AI coding assistants have transformed software development. Starting with GitHub Copilot and later adopting Codeium, we’ve explored AI pair programming in a .NET environment. Here’s what we’ve learned about adoption, best practices, and productivity.
Developer Adoption: The Key Success Factor
The value of AI tools hinges on engagement. Teams fully integrating AI assistants into daily workflows saw significant productivity boosts, while occasional users had mixed results. Success requires consistent use.
How We Use AI Assistance
We leverage AI for repetitive, well-defined tasks like:
- Generating unit tests
- Writing boilerplate code
- Implementing API endpoints
- Transforming data models
- Handling CRUD operations
These areas maximize AI’s efficiency.
Strengths and Limitations
AI excels at structured tasks but isn’t a developer replacement. It requires human oversight for complex logic and decisions. Used as a productivity enhancer, AI delivers optimal results.
Exploring Windsurf by Codeium
Some developers tested Windsurf, Codeium’s alternative IDE, offering AI-powered suggestions, a modern interface, and streamlined environment. While Visual Studio remains primary, Windsurf complements it effectively.
AI at DATASCOPE
We save time automating repetitive coding, accelerating test writing, and reducing boilerplate work. Balancing AI with human expertise is key.
AI tools are advancing rapidly, promising smarter integrations. At DATASCOPE, we use AI strategically to boost productivity while maintaining quality, ensuring human oversight where it counts.