The advent of AI-generated application code promises to be a significant boon to developers the world over. Tools powered by large language models (LLMs) are being integrated into software development environments, accelerating development timelines and reducing manual coding errors. The allure of AI in coding is undeniable, with its promise of boosting productivity and innovation. This new paradigm also brings with it a significant challenge: the potential for an exponential increase in technical debt.
Understanding technical debt in the age of AI
Technical debt, a term coined to describe the future cost of reworking code to correct shortcuts taken during development, is not a new concept. But the integration of AI into the software development lifecycle has the potential to compound this issue in ways previously unimagined. While AI-generated code can accelerate the development process, without proper oversight, it can also lead to inconsistencies, maintenance challenges, and security vulnerabilities, raising important questions about the integrity and maintainability of this code. Will AI-generated code result in more robust and efficient applications, or will it contribute to a growing mountain of technical debt that teams will struggle to manage?
Navigating the challenges: Strategies for managing AI-induced technical debt
The key to harnessing the potential of AI-generated code while mitigating the risks of technical debt lies in a balanced approach that combines the strengths of AI with the discernment and expertise of human developers. This includes:
- Establishing clear guidelines and standards: Developing a set of best practices for the use of AI in coding, including standards for code quality, documentation, and testing.
- Investing in continuous education and training: Ensuring that development teams are equipped with the knowledge and tools they need to effectively integrate AI-generated code into their workflows.
- Applying software intelligence for continuous oversight: Leveraging software mapping and intelligence technologies to accurately understand the quality and potential technical debt of both AI-generated and manually written code.
Software intelligence: A Beacon in the Fog of Technical Debt
Software intelligence technology can be immensely helpful in the quest to understand and manage technical debt in the era of AI-generated code. By mapping automatically generated insights about the inner workings of the applications with qualitative information across the software landscape, products like CAST Highlight enable leaders to understand their exposure to technical debt across an application portfolio based on facts.
CAST Highlight works by reading and rapidly analyzing the source code of applications and identifying patterns in the code that contribute to technical debt. For example, it will identify within a Java application’s code if too many literal numbers are being used. Although this is not necessarily a bad practice within the context of a small snippet of code that was generated by an AI, when it is integrated into the codebase of the broader application, it can make the application overly cumbersome to maintain and update over time. The recommended approach would be to manage this value globally. This is just one example of hundreds of technical debt code patterns that CAST Highlight detects automatically.
Using a built-in survey capability, CAST Highlight also captures contextual information such as the business impact of each application across a portfolio to map it against the technical insights from the code.
Tech debt insights (X-axis), Business criticality (Y-axis), Application size (bubble size)
Bird’s-eye view of a portfolio of 300 custom applications, generated by CAST Highlight
These insights enable organizations to:
- Evaluate and compare: By understanding technical debt trends across applications that use AI-generated code versus those that do not, leaders can make informed decisions about the adoption and management of AI in their development processes.
- Prioritize and address: Identifying the most critical areas of technical debt enables teams to focus their efforts on making impactful improvements, ensuring that resources are allocated efficiently.
- Embrace proactive management: Understanding the landscape of technical debt and AI-generated code equips leaders with the knowledge to take proactive steps in mitigating risks and optimizing development strategies.
Armed with this intelligence, IT executive can prevent technical debt build-up and also see the best paths for addressing the debt past due, see Unwinding Tech Debt. The 8% Approach.
Steering towards a sustainable future
By adopting a strategic approach that leverages both human expertise and technology like CAST Highlight, organizations can navigate this new frontier successfully. The goal is not to shun the advances brought by AI, but to embrace them with a clear understanding of their implications and a commitment to maintaining the health and resilience of software systems.
In the end, the journey through the evolving landscape of AI-generated code and technical debt is one of balance and foresight. By recognizing the potential pitfalls and equipping ourselves with the right tools and strategies, we can harness the power of AI to drive forward, not into a quagmire of technical debt, but towards a horizon of innovation and excellence.
Greg Rivera is VP of CAST Highlight. The CAST SaaS platform helps customers and partners accelerate app modernization / cloud migration, rationalize their app portfolios, and reduce open source risk.
https://www.castsoftware.com/
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