ChatGPT continues to garner a lot of buzz within the general technology community. This machine learning powered natural language processor effectively functions as a supercharged chatbot, with the ability to generate text including poetry, articles, and even source code. Not surprisingly, its rapid growth in popularity caused much consternation among software engineers worried ChatGPT might replace their jobs.
As far as software development is concerned, ChatGPT ultimately serves as a tool to assist programmers, making them more productive. In this regard, it relates to other similar applications used as part of the no-code or low-code movement. Any developer needs to be flexible and confident in their own abilities while also taking advantage of the productivity boost provided by an intelligent machine learning model.
So let’s dive into the details of ChatGPT and its potential influence on the practice of software development. We cover the changes in how a developer approaches their programming workflow when using ChatGPT or any other similar application. In the end, it’s important not to worry about your software engineering career. Instead focus on how to leverage ChatGPT or similar tools to make yourself a more productive programmer.
If you are curious about just what ChatGPT is, simply ask it. Note you might receive a “ChatGPT is at capacity right now” message, which is not surprising considering its popularity. The chatbot returns the following answer:
GPT-3 is a state-of-the-art language model developed by OpenAI. It is capable of generating human-like text and can be used for a variety of natural language processing tasks, including generating responses in a chat-like setting. GPT-3 stands for “Generative Pretrained Transformer 3,” referring to the type of machine learning model it is and the fact that it is the third iteration of the GPT series. It is a very large model, with 175 billion parameters, which gives it the ability to generate high-quality text.
The ultimate usability of the tool depends on the quality of its underlying machine learning model. As such, expect its suitability for software development and other content creation tasks to improve over time. In fact, this is no different from chatbots already in use today. A variety of businesses leverage automated bots to serve on the front lines of customer service. This strategy enables human CSRs to focus on more value-added tasks.
Expect a similar use-case with ChatGPT for software development. In the end, ChatGPT offers the promise to make software developers more efficient and productive. Software shops currently leverage automation and machine learning models to improve productivity by handling various repetitive tasks, like builds, migrations, source code maintenance, unit testing, and more.
In its current form, ChatGPT functions well as a tool to help programmers. For example, if you have a question about an obscure JavaScript function, just ask it. Expect a clear explanation including some example source code. In fact, many tech pundits wonder if ChatGPT or a similar AI-powered chatbot ends up replacing Google for online search. Additionally, current popular IDEs, like Eclipse or Visual Studio might replace their internal help functionality with ChatGPT or a similar tool.
The source code it provides might eventually handle much of the drudgery typical of classic programming, including coding object interfaces, regular expression definitions, or even how to perform basic tasks, like swapping the order of array elements. Of course, don’t expect perfect source code or even accurate information on programming languages and APIs. Consider ChatGPT to be a junior programmer whose work needs to be closely checked. Once again, it still offers the potential to make any developer more productive.
In fact, considering ChatGPT to be another aspect of the No-Code trend also makes perfect sense. No-code theoretically allows business analysts or other “non-technical” professionals to produce working applications by providing detailed requirements and specifications as an input. Still, experienced software engineers remain a critical part of any project using this approach. Software development remains a highly complex task, and even crafting the requirements for an application to be built using a no-code tool benefits from the skills of a veteran programmer.
Notably, the human element remains a crucial piece of any software initiative. Consider everything involved in identifying a business pain point, vetting the relevance of a target market, and finally creating detailed specifications to meet those first two items. All this work happens before one screen gets designed or one line of code is written. A chatbot might make that process faster, but there’s no real replacement for experienced technical professionals that understand context.
Ultimately, don’t expect ChatGPT or any similar tool to replace the human programmer. Once again, consider it to be a highly functional AI assistant with the potential to make anyone in the field a better, more efficient developer.
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