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Published Oct 10, 2023 ⦁ 7 min read

The Future of Coding is Conversational

Introduction

The way developers write and debug code is undergoing a profound transformation through the rise of conversational coding. Powered by advances in natural language processing (NLP) and machine learning, AI assistants like GitHub Copilot and TabNine enable programmers to interact conversationally with their IDEs and interpret code in plain English. This conversational approach is accelerating software development, reducing bugs, and enhancing code discovery.

In this post, we'll explore the origins of conversational coding, its key benefits, use cases, leading companies driving innovation, and future trends that will shape how we code. Specifically, we'll cover:

  • Background on natural language programming and code completion/generation models
  • Increased productivity, reduced bugs, and enhanced discovery
  • Applications like code snippet completion, refactoring, and documentation
  • Pioneers like OpenAI, Anthropic, and TabNine
  • The evolution towards more advanced, specialized, and integrated AI coding assistants

Conversational interactions are profoundly changing programmers' workflows. By tapping into the knowledge contained in thousands of repositories and collaborating with an AI pair programmer, developers are creating better code, faster. Let's dive in!

Background on Conversational Coding

Conversational coding involves utilizing natural language conversations to write, modify, document, and analyze software. This allows programmers to use plain English instead of strict programming syntax. Enabled by advancements in NLP and text generation models like GPT-3, conversational coding represents a paradigm shift in software development.

Natural Language Programming

NLP techniques empower programs to parse natural language instructions into executable code. This includes:

  • Semantic parsing to translate text into formal representations
  • Leveraging domain schemas to map concepts and relationships
  • Handling ambiguity through clarification dialogs
  • Integrating world knowledge about coding conventions

Early NLP systems like SapFix, NL2Bash, and Pythonista demonstrated the possibilities of coding in natural language. However, they required substantial domain knowledge and could not handle complex instructions. Modern conversational coding leverages large-scale pretrained models like Codex to overcome these limitations.

Code Completion and Generation

In addition to interpreting natural language, AI models can also generate code. Encoder-decoder architectures like GPT-3 are trained on millions of repositories to predict probable "next tokens" during coding. This enables autocompletion, error correction, and entire code generation from comments or tests.

Key benefits include faster coding, fewer typos, and suggestions personalized for each developer. Leading tools include GitHub Copilot, TabNine, and AI Coding Assistant. However, generated code can sometimes be biased or logically unsound. Human oversight is still critical.

Key Benefits of Conversational Coding

Conversational coding offers several major benefits:

  • Increased developer productivity and faster coding
  • Reduced simple bugs through autocorrection
  • Better code discovery with AI-generated suggestions
  • Natural programming lowers barriers to entry
  • Continuous feedback loop improves developer skills

Faster Development Cycles

Conversational coding accelerates repetitive tasks, enables rapid prototyping, and reduces context switching:

  • Autocomplete speeds up mundane coding work
  • Conversational debugging quickly identifies issues
  • Accelerated prototypes help validate ideas faster
  • Streamlined collaboration when teams use same tools
  • Frees up developers to focus on complex problems

For example, companies using GitHub Copilot report up to 10x faster development cycles by offloading rote work to AI.

Reduced Bugs and Issues

Bugs are also reduced through immediate error correction, code reviews, and continuous analysis:

  • Autocorrect fixes typos and syntax errors immediately
  • Code generation avoids bugs caused by human oversight
  • Conversational debugging spots logical errors quickly
  • Code reviews catch errors missed by one programmer
  • Continuous code analysis during development

Tools like TabNine and Claude boast up to 19% fewer bugs through real-time code validation.

Enhanced Discovery

Conversational systems enhance discovery by exposing developers to new solutions:

  • AI suggestions draw on huge datasets of code
  • Conversational search makes finding APIs faster
  • Recommends alternative approaches developer may overlook
  • Open-ended questions stimulate creative thinking

For instance, Copilot suggests context-relevant code from OpenAI's training corpus of billions of lines.

Use Cases and Applications

Conversational coding is valuable throughout the development lifecycle:

  • Coding assistance during software development
  • Conversational code reviews and critiques
  • Debugging code through natural language questions
  • Documenting codebase through QA interactions
  • Testing edge cases through conversation
  • Code optimization by rewriting existing code
  • Bootstrapping new projects
  • Code migration between languages or frameworks

Completing Code Snippets

Conversational coding helps fill in code details quickly:

  • Define function signatures and class interfaces
  • Implement boilerplate code like loops
  • Fill in details for core logic and algorithms
  • Handle repetitive CRUD operations
  • Ensure proper error handling and edge cases

For example, a developer could describe desired logic in plain English, and Copilot suggests valid code snippets.

Fixing and Refactoring

Bugs and improvements can also be identified conversationally:

  • Identify bugs through conversational debugging
  • Rewrite existing code to improve efficiency
  • Optimize algorithms through comparative analysis
  • Upgrade deprecated libraries or APIs
  • Improve code consistency and readability

Claude can take high-level instructions like "make this code run faster" and perform optimizations.

Documenting and Training

Conversations enable collaborative documentation and training:

  • Annotate complex codebase through QA
  • Explain unfamiliar code through examples
  • Onboard new developers by answering questions
  • Surface tribal knowledge locked in engineers' heads
  • Create tutorials by demonstrating workflows

Tools like Blueprint leverage QA to build a documentation knowledge base.

Key Players in Conversational Coding

Several companies are pioneering conversational coding tools:

  • OpenAI: Creators of GPT-3 and Codex for GitHub Copilot
  • Anthropic: Built Constitutional AI and private Claude assistant
  • TabNine: Pioneers of AI code completion since 2016
  • Google: BigQuery ML for code generation and analysis
  • Amazon: CodeWhisperer AI pair programmer

OpenAI

OpenAI's contributions include:

  • Releasing GPT-3 model for advanced text generation
  • Powering GitHub Copilot built on Codex model
  • Researching improvements like chain of thought prompting
  • Prioritizing safety and oversight for AI development
  • Providing API access through licenses and pricing tiers

OpenAI aims to ensure AI systems benefit all of humanity.

Anthropic

Anthropic focuses on safe conversational AI:

  • Created Constitutional AI aligned with human values
  • Built Claude private coding assistant with NLP
  • Focuses on controllable conversational AI
  • Tailors NLP models specifically for software engineering
  • Backed by investors like Durable Capital Partners

Anthropic strives to make AI more helpful, harmless, and honest through research.

TabNine

TabNine pioneered AI code completion:

  • First to productize neural code completion in 2016
  • Integrates with popular IDEs like VSCode and JetBrains
  • Indexes millions of open source projects for predictions
  • Customizes models for specific codebases
  • Offers free and paid access suitable for all developers

TabNine aims to boost productivity for coders of all skill levels.

The Future of Conversational Coding

Upcoming trends in conversational coding include:

  • More advanced AI assistants that explain reasoning
  • Tighter integration into developer workflows
  • Shift towards natural language interfaces
  • Specialized AI models optimized for niche tasks
  • Continued focus on human guidance and oversight

Tools like Codex already support querying code decisions in plain English. Claude explains its reasoning and allows human feedback.

As assistants become integral to workflows, new hybrid human-AI developer roles may emerge. With an AI partner handling tedious work, human developers can focus on creative problem-solving.

Specialized niche models are emerging for domains like mobile apps, data engineering, DevOps, security, and more. For example, a cybersecurity coder could converse about threat models and defenses rather than syntax.

Human oversight remains critical to ensure logical soundness and prevent bias. AI is a powerful tool, but human developers still own decision-making.

Conclusion

The rise of conversational coding is transforming software development by enabling natural language interactions between programmers and AI assistants. Key benefits like accelerated workflows and reduced bugs are already being realized by teams using tools like GitHub Copilot. Conversational approaches are valuable throughout the dev lifecycle, from completing snippets to optimizing algorithms.

Pioneering companies like OpenAI and Anthropic are driving cutting-edge innovation and research into human-aligned conversational AI. As natural language programming continues improving, AI assistants will become integral partners in helping developers write better code quickly. To explore integrating a conversational coding tool like GitHub Copilot or TabNine into your workflows, visit DevHunt to compare options. The future of coding is indeed conversational.

Artificial IntelligenceDevelopmentProgramming

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