take your quality to the next level.  

Meet Our Experts

Meher Bondili: Your QA champion, dedicated to ensuring your software runs flawlessly. Meher is a seasoned Quality Assurance Leader with a deep expertise in Continuous Quality Assurance (CQA). As the Head of Quality Engineering, Meher has a robust track record in automation framework design and development for mobile, web, and Windows applications, as well as web services. 

 

 

Robert Gormley: Your friendly neighborhood SME, here to ensure your experience with us is nothing short of amazing. Robert is a dynamic, result-oriented IT professional with a proven track record in leadership within multi-functional IT organizations. He excels in building teams from scratch, implementing best practices, and delivering innovative software solutions. With expertise in managing major technical projects, ensuring on-time and on-budget delivery, and driving continuous process improvement, Robert is dedicated to supporting and mentoring his team. 

 

Kiran Bhashyam: Your go-to for all things tech, ready to dive deep into your testing challenges and find innovative solutions. Kiran is a seasoned Solution Director responsible for Pre-Sales, Delivery, and establishing and maintaining a Test Center of Excellence. Kiran excels in planning, tracking, and delivering projects, programs, and accounts while ensuring the highest levels of customer satisfaction. Join us to hear Kiran’s expert insights on effective project delivery and the pivotal role of a Test Center of Excellence in quality assurance.