How AI Helped Numentica UI To Achieve 34% Efficiency in App Development
At Numentica UI, we believe in finding smarter ways to develop web and mobile applications. Recently, we started experimenting with Generative AI as part of our development workflow.
The results have been incredible, helping us achieve around 34% efficiency in delivering projects faster. This approach has been especially beneficial for projects with tight deadlines, enabling us to meet timelines without compromising on quality or accumulating technical debt.
Our Journey with Generative AI :
We began integrating AI tools to assist in software development and observed the following:
New Projects: Generative AI significantly reduced the time required to set up boilerplate code and project structures.
Legacy Apps: While AI helped, the efficiency gains were less noticeable compared to new projects.
Initially, not all team members were open to adopting these tools. Senior developers were hesitant, so we conducted experiments where teams were encouraged to use AI tools like Copilot and Cursor AI.
Areas Where Efficiency Improved :
1. Writing Unit Tests: We saw a 50% reduction in time spent creating unit tests. AI tools helped generate test cases quickly and accurately.
2. Code Refactoring: Tools like Copilot and Cursor AI made code refactoring much faster and easier, ensuring cleaner, more maintainable code.
3.Utility Functions: AI was excellent at generating utility functions. It provided quick solutions that met our requirements, saving significant time.
4.Code Completions: With well-crafted prompts, AI generated useful code samples. These samples could be easily modified to achieve the desired behavior.
5.Fixing SonarQube Issues: AI-assisted tools sped up the process of identifying and fixing code quality issues flagged by SonarQube.
6.Reduced Search Time: Developers spent less time searching for solutions on Stack Overflow. Instead, they found answers directly within the code editor, boosting productivity.
7.Error Detection and Debugging: AI tools improved error detection and suggested solutions during debugging, which reduced the time spent troubleshooting.
8.Documentation: Generative AI helped us create better internal documentation by summarizing code and providing clear explanations for functions and modules.
9. Dependency Identification: AI helped identify dependencies and suggested the most suitable packages for our specific scenarios. This reduced the time spent on research and ensured optimal package selection.
Challenges and Limitations :
While the benefits were substantial, we also faced challenges:
AI Adoption: Encouraging senior developers to use AI tools required additional effort and time.
Learning Curve: Team members needed to learn how to craft effective prompts to get the most out of the AI tools.
Context Awareness: AI occasionally lacked context, resulting in suggestions that were not always relevant or optimal.
Security Concerns: We remain cautious about using AI for handling sensitive project-related documents due to privacy concerns.
What We Avoid :
While we are thrilled with the efficiency AI brings to coding, we are cautious about using AI tools for project management. For now, we avoid sharing project-related documents with tools like ChatGPT due to privacy and security concerns.
Future Plans :
We plan to expand our use of Generative AI by:
Training Custom Models: Exploring the possibility of training AI models on our codebase to improve context awareness and suggestion relevance.
AI-Powered Monitoring: Using AI to monitor and analyze application performance in real-time, enabling proactive optimizations.
Cross-Team Collaboration: Encouraging cross-functional teams to adopt AI tools for tasks like UX design and QA testing.
Continuous Learning: Providing training sessions for team members to stay updated on the latest AI advancements and best practices.
Final Thoughts :
Generative AI has transformed how we approach web development, making us faster and more efficient. By leveraging AI for tasks like writing tests, refactoring code, and creating utility functions, we’ve successfully streamlined our workflow. While there are areas where AI is less effective, the overall impact has been overwhelmingly positive.
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