Neural networks are rapidly transforming the landscape of software development. According to analysts at true.code, as of May 2025, approximately 35% of programming tasks are performed using artificial intelligence (AI). A study published by Gazeta.Ru, highlights where AI excels, how it saves time, and why developers remain indispensable. We explore how neural networks are reshaping programming, which tasks they handle, and what this means for the industry.

Neural Networks: Assistants or Replacements?

The true.code report indicates that neural networks are widely used in programming, particularly for routine operations and boilerplate code. Their adoption reduces task completion time and boosts efficiency, but complex processes still require human expertise.

Key Applications of AI:

  • Boilerplate Code Generation: Neural networks handle 60% of tasks, producing template code (e.g., classes, functions, HTML/CSS layouts). Tools like GitHub Copilot and Tabnine generate ready-to-use snippets for Python, JavaScript, and other languages.
  • SQL Query Creation: AI automates 50% of tasks, generating complex database queries (SELECT, JOIN, aggregates) based on text descriptions.
  • Bug Detection and Fixes: Neural networks identify 40% of bugs, suggesting fixes for syntax errors, logical issues, or vulnerabilities (e.g., through static code analysis).
  • Technical Documentation: AI automates 40% of tasks, creating README files, code comments, and API documentation using models like Claude or ChatGPT.

AI Weaknesses:

  • Legacy Code Support: Only 15% of tasks are handled by neural networks due to the complexity of outdated systems (e.g., COBOL or legacy PHP), which demand deep contextual understanding.
  • Solution Evaluation: AI is used in just 10% of cases, as choosing architectures, optimizing performance, and making strategic decisions require human experience.

Expert Quote:
“Neural networks handle up to 60% of routine tasks, but key decisions and complex systems cannot be fully delegated to AI. They won’t design a module’s architecture without an experienced programmer; they only provide a starting point,” said Artem Shelengovsky, CEO of true.code.

Time Savings: The Numbers Speak for Themselves

True.code analysts compared time spent on standard tasks with and without AI, yielding impressive results:

  • Unit Test Generation:
    • Manual: 2 hours.
    • With AI (e.g., CodiumAI): 30 minutes.
    • Savings: 75%.
  • Bug Fixing:
    • Manual: 1.5 hours (log analysis, debugging).
    • With AI (Copilot, DeepCode): 30 minutes.
    • Savings: 66%.
  • CRUD API Creation:
    • Manual: 3 hours (writing endpoints, models, validation).
    • With AI (ChatGPT, GitHub Copilot): 1 hour.
    • Savings: 66%.

Example: Building a REST API on Flask with CRUD operations (Create, Read, Update, Delete) manually requires 100–150 lines of code, routing setup, and error handling. A neural network generates a basic template in 5–10 minutes, leaving developers to refine project-specific details.

How Neural Networks Are Changing Development

Neural networks serve as assistive tools, accelerating tasks and freeing up time for strategic work. Here’s how they impact the process:

  • Routine Tasks: AI generates code for common operations (JSON parsing, regex, UI components), reducing the workload for junior developers.
  • Learning and Onboarding: Neural networks aid beginners by providing code examples and explanations, shortening onboarding time.
  • Process Optimization: Automated testing (unit tests, end-to-end tests) and documentation allow teams to focus on business logic.
  • Faster Debugging: AI-powered tools like DeepCode or SonarQube detect vulnerabilities (e.g., SQL injections, memory leaks) quicker than traditional static analyzers.

Limitations:

  • Context: Neural networks struggle with unique project requirements without clear task definitions.
  • Architecture: Designing scalable systems (microservices, monoliths) demands expertise AI lacks.
  • Verification: Generated code often contains errors (e.g., outdated libraries or suboptimal algorithms), requiring human review.

What’s Next?

True.code analysts predict:

  • By 2027, AI will handle 50% of development tasks, especially in testing and documentation.
  • New tools like Grok Code (xAI) or DeepMind AlphaCode 2 will enhance complex code generation.
  • The focus will shift to training developers to work with AI, including crafting precise prompts (prompt engineering) and verifying outputs.
  • Legacy system automation will rise from 15% to 25% with specialized models.

Recommendations for Developers:

  • Master AI tools like GitHub Copilot, Tabnine, and CodiumAI.
  • Learn to formulate clear prompts for neural networks (prompt engineering).
  • Focus on architecture and business logic, where AI remains weak.

Conclusion

Neural networks already handle 35% of programming tasks, automating up to 60% of routine work—from code generation to test creation. They save up to 75% of time on standard tasks, but complex systems and decision-making remain human domains. The true.code study shows that AI is a powerful assistant, speeding up development and freeing time for strategic tasks. Are you using neural networks in your coding, and how do they help you? Share in the comments!