Artificial intelligence (AI) code generation services are becoming increasingly popular in the programming world.
Today in the #rnc_experts section our iOS developer Serhii will tell a few words about this technology and its use by our team.
Code generation services based on AI use advanced algorithms and models to automate the creation of source code. These services leverage machine learning techniques, particularly natural language processing (NLP) and neural networks.

Here’s a simplified breakdown of how these services work:

1. Input Processing:
◾ Users provide high-level input specifications or requirements in natural language.
◾ NLP models extract the semantic meaning from these requirements.

2. Code Generation:
◾ The system uses the extracted information to generate code snippets or complete programs.
◾ Neural networks, especially deep learning models, learn patterns and structures from existing codebases to replicate common programming constructs and best practices.
◾ Two types of code generation include creating code based on high-level specifications and providing hints while writing code.

3. Strategies for Code Generation:
◾ Template-based generation fills in predefined code templates with relevant details based on input specifications.
◾ Advanced techniques, like sequence-to-sequence models, can generate code similar to language translation.

4. Code Quality Assurance:
◾ Generated code undergoes checks, including syntax validation and sometimes basic semantic analysis.
◾ Consideration may be given to performance, security, and coding standards.

5. Example of Work:
◾ Input Specification (Natural Language): “Create a function that calculates the factorial of a given number.”
◾ Semantic Analysis: Extracts key information like the task (create a function), operation (calculate factorial), and subject (given number).
◾ Code Generation: Creates a code snippet that fulfills the specified functionality.
◾ Semantic Validation: Ensures the generated code aligns with the intended meaning.

It’s essential to note that the effectiveness of AI tools depends on factors like the quality of training data, sophistication of models, and continuous refinement through feedback loops. As AI advances, these services are expected to become more proficient in producing high-quality, human-readable code for diverse programming languages and domains.

Members of our #RNC_team are increasingly using AI technologies in their daily work, and are also studying the implementation of AI tools on a scientific basis. We will tell you about the interesting aspects of these developments in the following publications.