Extended Abstract, Intetics Inc.
4851 Tamiami Trail N, Suite 200, Naples, FL 34103-3098, United States
www.intetics.com
Keywords: Generative Artificial Intelligence, GenAI, Product Life Cycle, Software Development Life Cycle, Opportunities of AI, Limitations of AI, AI Projects, AI-driven Tools, AI Assistants
AI is gaining prominence across various industries, including IT, where its potential to automate development processes is full of promise. This White Paper explores the practical application of generative artificial intelligence in several Intetics’ projects. After testing AI capabilities and limitations, we determined that it allows for automating various tasks, from requirements analysis to system scaling.
Planning and Analysis
Generative AI can automate numerous text-based tasks. For example, AI assistants can search, retrieve, collect, and organize data while helping generate content. As our experiment showed, AI tools can reduce time by 30% to 40% on selected tasks during the planning phase.
Solution Design
AI-driven solutions are reshaping how teams approach this critical stage, demonstrating a 10%- 20% improvement in efficiency for architectural and technical design tasks.
Implementation (Code Writing)
Depending on the type of task and AI solution used to perform it, software engineers can increase productivity from 5% to 60%. In this block, you will discover how to choose between an AI-enabled tool and a standalone AI solution.
Testing
AI-based testing plays a significant role in automating multiple tasks and optimizing product quality. For instance, AI can elaborate data-driven test cases that are precise and tailored to the solution’s specific use scenarios, raising testing efficiency by up to 40%.
Deployment and Maintenance
With GenAI, deployment methodologies can be refined, resulting in an efficiency increase of up to 15%. For example, AI can speed up the configuration management process, ensuring that each deployment environment is set up properly.
Conclusion
Our experimentation with GenAI showcased its capacity to reduce feature release cycles and help deliver products of higher quality, boosting efficiency by up to 50%. As AI continues to evolve, its role in shaping the future of software development appears increasingly pivotal.
