The Role of AI Code Generation in Software Development

Reduce Manual Coding

Instead of writing all code from scratch, developers can use AI code generators to automatically generate code. They can use the autocomplete function to generate partial code snippets or complete functions. They can also interact with an AI chatbot to state their intentions and generate code suggestions.

AI code generation can speed up the process of writing code that a developer was going to write from scratch anyway, and it can help them jog their memory by translating their written intentions into contextually relevant code.

Speed Up Code Reviews and Maintenance

When a developer in an unfamiliar environment needs to perform maintenance or a code review, they can use an AI code generator to understand the functions and variables within the source code (code repository).

The AI code generator can produce more accurate responses if such functions and variables have specific names. Labeling makes it easier for the AI code generator to identify and contextualize a code snippet or a complete function, speeding up the code review and maintenance process.

Additionally, AI code generators can analyze trends and patterns in the source code to identify and suggest fixes for bugs, duplicate code, and inconsistencies.

Increase Accessibility

For novice developers, AI code generator tools can be an effective way to help them come to grips with basic coding concepts. These tools can provide real-time feedback, alerting developers to mistakes and suggesting areas of improvement.

These accessibility benefits extend to a non-technical audience, people who are subject matter experts first and programmers second, such as data scientists who are experts in analyzing data and not creating software applications.

That said, all developers should know what they’re doing and not just accept every AI-powered suggestion.

Risk of Technical Debt

Technical debt is the cost of prioritizing speed over quality. It is the measurable time spent on reworking code. AI code generators are prone to generating results that may be syntactically correct but also hard to understand, inconsistent with predefined coding standards, and not optimized for performance.

A 2024 report found that the code churn rate has increased to 7.1% – an increase of 3.5% compared to the pre-AI era of 2021, meaning developers spend more time on code revisions than ever due to the advent of services like GitHub Copilot. While some code churn is acceptable in custom software development, too many code rewrites may prolong development and take time away from developers on important tasks.

Security Vulnerabilities

Aside from being hard to understand and overly verbose, AI-generated code may pose a security risk if developers fail to review and test it. Insecure code may expose system flaws or weaknesses that threat actors can exploit to access and compromise an application’s security.

How prevalent is insecure AI in software development? In a 2023 survey, 56.4% of respondents (537 software engineering and security squad members and leaders) said they frequently encounter security issues with AI code suggestions.

While it’s good that developers can identify insecure AI-generated code, this study highlights the importance of choosing a software development team that prioritizes security. At Orient Software, our independent QA and software testing team performs rigorous testing to foresee and address any potential errors – before they escalate to service-impacting issues.

Decrease in Critical Thinking and Problem-Solving Tools

Creative problem-solving is an essential skill for software developers. It enables them to identify a problem, experiment with different solutions, and choose the best solution to resolve that problem. For example, developers had to use creative problem-solving to ensure that Google autocomplete did not show predictions that violated Google Search’s policies.

Unfortunately, developers who rely on AI-generated code risk losing (or never acquiring) their problem-solving skills if they don’t understand what the code does, how it works, and how it fits within the context of the source code. Additionally, developers may lose control of their source code, making it harder to maintain than if they wrote their code manually.

GitHub Copilot

Developed by GitHub in collaboration with OpenAI, Copilot uses a Generative Pre-trained Transformer (GPT) to understand natural language and generate code based on context and a developer’s intentions. With support for programming languages like JavaScript, Go, Ruby, and Python, Copilot can be deployed into almost any Integrated Development Environment (IDE), such as JetBrains and Visual Studio Code.

Amazon CodeWhisperer

Like Copilot, Amazon CodeWhisperer is another AI code generator that can autocomplete code based on context and intent. What sets CodeWhisperer apart? The first is the free pricing tier for individual developers, whereas Copilot is only free to students and educators. The second is a feature that tells developers when a code suggestion is similar to its training data – letting them decide on whether to use the code.

Tabnine

With a focus on privacy and security, Tabnine goes beyond offering real-time code autocompletion. The platform never stores your code or uses your code to train its AI. It’s also trained on only open-source code with permissive licenses. For developers concerned about the legal implications of AI code generators, Tabnine is promoting itself as an ethical alternative.

Google AI Codey

A three-suite of AI code assisting tools, Google AI Codey has APIs for code generation, code chat, and code completion. Each API performs a specific function. For example, the code generation API can generate code based on natural language descriptions, and the code chat API can answer code-related questions. Google AI Codey offers limited integration support for Visual Studio Code, PyCharm, and IntelliJ IDEA.