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The Future of Testing – A Roundtable Discussion on AI and Automation

Recent advances in artificial intelligence (AI), particularly in generative AI with the release of large language models (LLM) such as OpenAIu2019s GPT 3.5 and 4.0, Googleu2019s Gemini, and Metau2019s Llama in 2023, have had a profound effect on business procedures and practices in several industries, including software development, operation, and quality engineering (QE).

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The Future of Testing – A Roundtable Discussion on AI and Automation

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  1. The Future of Testing – A Roundtable Discussion on AI and Automation 

  2. The Future of Testing – A Roundtable Discussion on AI and Automation • 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).  • 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.  • Key Findings from the Report • Here are some key findings from the report.  • 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.  • 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. 

  3. The Future of Testing – A Roundtable Discussion on AI and Automation • 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. • AI adoption remains low overall, with notably lower rates observed among more experienced professionals, suggesting a reluctance to embrace this technology within this group. • 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. • For more insights, download the report. • Interesting right?  • Want to know more on this topic: https://bit.ly/3VceZIW

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