- AI coding assistants can help developers to fill in the continuation of the code, write unit tests, debug, and generate code according to the comments.
- Some developers are still sceptical about the effectiveness of AI coding tools, and the widespread use of large language models brings risks and hazards of disinformation dissemination, copyright infringement, academic misconduct and cheatin.
- AIGT detectors analyse code patterns, syntax, and other markup to identify AI-generated scriptsa and act as quality checks to ensure that AI-generated code meets quality standards.
The emergence of artificial intelligence as a tool for generating code is both a boon and a challenge. On the one hand, it has increased software productivity by offering developers the opportunity to automate repetitive tasks and generate code quickly. On the other hand, it raises concerns about code authenticity and quality.
AI-generated tools as good assistant
With the rapid popularisation of large language models such as ChatGPT, Claude, etc., the Large Language Model (LLM) has been widely used in work and daily life, bringing much convenience to people’s productive lives.
AI intelligent coding assistants have become essential tools for more and more developers, and Github Copilot, Amazon AI coding tools such as Github Copilot, Amazon CodeWhisperer and so on have appeared one after another, and the “Tongyi Spirit Code” released by Aliyun in last year’s Yunqi Conference is also expected.
Also read: GitHub’s latest AI tool can automatically fix code vulnerabilities
These AI coding tools are also known as the programmer’s “plug-in”, without the need for too complex operation, AI coding assistants can help developers to fill in the continuation of the code, write unit tests, debug, and generate code according to the comments.
AI-driven tools can significantly improve software productivity. They automatically generate sample code, perform routine tasks, and even suggest optimisations. However, integrating AI into the software development process requires balance. AI software productivity gains must not come at the expense of code quality or authenticity.
AIGT detectors as solution
Some developers are still sceptical about the effectiveness of AI coding tools, and the widespread use of large language models brings risks and hazards of abuse. Disinformation dissemination, copyright infringement, academic misconduct and cheating, and phishing attacks have already jeopardised normal human society.
Some individual companies require that code that can be written by AI is not allowed to be handwritten by programmers, and if it is to be handwritten, it must be annotated to explain the reason why the AI can’t write this code.
Also read: Ethereum’s Vitalik Buterin excited by AI for code testing
If it is possible to detect AI-generated code, I believe the answer is yes. However, detection methods are still under continuous improvement, and the efficiency of detection is yet to be examined in the long run.
Therefore, AIGT (Artificial Intelligence Generated Text) detection is an effective solution. An AI software code detector is a tool designed to distinguish between code written by humans and code written by AI. Such tools are becoming increasingly important as more and more developers utilise AI to speed up the coding process.
These detectors analyse code patterns, syntax, and other markup to identify AI-generated scripts. At the same time, these tools can also act as quality checks to ensure that AI-generated code meets quality standards.
However, distinguishing AI-generated code from code written by humans is not an easy task. These tools use advanced algorithms to scrutinise code structure and logical flow, looking for common patterns in AI-generated code, such as repetitive syntax or overly generic comments that may not be as nuanced as those written by humans.
The analysis of AI-generated code involves a variety of complex techniques, including statistical analyses that identify code pattern anomalies, machine learning models trained to recognise features of AI-generated scripts, and syntax evaluation algorithms.
