The faster developers can create, build, test, and launch applications, the better it is for the growth of their careers. However, many developers find it challenging to complete projects on time while maintaining the highest levels of excellence and accuracy in coding. This is why many developers today prefer to work with AI-assisted programming.
AI-driven tools like GitHub Copilot and Tabnine can help developers write highly accurate code in a short amount of time. Now as a developer, you may wonder: how much quicker results can developers accomplish by using AI-assisted programming and how much of a venture can it take care of on its own? So, let’s find out the answers to these questions.
Speeding up the development process
One of the best things about AI-assisted programming is that it can significantly speed up the development process. AI tools are designed to streamline several aspects of coding, such as automating tedious tasks, eliminating bugs, and optimizing code. By doing so, they allow developers to focus on more intricate and creative aspects of the project.
How much of the project can AI cover?
AI tools can speed up the coding process meaningfully, but they cannot yet replace developers. Here’s how AI can contribute to the diverse stages of a project.
Code generation (30-50%)
AI can generate large parts of a program, especially when it comes to repetitive code or functions that follow set patterns. For instance, AI can handle tasks like setting up databases, creating user interfaces, and executing basic algorithms. However, they cannot manage more creative, problem-solving aspects of a project. On average, AI-assisted programming tools can handle coding about 30-50% of a project, depending on its complexity.
Project architecture and complex features (10-20%)
AI tools have limited capability in defining the project architecture, building complex features, or designing the overall system. Such tasks require domain knowledge, deep understanding, and creative problem-solving skills that AI tools do not yet have. In such cases, developers must make complex decisions, research, and integrate various components. Even though AI tools can help with some basic functionalities, they cannot replace human expertise in creating innovative or specialized features. AI contributes only about 10-20% of the work for such tasks.
Testing and debugging (20-40%)
Testing and debugging applications are vital steps in their production process. AI-driven tools can help with debugging. Such tools can detect common bugs and vulnerabilities in the code. These tools offer exceptional efficiency in removing lower-level issues, but they cannot resolve deep architectural flaws or logical errors. AI tools can also take care of testing but with limited proficiency. Thus, AI can contribute only about 20-40% of testing and debugging.
Hence, it can be said that while AI tools can speed up development, they still require developers to put in consistent manual effort to produce desirable results. AI can improve their productivity and make it easier for them to launch applications with faster results.