Stack Overflow arrived 15 years ago, revolutionizing the way programmers share knowledge and ready-to-use code. Today we are seeing another transformation with generative artificial intelligence (AI) taking the programming world by storm. Chat interfaces and plugins such as GitHub Copilot and ChatGPT are generating code giving developers the power to create code almost instantly.
However, those who think these tools can turn them into super software developers overnight are deluding themselves. Using a code generation tool still requires being a good programmer who still has to make the effort to understand the code produced and figure out if it is correct and the best possible for the use case.
Codegen tools are trained on large amounts of existing code, including the ways the code has been written and how it has solved problems in the past. If you’re writing something that solves common problems-such as database or SDK connections, API definitions-codegen can handle it easily and excels at these types of tasks.
For example, if I need a function to connect to a database, codegen can create it for me quickly. This is a huge time saver compared to manually searching and editing code examples. However, the real trick is knowing what you want. Codegen can be a great help if you already know what you are trying to achieve. It can be an incredibly useful tool for solving team, company, and workflow-specific problems.
However, the problems of understanding requirements remain fundamental. Code has always been the easy part, while requirements have always been the main challenge. You have to have a clear understanding of what you want to achieve from the software so that the codegen can generate the correct code.
But AI answers are not always reliable; the results can be wrong or even made up. Developers should therefore be cautious about blindly relying on AI answers, and in fact only a tiny minority of 3 percent trust them without hesitation. The real effectiveness of codegens lie in their aid to documentation and the creation of context-specific code examples for the project. However, real benefits require improving the entire organization, not just the speed at which code is written. This includes automating processes, sharing knowledge across the enterprise, and creating effective processes in team management.
In addition, it is critical to focus on documentation needs when using AI-generated code. A poorly documented or understood code base poses a risk to the long-term productivity of the organization. It is crucial to have the original information available for reference, especially if it is not in the public domain. Also because simply copying and pasting the code could create an intellectual property problem for the company since at the moment whatever is generated by AI cannot be copyrighted in addition to a potential risk of infringing other copyrights by using code that AI has taken from protected software.
In summary, generative AI can certainly speed up the software development process, but it is no substitute for a thorough understanding of business requirements and context. Organizations aiming for sustainable productivity must focus on automation, knowledge sharing, process optimization, and team management.
