In the rapidly evolving field of artificial intelligence, developers are constantly exploring the capabilities of AI models to enhance various aspects of their work. In this article, we will delve into the world of AI-powered coding. The article explores the abilities of different AI models, including ChatGPT 3.5, ChatGPT using GPT-4, Bing Chat, Google’s Bard, and Open Assistant, to perform tasks such as code generation, code completion, bug detection, API documentation generation, and code refactoring. In this article, we’ll take a closer look at the experiments and assess the performance of these AI models in each task.
These 5 Developer tasks are:
Code Generation
Artificial Intelligence models are put to the test to generate code. The task is to create a Python script to parse a JSON file and extract course titles. ChatGPT 3.5 and ChatGPT using GPT-4 handle this task adeptly. They successfully generate Python code that accomplishes the task correctly and efficiently. This demonstrates the AI models’ proficiency in understanding the task and translating it into functional code.
On the other hand, Open Assistant, an open-source alternative to ChatGPT, struggles with the task. It misunderstands the requirements, attempting to scrape data from the Pluralsight website instead of parsing the provided JSON file. This highlights a limitation in Open Assistant’s comprehension and adaptability compared to its commercial counterparts.
Bing Chat, which is based on GPT, also produces a functional script but exhibits an issue when copying and pasting the generated code, including unnecessary content. This minor inconvenience could potentially impact productivity.
Surprisingly, Google’s Bard, initially launched without coding capabilities, performs well in generating code for the task. However, it introduces an unexpected element by mentioning “ionic” in the course titles, even though it’s not present in the provided JSON data. This suggests a degree of creativity or misunderstanding on Bard’s part.
In the code generation task, ChatGPT 3.5 and ChatGPT using GPT-4 emerge as the top performers, demonstrating their ability to generate accurate and relevant code. Bard follows closely behind, while Bing Chat exhibits minor inconveniences in the copy-paste process. Open Assistant, unfortunately, falls short due to a misunderstanding of the task.
Code Completion
Moving on to the code completion task, AI models are provided with a partially completed Python script and asked to fill in the missing part. ChatGPT 3.5, ChatGPT using GPT-4, and Bard perform reasonably well in completing the code. They understand the context and generate code that fits seamlessly into the existing script.
Open Assistant, after some adjustments to the prompt, produces a satisfactory code completion. However, it does not offer the same level of detail and organization seen in the ChatGPT models and Bard. Bing Chat provides an incomplete result and appears to struggle with this particular task.
In the code completion task, ChatGPT 3.5, ChatGPT using GPT-4, and Bard shine once again, delivering code that integrates seamlessly with the existing script. Open Assistant makes progress with adjusted prompts, but its output lacks the depth and clarity seen in other models. Bing Chat faces challenges in providing a complete solution.
Bug Detection
Bug detection presents an intriguing challenge for AI models. The task is to locate logic errors in a given Python script. This task requires the AI to identify issues in the code that prevent it from functioning correctly. Here, ChatGPT 3.5 fails to pinpoint the logic error accurately. Its proposed corrected code does not address the issue, highlighting limitations in its debugging capabilities.
ChatGPT using GPT-4 faces a similar issue, failing to identify the bug accurately. Open Assistant, Bing Chat, and Bard also struggle with this task, indicating that identifying logic errors within code remains a significant challenge for current AI models.
In the bug detection task, all AI models encounter difficulties in pinpointing and correcting logic errors within the provided code. This suggests that AI-powered bug detection tools are still a work in progress and may require further development to meet the accuracy levels expected by developers.
API Documentation Generation
API documentation generation is a critical aspect of software development, and the task is to create documentation for a Python script. ChatGPT 3.5 impresses with its comprehensive documentation generation. It generates detailed documentation, including function descriptions, syntax, parameters, returns, and usage examples. This output is a potential game-changer, as it alleviates the burden of writing documentation, a task often disliked by developers.
Open Assistant’s attempt at generating API documentation falls short. It fails to understand the task, resulting in a vague and unrelated response. Bing Chat provides inline documentation, which can be useful but does not align with the presenter’s request for API documentation.
Bard’s documentation generation follows a class-based approach, providing a unique perspective. However, it lacks proper formatting and organization, making it less user-friendly than ChatGPT’s output.
In the API documentation generation task, ChatGPT 3.5 stands out as the leader, offering a detailed and well-structured documentation template. Bard provides an alternative approach but falls short in terms of formatting. Open Assistant and Bing Chat struggle to grasp the task’s essence, resulting in less relevant outputs.
Code Refactoring
The final task explores code refactoring, a process of restructuring code to enhance testability. ChatGPT 3.5 suggests refactoring the provided code by creating functions for loading JSON and adding input validation. It provides a clear and structured explanation of the changes, demonstrating the potential of AI in assisting with code improvement.
However, when applying ChatGPT’s suggested changes, the presenter encounters issues, emphasizing that the AI’s suggestions do not guarantee a flawless outcome. The presenter also points out that AI models tend to operate based on the assumption that the provided code is correct, which can lead to unexpected results.
Open Assistant, in this instance, fails to provide meaningful refactoring suggestions, stating that there are no obvious logical errors. Bing Chat and Bard offer their refactoring recommendations, with Bing Chat suggesting a more extended code with a try-except block for error handling, and Bard opting for a class-based approach. These alternative approaches showcase the diversity of AI-generated suggestions.
In the code refactoring task, ChatGPT 3.5 shines in offering well-explained refactoring suggestions, despite potential issues when implementing them. Open Assistant struggles to identify logical errors, while Bing Chat and Bard provide alternative approaches to refactoring.
Developer Tasks Done in Seconds
This article provides a fascinating glimpse into the capabilities of various AI models in assisting developers with coding tasks. ChatGPT 3.5 and ChatGPT using GPT-4 demonstrate strong performance in code generation, code completion, and API documentation generation, showcasing their potential to streamline development workflows.
However, it is essential to recognize that these AI models are not without limitations. They may struggle with bug detection and may not always provide flawless code when applying suggested changes. Open Assistant, while an open-source alternative, lags behind in understanding and delivering accurate results for some tasks.
Bing Chat and Bard offer alternative perspectives and approaches to tasks, illustrating the diversity of AI-generated solutions. Still, they may require further refinement to match the capabilities of the ChatGPT models.
In conclusion, AI models are making significant strides in assisting developers with various coding tasks. While they excel in some areas, they are still evolving and may benefit from fine-tuning to enhance their performance further. Developers should consider leveraging these AI tools judiciously, understanding their strengths and weaknesses, to improve productivity and code quality in their projects. As AI continues to advance, it holds the promise of reshaping the way developers work, making coding tasks more efficient and accessible than ever before.
