Artificial Intelligence as CEO of a Software Development Company: An Experiment that Revolutionizes Software Engineering

Artificial Intelligence as CEO of a Software Development Company: An Experiment that Revolutionizes Software Engineering

2023-09-26 08:00:00

A recent study by Brown University has attracted the attention of the technology community by naming ChatGPT 3.5, an artificial intelligence model, as the CEO of a virtual software development company called CHAT DEV. The experiment was divided into four key stages: design, coding, testing and documentation. With minimal human intervention, AI was able to develop a board game called Gomoku in a staggering seven minutes and at a cost of approximately one euro.

What is surprising is that 86.66% of the projects developed were executed without errors, highlighting the efficiency and precision of AI in this context.

Let’s look into more details about the importance of AI in software development and the underlying technologies that make this achievement possible.

Importance of AI in software development:

Artificial intelligence has been at the center of numerous discussions in the field of software development, but this experiment takes the debate to a new level. The ability of an AI to manage a software company, make logical decisions, assign tasks and correct errors, all with minimal human intervention, marks a milestone in the evolution of software engineering. It not only raises questions about efficiency and automation, but also opens new avenues for implementing AI in more complex development cycles.

The importance of this experiment is not limited to speed and cost; It also highlights how AI can be a valuable tool for identifying and fixing vulnerabilities in software, which is crucial for cybersecurity.

Technological context and description of ChatGPT model 3.5:

ChatGPT model 3.5 is an advanced version of the GPT (Generative Pre-trained Transformer) model series developed by OpenAI. This model is based on a transformer architecture with millions of trainable parameters, allowing it to perform natural language processing tasks with remarkable accuracy and efficiency. Unlike its predecessors, Model 3.5 features improvements in contextual understanding, text generation, and the ability to perform more complex tasks such as logical decision making and problem solving.

The underlying technologies include an efficient architecture for processing data sequences, supervised training on large labeled data sets, mechanized attention to focus on specific parts of the text, multilingual capability, and the possibility of integration through APIs and SDKs.

Experiment methodology:

The experiment was designed and executed by a team of researchers from Brown University. They created a virtual software development company called CHAT DEV, with clearly defined roles such as CEO, CTO, programmers and designers, assigned to AI instances. The software development process was divided into four main stages: design, coding, testing and documentation.

Division into atomic sub-tasks:

Each of the four main stages was decomposed into smaller, more manageable sub-tasks, allowing for effective collaboration, parallelization of work, dependency management, and a detailed focus on code review and quality.

Software development: case study – Creation of the Gomoku Board Game:

The first project was the development of a board game called Gomoku. The AI, in its role as CEO and CTO, established requirements and guidelines, and the Python programming language was chosen. Agile development practices were followed with short iterations and constant code reviews.

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Efficiency and costs:

The development time was astonishingly short, just seven minutes, and the cost was estimated at around one euro. This raises questions about how AI could change expectations around development timelines and costs in the software industry.

Vulnerabilities and security:

The AI ​​not only developed the software, but also identified and fixed potential vulnerabilities in the code before final delivery. This highlights the ability of AI to improve software security and reduce the resources required for security testing.

Limitations and challenges:

The study identified limitations such as errors and biases in linguistic models, as well as challenges related to scalability and real-world applicability. Code hallucinations were also addressed through automated peer reviews, automated testing, continuous feedback, and minimal human supervision.

Implications for software engineering:

AI could lead to greater efficiency, lower costs, and shorter development times in the field of software engineering. However, it could also change demand for certain traditional roles in this field, which could have significant career implications.

This experiment is an important milestone in the evolution of software engineering and raises important questions about how AI can transform this industry.


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