Traditional coding is the fundamental way developers write code manually, line by line, in a specific language or framework as per the requirements. Developers have used this process over the years, developing logic and algorithms, sometimes even reusing them by creating libraries and distributing them. Many snippets are even present in blogs and Q&A sites like stack overflow and a huge amount of knowledge is accumulated over the years. However, there are quite a few challenges associated with the traditional or manual approach.
Increased Productivity:
A lot of repetitive tasks and simple functionalities can be generated by AI, which leaves more time for the developer to work on innovation, design, and come up with solutions for complex logic.
Error reduction:
The LLMs are trained on a massive volume of code and a vast number of codes. Although the error rate might not become zero, it significantly reduces the errors in generated code.
Explore better options:
When AI generates code, it can be configured to provide a few alternatives. This helps in choosing a useful and better code.
Improved Maintainability:
AI tools can suggest best practices and help in keeping similar coding styles. It can also generate comments along with the code, which helps in documentation
Using AI for highly complex logic might not be feasible as it might lack understanding of specific requirements, especially in niche domains. Bias can come into play because the models are trained on pre-existing codes. Any bias must be adjusted accordingly.
AI is best used for tasks like common functionalities, repetitive codes, boilerplate codes, etc. More complex logic must be handled by developers. Different approaches to coding, strategic coding, prototype development, POCs must be taken up by developers.
