Is Chat-GPT4 replacing Software Engineers

This week has been one of the most eventful weeks AI has ever seen. We have seen new releases from Stanford (Alpaca 7B), Google (Med-PaLM 2, PaLM API, MakerSuite), GPT4 (OpenAI), Meta (PyTorch 2.0) and Microsoft (AI Copilot for the 365 suite) to name a few. As a software developer, I am curious to see how good AI is to do computer programming. For this purpose, let us pick the questions from the MAANG interview pool of LeetCode, a go-to place for Software Engineers for preparing tech interviews. In this article, we analyze the performance of GPT-4 in solving questions related to five different algorithms: Binary Tree, BFS, DFS, Recursion, and Sliding Window. We aim to assess the effectiveness of GPT-4 in providing solutions and its potential impact on the future of software engineering.

GPT-4 Performance Breakdown by Algorithm:

Binary Tree:

Depth First Search

Breadth First Search

Sliding Window:

Recursion:

Analysis and Insights:

Based on the results, GPT-4 demonstrates a remarkable ability to solve various types of LeetCode questions. It managed to provide accurate and efficient solutions for several questions across different difficulty levels and algorithms. However, the model encountered difficulties in solving some questions, requiring multiple attempts or even failing altogether.

The performance of GPT-4 in solving these questions indicates that while the model is highly capable, it is not yet ready to replace software engineers completely. Human engineers still possess the edge in understanding complex problems, optimizing solutions, and handling edge cases that the model might not be able to handle effectively.

The Future of Software Engineering with GPT-4:

While GPT-4's performance in solving LeetCode questions is impressive, it is essential to recognize the importance of human software engineers in the development process. The limitations of GPT-4 highlight the need for skilled professionals who can not only write efficient code but also adapt to complex problems and develop optimized solutions.

Collaboration between AI and Human Engineers

The future of software engineering is not about replacing humans but rather finding a synergy between AI and human engineers. AI models like GPT-4 can assist developers in generating code snippets, debugging, and optimizing algorithms. On the other hand, human engineers can provide the necessary insight, creativity, and problem-solving skills to address challenges that AI may not be equipped to handle.

Continuous AI Improvement

As AI models like GPT-4 continue to evolve, they will become more adept at understanding and solving complex programming challenges. However, it is unlikely that they will entirely replace software engineers. Instead, these models will serve as powerful tools that augment human expertise, leading to more efficient and innovative solutions.

Skill Development for Software Engineers

To stay relevant in the age of AI, software engineers need to focus on honing their skills and embracing the opportunities presented by AI models like GPT-4. This includes understanding the strengths and limitations of AI, learning to collaborate effectively with AI models, and developing a deep understanding of the algorithms and data structures that underpin modern software development.

Conclusion

GPT-4's performance in solving LeetCode questions offers a glimpse into the future of software engineering. While AI models like GPT-4 have made significant strides, they have not yet reached the point where they can replace human software engineers. Instead, the future of software development will likely involve a symbiotic relationship between AI models and human engineers, leveraging the strengths of both to produce more efficient, robust, and innovative software solutions. By embracing this collaboration, software engineers can ensure their skills remain relevant and valuable in an ever-evolving technological landscape.