AI Code Assistant

Overview

Building an AI code assistant requires more than just a large language model. It needs context awareness, deep understanding of syntax, and the ability to verify its own outputs.

System Architecture

The system comprises four main modules: Code Analysis, Code Generation, Documentation, and Test Generation.

Code Analyzer

This module parses the Abstract Syntax Tree (AST) to understand the structure of the code before performing semantic analysis.

class CodeAnalyzer(dspy.Module):
    def forward(self, code, file_path):
        language = self._detect_language(file_path)
        parsed = self._parse_code(code, language)
        analysis = self.analyze(code=code[:1000], language=language.value)
        return CodeAnalysisResult(language=language, ast_tree=parsed)

Code Generator

The generator takes a natural language prompt and existing context to produce code, which is then refined and validated.

class CodeGenerator(dspy.Module):
    def forward(self, prompt, context, style):
        # Generate initial draft
        gen = self.generate(prompt=prompt, context=context)
        # Self-correction loop
        refined = self.refine(code=gen.code, errors=self._validate(gen.code))
        return refined

Automated Testing

A unique feature of this assistant is its ability to generate unit tests for the code it produces, ensuring correctness.

class TestGenerator(dspy.Module):
    def forward(self, code, language="python"):
        # Generate pytest/unittest code
        tests = self.generate(code=code, framework="pytest")
        return tests