Large Language Models (LLMs) are versatile. They also provide a significant increase in efficiency in software development. Here are five use cases for LLMs.
1. Writing small software blocks
Often a software developer has to write short blocks of code that are not particularly complex, but necessary to guarantee the functionality of the software. However, writing these code blocks manually can take a lot of time. Fortunately, there is a remedy in the form of Large Language Models (LLMs) such as ChatGPT. These models can use their ability to quickly and efficiently create code that is often even of good quality, with sensibly named variables and thorough comments.
However, depending on the prompt, it is still sometimes necessary to manually integrate the generated code into the existing code. This is because Large Language Models are not always able to fully understand the context and specific requirements of a project.
2. Code documentation
Everyone probably knows it: You don’t have the time or the desire to document the code you just wrote. This can lead to problems, especially when other developers try to understand and use the code later. Fortunately, ChatGPT can quickly remedy this situation as well. The model can identify gaps in code documentation and add missing documentation.
While it can be tempting to forgo documenting code, one should always be aware that well-documented code can save time and resources in the long run. Clear and complete documentation makes it easier for other developers to understand and use the code, which in turn can help find and fix bugs faster and speed up the development process overall.
3. Understanding undocumented legacy code
Understanding legacy code, especially undocumented code, can be extremely time-consuming. Fortunately, Large Language Models (LLMs) can help by often being able to reliably explain the code. In addition, they can also identify and suggest bugs or potential code optimizations to improve software performance.
Even in cases where the code is insufficiently documented or the original developer is no longer available, LLMs can help quickly provide clarity and resolve issues.
4. Code-Reviews
By using LLMs such as ChatGPT, potential bugs and vulnerabilities can be identified before the code review. The LLM can analyze the code and point out potential vulnerabilities. In many cases, it can even suggest a quick fix for the problem, which can save developers a lot of time. This type of pre-review can speed up the code review process and potentially leave more time for a more thorough review and optimization of the code.
5. Translate Code
It may happen that a software developer is faced with problems for which he has to write in a programming language he does not know well. In such cases, the required functionality can be written in any other programming language and translated into the target language by ChatGPT. However, a subsequent manual integration of the code in the target language is necessary. However, the initial effort is reduced enormously if one has no experience in the target language.
So, through e.g. ChatGPT, developers can use many programming languages without worrying about the details of syntax and grammar. This gives them the freedom to focus on the function of the software without worrying too much about the technical aspects. This saves time and costs for advanced training.
Conclusion
LLMs like ChatGPT can significantly speed up software development by taking a lot of work off developers’ hands, freeing up more time and energy for other important developer tasks. Although the output of LLMs is not always perfect, they can still help speed up the understanding of code, find bugs, and perform simple tasks.
However, it is important that developers do not rely completely on LLMs. Although they can be very useful, it is still necessary to check the output and make sure it is correct. A developer should not blindly rely on the results of ChatGPT or other LLMs, but should always perform a critical review before sending the code to production.
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