ChatGPT is disrupting human-machine interactions, empowering users to interact with it conversationally and generate answers and content they ask for. Since its launch in November 2022, just about everyone has read ChatGPT news reports, heard a ChatGPT poem, or tried ChatGPT to “assist” with homework.
The development community has also tried ChatGPT for coding and there are conversations that ChatGPT would revolutionize traditional software, as we know it. With over two decades of software development experience under our belt and having developed complex software applications for innumerable clientele across the world, we have our own unique take on it.
But before that, lets understand what ChatGPT is and how it might potentially impact the software application development industry.
ChatGPT is a new conversational AI chatbot developed by OpenAI that reached public visibility in late November of 2022. Unlike traditional chatbots, ChatGPT is able to take advantage of advances in machine learning, which give it a greater contextual awareness of the documents it has been trained on. This allows it to generate responses that mimic those you might see from a human.
What makes ChatGPT different from some of the other similar tools is its breadth of responses and its ability to generate new responses that seem to have a high degree of intelligence behind them. This means that I can ask ChatGPT to tell me stories, outline an article, or even generate code and it will give me something that looks convincing and may even be usable.
As an AI language model, ChatGPT has a wide range of capabilities, including natural language processing, text generation, and machine learning. However, when it comes to building software, there are several reasons why ChatGPT might not be the ideal choice.
Lack of Context Awareness: One of the biggest challenges of using ChatGPT for coding is that it lacks context awareness. This means that the generated code may not always be appropriate for the specific context in which it is being used. Developers need to be careful when using the model to generate code and ensure that the generated code meets their specific requirements and fits the context in which it is being used.
This is because software development is an incredibly complex field that requires you to think about many different aspects of development including:
It turns out that in order to meet the varying and competing requirements of a software project, you need to be able to understand people, balance those competing concerns and deliver a creative solution that meets those needs in both short and long term ways. Since ChatGPT doesn’t actually understand the content it generates, it has no idea if its responses will work or are relevant to all of your business needs.
Need for Human Intervention: While ChatGPT might generate code quickly and accurately, it is still necessary for developers to review and validate the generated code. Because the code ChatGPT generates is based on code it has encountered before, it cannot make any guarantees that the generated code:
Perhaps most crucially, ChatGPT cannot modify code that it has previously authored or understand large solutions and modify them as needed. Furthermore, ChatGPT can generate code for specific purposes and in bits and pieces. You as a developer might still need to piece these together. Compare this with Xamun where the innovation is not just within the use of AI and bots but also with the process itself.
Dependence on Training Data: ChatGPT is a general-purpose language model that has been trained on a wide range of text data. While this makes it a versatile tool for many applications, it also means that it may not have the specialized knowledge and expertise required for certain types of software development. For example, ChatGPT may not be familiar with specific programming languages or frameworks that are commonly used in certain industries or domains. This could limit its ability to provide useful advice or guidance for developers working in those areas.
Furthermore, ChatGPT is a machine learning model that relies on training data to generate its outputs. This means that its outputs are only as good as the data it has been trained on. While ChatGPT has been trained on a large and diverse corpus of text data, it may not have been exposed to all the nuances and complexities of software development. As a result, its outputs may be incomplete or inaccurate, and it may not be able to generate novel solutions or approaches to software development problems.
Limitations of the ChatGPT Interface: ChatGPT is a text-based interface that may not be well-suited to certain types of software development tasks. For example, tasks that involve complex visual or spatial reasoning, such as designing user interfaces or optimizing graphics performance, may require more specialized tools and interfaces. ChatGPT may not be able to provide the same level of interactivity or feedback that is required for these types of tasks.
ChatGPT is used by developers to write code, but it still needs to be ported into an IDE where that piece of code written interacts with other modules; but for Xamun we would have our own approach.
Tackling complexity of dextrous software development: ChatGPT is designed to generate text outputs based on the input it receives. This means that it may not be able to take into account other factors that are important in software development, such as performance, security, scalability, and maintainability. Developing high-quality software requires careful consideration of these factors, as well as trade-offs between them. ChatGPT may not be able to provide comprehensive guidance on these issues, and its suggestions may need to be supplemented with other sources of expertise.
In conclusion, while ChatGPT is a powerful tool for natural language processing and text generation, it may not be the ideal choice for building software. Its focus on natural language may limit its ability to understand and work with programming languages and technical concepts, and its general-purpose training may not provide the specialized expertise required for certain types of software development. Additionally, its text-based interface may not be well-suited to all types of software development tasks. While ChatGPT may be able to provide some useful advice and guidance for software developers, it is likely to be most effective when used in conjunction with other sources of expertise and specialized tools.
Instead of replacing developers, we view tools like ChatGPT, GitHub CoPilot, and Amazon CodeWhisperer (as well as those that follow) as new tools in the developer’s tool belt. These code generation tools are good at generating basic ‘boilerplate’ code that can then be refined and modified by a professional developer to meet your needs. Even if the technology matures it is unlikely that tools such as ChatGPT would be able to:
Even if a successor to ChatGPT overcomes most of these limitations, you still need a developer with a deep technical understanding to be able to tell it what to do and evaluate the quality of its output. This is where AI-powered software development tools like Xamun would be most effective.
Having AI-powered software development would always be a more prudent alternative than having developers generate isolated pieces of code to plug it into a larger base to run the application. Reach out to one of our digital transformation evangelists HERE to understand how AI-powered software development might help you with your technical ambitions.
