Risks and rewards of generative AI for software development

Generative artificial intelligence (AI) is a form of AI that can create new, original content such as text, code, images, video, and even music. Generative AI-powered tools like GitHub’s Copilot and OpenAI’s ChatGPT have the potential to revolutionize the way you develop software, enabling you to be more efficient and creative.

Used in the right way, generative AI can streamline workflows, accelerate development cycles, and unlock the potential for innovation. However, the technology poses inherent risks that demand careful consideration and ongoing vigilance against the potential for introducing errors, security vulnerabilities, compliance issues, and ethical concerns into your code base.

Below, we’ll weigh both the transformative benefits and intrinsic risks of AI in software development and describe techniques you can use to safely incorporate this powerful technology into your delivery process.

Benefits of generative AI tools in software development

Generative AI has roots stretching back to the 1940s, with Alan Turing’s seminal 1950 paper “Computing Machinery and Intelligence” introducing the concept of machine intelligence and its potential for creative generation and problem-solving. New neural network capabilities in the 1980s allowed models to learn by computing the differences between predictions and outputs.

In recent years, the combination of larger labeled data sets, faster computers, and new ways of automatically encoding unlabeled data has expedited AI development. Transformer models — neural networks specialized in natural language processing — can analyze the natural-language context that programmers provide to return relevant code suggestions.

These generative AI tools offer unique advantages that can transform how you create and deploy software. Let’s explore some of the most notable examples.

Improved efficiency and productivity

Generative AI tools streamline development workflows by automating routine coding tasks and providing real-time code suggestions. This automation frees up your time, allowing you to focus on higher-level design and problem-solving development tasks.

Accelerated time-to-market

Generative AI tools can significantly reduce the time required to develop and deliver software products. You can rapidly prototype, iterate, and refine their code. This agility enables faster experimentation and validation of ideas, leading to quicker iterations and shorter development cycles.

Increased innovation

Generative AI tools stimulate creative thinking by providing novel code suggestions and alternative solutions, enabling you to consider new design possibilities. Generative AI also accelerates certain development tasks, allowing you to focus more on innovation.

New tools like Copilot can generate entire functions or classes from a few lines of input, speeding up prototyping and testing new ideas. As models continue to improve, generative AI will enable faster experimentation and more ambitious software projects through increased automation and intelligence.

Cost savings

Integrating generative AI tools can lead to significant cost savings. These tools enable you to use existing codebases more effectively. You can build upon previous work by suggesting relevant code snippets and reusing proven patterns, minimizing redundant coding efforts. In short, you do not have to expend resources rewriting existing functionality.

Integrating generative AI can help you complete projects faster with smaller teams, which also saves money, especially in large software projects. You can reduce costs throughout software creation, including code development, computing infrastructure, and project management overhead. Because generative AI automates routine tasks and provides intelligent code suggestions, you can do more with less and get the most out of your development budget.

Risks of generative AI in software development

As with any transformative technology, generative AI in software development raises policy and ethical concerns. When training AI models on publicly available code, issues such as potential copyright infringement or loss of proprietary information arise.

Moreover, generative AI models may replicate biases present in the training data, leading to the propagation of discriminatory practices in the generated code. More widely, bias in AI systems can perpetuate social inequalities and reinforce unfair practices.

It’s also important to note that generative AI tools are not 100% accurate. They are known to hallucinate, fabricating information entirely. Relying on AI models means you run the risk of bugs, security vulnerabilities, and architectural flaws.

Let’s examine some of these risks more closely.

Code quality issues

AI-generated code may not always meet your organization’s quality standards. Generative AI tools heavily rely on the patterns and practices learned from training data, which can lead to suboptimal or inefficient code.

You can mitigate this risk by exercising caution and carefully reviewing the generated code to ensure it meets the required quality standards.

Security vulnerabilities

AI models are trained on vast code repositories that may contain exploitable patterns or known vulnerabilities. These tools can introduce potential security flaws via incorrect input validation, weak encryption, or insecure access controls.

Compliance and intellectual property (IP) concerns

Compliance with IP rights and licenses is another area of concern. Generative AI models are trained on publicly available and proprietary code, exposing them to code with an unknown pedigree and ownership. There is a risk of inadvertently infringing copyright or violating licenses when generating new code from these models.

Many generative AI tools also reserve the right to train on the prompts provided by users. In organizations with little oversight into how developers are using AI, there is a risk that proprietary code, customer data, or other secrets may be publicly exposed. This can result in major compliance violations, especially in highly regulated industries.

Addressing compliance and IP concerns requires oversight and analysis to understand how models work, evaluate training data, and review generated code. Transparency and strong governance processes are necessary to integrate generative AI responsibly and maintain confidence in the systems and their output.

Lack of visibility

AI-generated code can be complex and challenging. Even if it works as expected, you may find it difficult to understand the underlying logic, or how the AI determined which approach to use. This lack of visibility can make it harder to ensure compliance with coding standards, architectural guidelines, and industry best practices. It can also make it difficult to refactor or debug your code when something goes wrong.

Increased technical debt

Generative AI tools can expedite software development, but rapid code generation may increase technical debt. Technical debt refers to the cumulative consequences of suboptimal design choices, shortcuts, or compromises made during development. The speed and convenience of generative AI tools can result in developers bypassing proper software engineering practices, such as modular design, documentation, or clean code principles.

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. Crucially, the extent of the technical debt you are likely to accrue depends on how you deploy and integrate generative AI into your development workflow.

Technical debt is contingent upon the practices and safeguards adopted during development. You can avoid technical debt by having experienced developers guide generative AI tools and ensuring the generated code adheres to secure, compliant, and well-structured standards.

Counteracting the risk of AI-generated code with continuous validation

To mitigate against the new sources of risk introduced by AI coding tools, organizations need to implement rigorous testing and validation processes. These include thorough code reviews, automated testing, and security analysis. Human oversight and expertise remain essential in ensuring the quality, security, and compliance of AI-generated code.

Follow these strategies to counteract some of the risks of using AI-generated code:

• Code quality testing: Static analysis tools can examine whether the AI-generated code adheres to coding standards and identify potential issues such as code complexity, unused variables, or improper error handling. In combination with automated code quality checks, you can implement manual code approvals can help identify code quality issues, ensure compliance with coding standards, and improve maintainability.

• Security testing: Automated security scanning tools can analyze the AI-generated code for known vulnerabilities and insecure coding practices. You can implement both static and dynamic tests to ensure the robustness of your AI-generated code.

• Compliance and intellectual property testing: Automated compliance testing tools can verify that the AI-generated code adheres to open source licenses and intellectual property rights.

• Functional and integration testing: You can create unit and integration tests to verify the functionality and behavior of individual AI-generated code components and ensure that code interacts correctly with other software components and external dependencies.

You can automate these tests in your continuous integration and continuous delivery (CI/CD) pipeline to easily implement the vetting practices outlined above. The pipeline integrates various testing stages and automatically executes tests with every code change.

Here is how your CI/CD pipeline can help you maintain confidence in and visibility of AI-generated code:

• Automated testing: The CI/CD pipeline can trigger automated tests, such as unit tests, integration tests, and security scans, whenever you commit new code. This reduces your workload and ensures that tests are consistently executed.

• Test coverage and reporting: The pipeline provides visibility into test coverage, indicating which parts of the AI-generated code require additional attention.

• Continuous validation: Integrating continuous validation into the CI/CD pipeline subjects any changes or updates in the AI-generated code to a series of tests. This testing continuously validates code quality, security, compliance, and functionality.

• Feedback loop: The CI/CD pipeline facilitates rapid feedback loops, enabling quick iteration and refinement of the AI-generated code.

Conclusion

AI tools like Copilot and ChatGPT can offer immense efficiency gains and accelerated time-to-market advantages. However, it’s essential to approach AI integration with caution, considering the associated risks.

To ensure success, be sure to vet and test all AI-generated code — not just for functionality but also for IP issues, vulnerabilities, and more. Implementing robust tests in your CI/CD pipeline empowers you to leverage AI without exposing your organization to uncertainty and risk.

Sign up for a free CircleCI account to learn more about how continuous integration can help you safely integrate generative AI into your development process.