AI Coding Assistants: Are They Replacing Coders or Developers?

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The software development landscape is changing very fast and soon coders will vanish from the development landscape with the evolution in AI space with introduction of new AI coding tools such as Replit, CodiumAI, GitHub Copilot, Amazon code whisper etc. Does that hold true?

AI powered coding assistants are more sophisticated, advanced, faster, capable of generating boilerplate code which is complex to write, provide guidance in API usage and translate natural language requirements into basic code structures, compilation based on common language patterns and so on. 

In this article we will learn about AI coding assistants,  capabilities and limitations of AI coding assistants and can they replace coders or developers? 

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What are AI Coding Assistants 

Will ‘AI’ take over ‘Coding’ in the next few years? In 2023, OpenAI ChatGPT claimed to pass a high-level developer proficiency test. GitHub reported (46%) of code across all programming languages is built using the company’s AI powered development tool ‘Copilot’. 45% of expert programmers are outperformed by ‘DeepMind Alpha code’. These are some publically available statistics on how AI had taken over development work from actual coders. 

Human Elements that AI can’t Replace 

From a holistic perspective software development is not just about writing correct code. It is also about solving human problems in complex environments having a range of constraints and where humans definitely do better than AI. 

Problem Understanding

Developers not just merely translate specifications into code lines but decode the problem statements and actual end user requirements which is often very different from which is the explicit task. This requires empathy and contextual understanding which AI does not possess. For example, a simple client request could be a software to analyze or formulate contracts but actual requirement need to address an array of requirements such as :

  • Accessibility standards
  • Regulatory requirements
  • Integration requirements 
  • Security requirements
  • Performance under various conditions etc

And all this takes place before writing a single line of code.

Design Thinking and System Architecture

How to build and what to build is the most challenging part of development. Interaction of individual components and good architecture will balance:

  • Future flexibility and addressing immediate requirements
  • Business goals and technical limitations
  • Maintainability and performance 
  • Quality and low costs 

AI coders may suggest how to implement a cache layer but it can’t decide whether caching is an ideal solution for specific performance requirements in lieu of proposed system architecture. 

Complex Issues Debugging

Finding syntax errors is not just debugging. It involves:

  • Understanding working of interconnected systems and its issues
  • Failure modes and associated patterns
  • Domain knowledge application to identify root cause
  • Historical context of database in-depth understanding 

Ethical Judgement and Responsibility 

How will data privacy be ensured? How to determine AI algorithm outcome is fair and involves no bias. If the system fails, who will own the responsibility, all these questions require human judgement and values which AI lacks fundamentally. 

AI is a Partnership not Replacement 

AI will bring a new paradigm where humans and AI will work in collaboration in the area of software development by augmenting the coding work but will not replace the coders or developers for sure. 

  • Productivity Enhancements – Developers can focus more on creative problem solution aspects and leave coding to AI assistants 
  • Lower Entry Barriers – Coding assistants will help freshers or new developers in learning programming concepts quickly with interactive explanations and suggestions
  • Elevated Role – High level design architecture, business alignment will be focus and routine job of coding will be delegated to AI assistants 
  • Skill set Evolvement – This AI based coding will help developers to enhance their skills in the area of review of AI generated code, data flow visualizations, anticipate failure modes, create flexible architecture etc. 

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