Generative AI in Software Development:
Balancing Innovation and Code Quality
Generative Artificial Intelligence (Gen AI) rapidly transforms various industries with its remarkable ability to produce human-quality text, source code, and other creative content formats. Recent studies suggest that Gen AI can improve productivity for content writing and customer support tasks, particularly for less experienced workers.
A bit unexpectedly (at least a couple of years ago) the software engineering field is no exception, as Gen AI offers the tantalizing potential to enhance developer productivity, improve problem solving and accelerate innovation. Many have said that Gen AI is like a double-edged sword. As such, a crucial question comes to the forefront: How can software developers effectively leverage Gen AI tools within the software development process, while safeguarding the essential qualities of code integrity, reliability, and security?
The Promise of Gen AI for Developers
According to a survey by StackOverflow in 2023, 70% of software developers are currently using or planning to use AI tools in their development process. Additionally, of those already using AI in their development, more than 82% use it for writing code. Last but not least, another research from GitHub in 2023 indicated its Copilot was writing already 46% of code helping developers to code up to 55% faster. These (impressive) statistics suggest a paradigm shift in how software engineers develop new code, reuse existing one, and build the requested features for the systems/component they work on.
Generative AI presents several compelling advantages for software developers, with the potential to revolutionize how code is written, and projects are tackled.
Increased Efficiency and Problem Solving: One of the primary draws of Gen AI lies in its ability to automate repetitive and time-consuming coding tasks. From generating boilerplate code and basic functions to suggesting common code patterns, Gen AI can free up developers’ time. This allows them to focus their expertise on more complex problem-solving and the core logic of their applications.
Accelerated Idea Exploration: Gen AI tools can act as powerful ideation partners or else as AI pair programmers. They can enable swift testing of concepts and rapidly generating code snippets, alternative implementations, or even rudimentary prototypes based on a developer’s descriptions. This can streamline the development process and fuel innovation.
Improved Accessibility for Newcomers: Gen AI has the potential to democratize coding to some extent. By providing scaffolding, suggested code structures, and explanations (for given pieces of source code), these tools can decrease the learning curve for less experienced developers. This accessibility can nurture the next generation of software engineers and broaden the talent pool within the industry. Also these tools can be used to produce better and up-to-date software documentation speeding up the onboarding for newcomers in several software development projects.
The Risks of Overreliance on Gen AI
While the promises of Gen AI in software development are enticing, it’s crucial to acknowledge the inherent risks associated with this technology and the overdependence on it. Here are some primary concerns:
Hallucinations and Inaccuracies: Gen AI models are trained on massive amounts of data, but lack a proper understanding of coding principles. This can result in the generation of flawed, incorrect, or even insecure code, often presented with misleading confidence. This issue of “hallucinations” poses significant risks, if developers fail to critically evaluate the output.
Blind Acceptance of Output: The ease with which Gen AI produces code can lead to complacency. Developers may be tempted to trust the generated output without sufficient scrutiny. This is a dangerous path, as even seemingly plausible code may contain subtle errors or security vulnerabilities that could have far-reaching consequences. A report by GitClear, Coding on Copilot, notes, “Copilot is a powerful tool; however, it should not be ‘flying the plane’ by itself.”
Potential Skill Atrophy: While Gen AI can undoubtedly augment a developer’s abilities, overreliance could have unintended side effects. If developers habitually lean on the tool for basic code generation and problem-solving, their core coding skills may weaken over time.
Generating Technical Debt at Scale: The rapid code generation can lead to producing technical debt at scale. Technical debt refers to the negative consequences of making suboptimal design choices, taking shortcuts, or making compromises during software development. Gen AI tools may cause developers to neglect proper software engineering practices, (e.g. modular design, clean code principles, etc.) and instead produce source code of lower quality that can come at high volumes.
Increased Code Churn: In relation to the previous point, the GitClear report also highlights the potential problem of code churn: “Code churn — the percentage of lines that are reverted or updated less than two weeks after being authored — is projected to double in 2024 compared to its 2021, pre-AI baseline. The bottom line is that ‘using Copilot’ is strongly correlated with ‘mistake code’ being pushed to the repo.”
Poor Code Maintainability: Accumulated technical debt can lead to decreased code maintainability, increased development time for future enhancements or bug fixes, and higher costs in the long run. Poor code, maintainability can become the source of many hidden security gaps, jeopardizing not only software quality, but also the entire application security. Or, as the GitClear paper concludes, “Who’s on the hook to clean up the mess afterward?”
Best Practices for Leveraging Gen AI Responsibly
To harness the power of Gen AI in software development while minimizing risks, it’s essential to adopt a strategic and cautious approach. Here are some critical factors to consider when employing Gen AI tools to ensure code quality and security:
Domain Expertise Remains Essential: Gen AI tools should be viewed as powerful assistants, not replacements for a developer’s knowledge and experience. A deep understanding of programming principles, design patterns, and security best practices is irreplaceable for ensuring software integrity.
Treat Gen AI as a Starting Point: Rather than unquestioningly accepting generated code, developers should consider it a foundation for further refinement. Critical review, manual modifications, and rigorous testing must remain standard practices.
Implement Rigorous Code Review: Thorough code reviews are crucial, whether conducted by humans or through source code analysis tools. These reviews help identify potential errors, security weaknesses, and opportunities to optimize the code produced by Gen AI models. You may opt to partner with an experienced provider to undergo software quality and risk assessment and monitoring to get proficient advisory and a clear roadmap for improvement.
Invest in Gen AI Literacy: Empowering developers with a strong understanding of Gen AI capabilities, limitations, and potential biases is essential for responsible use. This includes training on critically evaluating generated output and spotting potential errors.
Use with Cautiousness
Generative AI undoubtedly holds transformative potential for the software development industry. However, it’s imperative to remember that Gen AI tools must be deployed with a healthy dose of caution and a continued emphasis on code quality. The future of software development likely lies in a synergistic model where human expertise and AI capabilities join forces and work hand-in-hand, enabling developers to create better, more secure software at an accelerated pace.