{"id":21783,"date":"2024-05-23T18:02:56","date_gmt":"2024-05-23T12:32:56","guid":{"rendered":"https:\/\/www.cigniti.com\/blog\/?p=21783"},"modified":"2024-05-23T18:02:56","modified_gmt":"2024-05-23T12:32:56","slug":"future-testing-roundtable-automation-ai-ml","status":"publish","type":"post","link":"https:\/\/www.cigniti.com\/blog\/future-testing-roundtable-automation-ai-ml\/","title":{"rendered":"The Future of Testing – A Roundtable Discussion on AI and Automation"},"content":{"rendered":"

Recent advances in artificial intelligence (AI), particularly in generative AI with the release of large language models (LLM) such as OpenAI’s GPT 3.5 and 4.0, Google’s Gemini, and Meta’s Llama in 2023, have had a profound effect on business procedures and practices in several industries, including software development, operation, and quality engineering (QE).<\/p>\n

For example, QE engineers utilize ChatGPT and other AI-augmented tools to generate test plans, use cases, scripts, and data for manual and automated testing. At the same time, software programmers employ generative AI tools like GitHub Copilot to generate code. When integrated into QA processes, these technologies can significantly increase productivity, improve the caliber of work, and quicken software delivery.<\/p>\n

These advancements have forced people and organizations in the QE community to change and take advantage of fresh opportunities to improve their procedures, methods, instruments, and approaches by incorporating AI-powered technology. People and businesses must stay current on industry advancements and trends to keep up with the quickly evolving AI-driven world. Moreover, being flexible and taking the initiative to welcome and promote change is critical.<\/p>\n

Every year, Katalon conducts a quality study and releases a report with significant findings, trends, and recommendations to assist the QE community. The study is conducted in collaboration with Deloitte, Cigniti, Quality Kiosk, and XRay. The information for this year’s edition was gathered through an in-depth survey and additional interviews between December 2023 and March 2024.<\/p>\n

More than 3,800 people who work actively in quality engineering in various industries, such as software and IT, banking, financial services, insurance, healthcare, and life sciences, participated in the poll and provided their perspectives. Senior executives, consultants, software engineers, QA engineers, and project managers were among the participants.<\/p>\n

After the poll, 14 industry experts and leaders were comprehensively interviewed for the second phase. These people were chosen for their extensive backgrounds and rich experiences, which provided priceless insights into software quality engineering. These two research stages offer a thorough picture of software quality today, highlighting the difficulties, breakthroughs, and methodologies reshaping the industry.<\/p>\n

Trends in the development of Test Automation paradigms across audiences and technical landscapes are captured in the State of Software Quality Report. This wide range of international research participants serves as an essential testbed for extracting useful leading indicators in AI-driven automation, consistent trailing parameters in manual testing, and identification of the mainstream set of widely acknowledged hybrid automation techniques.<\/p>\n

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Register now<\/a> and secure your spot for a high-level discussion on AI and Automation and the future of testing based on the insights from the State of Software Quality Report, featuring industry leaders from Deloitte, Cigniti, QualityKiosk, and Xray.<\/p>\n

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Key Findings from the Report<\/h2>\n

Here are some key findings from the report.<\/p>\n

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  1. Test Automation is the foremost QE practice, with its adoption on the rise. It is deemed the most effective, yet lesser-used practices like behavior-driven and test-driven development are also highly valued for their efficacy.<\/li>\n
  2. Test Automation brings high returns on investment (ROI), and teams with more experience gain higher ROI. Other benefits include higher software quality, the ability to test more often and more thoroughly, and improved test coverage.<\/li>\n
  3. The top challenges in achieving quality goals are the lack of time and skilled resources. Skill shortages and frequent requirement changes remain the key obstacles in test automation adoption.<\/li>\n
  4. AI adoption remains low overall, with notably lower rates observed among more experienced professionals, suggesting a reluctance to embrace this technology within this group.<\/li>\n
  5. AI is used in various QE tasks, focusing on generative AI for generating test cases, data, and scripts. Despite low AI adoption rates, the use of AI for test cases, scripts, and data generation is steadily increasing.<\/li>\n<\/ol>\n

    For more insights, download the report<\/a>.<\/p>\n

    Recommendations from the experts<\/h2>\n

    Based on the findings, here are a few recommendations from the experts.<\/p>\n

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    1. \n
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      1. Implement test automation and improve its coverage.<\/li>\n
      2. Embrace AI for QE processes. Encourage a culture of experimentation.<\/li>\n
      3. Prioritize training, focus on crucial testing areas, and choose the right tools to improve ROI and benefits of test automation.<\/li>\n
      4. Use a blend of practices and techniques for QE. Implement a systematic QE approach encompassing the entire software development scope, not limited to just QA or testing.<\/li>\n
      5. Adopt low-code and no-code test automation solutions. Focus on test prioritization and selection.<\/li>\n<\/ol>\n<\/li>\n<\/ol>\n

        Conclusion<\/h2>\n

        Leveraging insights from the State of Software Quality Report<\/a>, we’ve assembled industry leaders from Deloitte, Cigniti, QualityKiosk, and Xray for a high-level discussion on AI and Automation and the future of testing.<\/p>\n

        Join us in unveiling cutting-edge QE strategies to attain high software quality.<\/p>\n

        This exclusive roundtable will explore:<\/p>\n