Artificial intelligence (AI) has been used in several industries to automate procedures, increase efficiency, reduce errors, data analysis and improve decision-making. This helps businesses increase efficiency, innovation, productivity and customer satisfaction. AI has also been used in software development. This article talks about AI. AI-generated code, AI-powered code generators, the benefits, downsides and limitations and why human expertise is still needed to build applications that are reliable, robust and maintainable. 

The Role of AI in Software Development 

AI has just jostled its way into almost everything we touch and software engineering is no exception. The incorporation of AI in software engineering has been transforming software development processes in more ways than one. You can use AI to automate tedious tasks, generate source code faster, create code documents and even write unit tests. 

The usage of AI in the software development life cycle of any project will facilitate faster workflows, thereby enabling your software developers to focus on creative and complex tasks instead of spending time on mundane tasks. 

Here’s how AI can help in software development at a glance: 

• Project Planning 

• Project management 

• Faster development cycles 

• Generate source code 

• Create code documentation 

• Testing and debugging 

• Assist in code reviews 

• Build UI prototypes 

What is AI-Generated Code? 

AI-generated or AI-powered code refers to the source code produced by AI-powered tools rather than manually written by software developers that typically use machine learning models — often trained on vast datasets — to produce source code based on given inputs. 

It involves the usage of AI-based code generators to generate source code by leveraging the advances in large language models (LLMs) and natural language processing (NLP).  

Typical examples of inputs provided to these tools manually can include an instruction to generate a program based on a given scenario. The produced code could be a single function, a whole program, or simply a few useful lines to expedite a tedious task. 

AI-enabled tools have altered the development landscape, relieving software developers from writing tedious code. As a result, this helps these developers address intricate coding challenges while automating source code wherever needed. 

There are several benefits of AI-based code generation, such as the following: 

• It reduces development time by generating code quickly and relieving the developers of having to write code manually 

• Streamline processes in the software development life cycle by automating monotonous, tedious or mundane tasks 

• It can help you generate unit tests quickly and efficiently 

• By enabling you to generate code faster, AI can help boost the overall productivity 

Where Does Generated AI Code Fall Short? 

While AI-generated code can help software development, it is not at all a replacement for code written by human expertise because of the reasons discussed in this section. 

Technical Debt 

Although many AI-powered tools can generate source code for you, they seldom consider the design and architecture of your application or how the architecture components are related to one another.  

Most importantly, if you’re using AI-generated source code, you might have code redundancy and even technical debt, as these tools do not reuse or refactor existing code but build everything from the ground up. 

Quality Concerns 

While it is excellent at generating code quickly for you, it cannot produce code by considering maintainability, reusability, or scalability and performance as a human software developer can. Additionally, AI-generated code can often be insecure and contain bugs. As a result, before you use such code, you must perform code reviews, make changes to the generated code to clean it up and make it free of bugs and then test the code thoroughly. 

Maintainability Issues 

Although the source code generated by AI-powered tools might cater to the functionality you need, it can be overly complex and contain unnecessary abstractions, which might make debugging and maintenance a nightmare.  Worse, such inefficient issues may increase the KLOC (an acronym for Kilo Lines of Code,) making maintenance even more difficult.  In software engineering, KLOC is a metric often used to measure the number of lines of code in an application. 

How Does AI-Based Code Generation Work? 

AI-powered tools such as ChatGPT and GitHub Pilot leverage advanced machine learning models and are trained from source code and natural language text that is readily available in public data sources and repositories. Software developers can take advantage of AI-powered tools to generate source code in various ways, such as the following: 

• Autocomplete: Code is typed in manually and AI-powered tools can autocomplete the code 

• Natural Language Input: Developers enter the specific requirements by entering text, prompting the AI-enabled tool to generate code. Once this input is provided, the AI-powered tools can produce relevant code for you.  

• Direct Interaction: Chat directly with AI-enabled chatbots such as GitHub Copilot Chat 

You can also take advantage of AI-powered tools to translate source code from one programming language to another. 

AI-Based Code Generation vs Low-Code vs No-Code 

AI-based code generation tools and the low-code and no-code platforms have one thing in common: They all can help you build applications quickly. Low-code platforms help developers build software applications without having to write code for each feature of the application. It encompasses a collection of reusable components that can be used to build applications simply by dragging, dropping and arranging the components in the user interface.  

As the name suggests, no-code platforms enable developers to build applications using drag-and-drop user interfaces without the need of having to write even a single line of code. AI-powered code generation tools use Generative AI models to generate source code based on the inputs provided to them. While low-code and no-code tools often target non-developers and business users, AI-based code generation tools are typically used by software developers. 

Future of AI-Powered Software Development 

Because of the surge in AI usage, many AI-powered tools are already available to help you write code fast. Many more tools will emerge to help developers write code and optimize development workflows and efficiency. 

While LLM technologies continue to evolve, AI code generation will become more popular. However, code generated using AI-powered tools will have defects and constraints, thereby requiring human intervention to refine the generated code and make it error-free and usable. 

However, while AI may become more integrated into the development workflows, it will never replace developers. Instead, these tools will assist developers in generating code quickly and reduce the time spent writing repetitive code, thereby enhancing the team’s throughput. 

Striking the Balance is Key 

While AI-generated code won’t be able to match human code quality, it might greatly increase efficiency. While these tools can produce code quickly, there are a few drawbacks to using these technologies. Use a combination of AI-generated and human-written code in your apps to reap the maximum benefits of these tools in your projects.  

Before using AI-generated code, make sure it passes all necessary tests. While AI-generated code may get you started, it might be necessary to rework it, so it meets the required standards for coding, design and architecture. To get the most out of AI, try incorporating AI-powered code into your project while bearing these things in mind. In essence, striking the right balance is what you need to do. 

Key Takeaways 

• AI is not a substitute for human capabilities, intelligence; it can help software development by generating code quickly. 

• AI-based code generators are great, but they have several limitations as well 

• Soon, AI will play a significant role in software development 

• AI-based tools can help translate source code from one programming language to another programming language 

• You should blend AI-powered tools with manual effort and intelligence to build high quality applications faster 

• Some of the widely used tools for code generation include GitHub CoPilot, ChatGPT, Amazon Q Developer and Google AI Code Generation Tools such as Gemini, Code Llama and TabNine.