How to use self-healing code to reduce technical debt

The idea of using LLMs to implement self-healing code is exciting, but balancing automation and human oversight is still critical.

译自 How to Use Self-Healing Code to Reduce Technical Debt，作者 Kirimgeray Kirimli。

By 2028, some estimates suggest that 75% of enterprise software engineers will actively use AI code assistants, up from less than 10% at the start of 2023. In an industry where time is money, turning to generative AI is an invaluable tool to streamline workflows and increase productivity. However, valuing project speed can cost those in our industry.

Technical debt, the cost of choosing a faster rather than more efficient approach that takes more time, has been a drag on software developer productivity. Studies show that 23 to 43 percent of their time is spent solving problems that come with this practice. This saves time, but later pays interest in the form of rewriting, correcting, and improving the code.

Many believe that the rush to integrate generative AI-related tools will lead to an increase in technical debt due to rapid growth, complex code, and infrastructure changes. However, AI, especially LLM systems, has the potential to be the solution to this long-standing problem. Through its automation and self-healing capabilities, software teams that use the technology wisely can strive to reduce technical debt by proactively maximizing their self-healing code capabilities.

Let's discuss the concerns surrounding AI and technology debt, and how organizations can address and reduce this industry-wide dilemma with self-healing code.

What causes technical debt?

Technical debt is a significant problem in the software industry, leading to higher maintenance costs, slower development speeds, and less agility. Any form of cutting corners increases the risk of technical debt, and some in the industry see AI coding tools as the culprit.

Some developers are wary of these tools because of the lack of reliability of AI-generated code and the fact that they have no control over workflows. It is also believed that AI can lead to technical risks that can cause problems later on.

While AI isn't a major cause of technical debt, it can also play a role if used incorrectly. For example, a complex model that may initially perform well may present maintenance issues as new data emerges or needs to be upgraded. Integrating new applications and capabilities will equate to more machine learning operations (MLOps) processes, which can overwhelm existing systems.

Outdated or outdated coding practices are slowing down today's developers, forcing them to upgrade their processes, exacerbating the potential for technical debt. Solving this problem requires a systemic approach. For example, we once had a project where a customer's legacy code was hindering development. To address this, we deployed a team of experienced developers who systematically addressed technical debt and optimized the codebase, resulting in a 47% increase in development productivity and reduced project delivery time.

Conversely, AI, especially large language model (LLM) systems, has great potential to help reduce technical debt through automation and self-healing capabilities. Self-healing code, where software is able to identify defects and fix them without human intervention, is a popular solution to technical debt.

Address and reduce technical debt

As mentioned earlier, LLMs have emerged as a transformative solution for mitigating the risk of technical debt. AI has made great strides in understanding and generating text, and with its ability to process and generate human-like responses, it's clear that LLMs can integrate with existing codebases and ticketing platforms to create self-healing code. For example, code review tools can be developed using AI and LLMs to provide line-by-line analysis of generated code and issue human-like responses.

Additionally, AI tools can automatically find and fix bugs, which can help significantly reduce the problem backlog. Then, there are tools designed for code refactoring. They analyze inefficiencies in the code and make improvements to optimize performance, so that everything runs more smoothly. Based on experience and given the current pace of technological development, AI tools can dramatically improve code quality and make the development process more efficient. This impact is likely to really reduce technical debt in the next five to ten years. This gives the industry enough time to refine the technology, build a solid integration framework, and establish a solid manual review process to ensure everything runs smoothly.

Another way to reduce technical debt is dependency management. AI systems can update and manage code dependencies to keep everything secure and up-to-date. Finally, AI can significantly improve the overall quality of the code by identifying potential issues and suggesting fixes.

Establish internal practices

The idea of LLM self-healing code is exciting, but balancing automation and human oversight is still critical. Manual review is critical to ensuring that AI solutions are accurate and aligned with project goals, and self-healing code can drastically reduce manual effort.

Good data management is critical, and it's equally important to ensure that teams are familiar with best practices to ensure optimal data management for AI technologies, including LLMs and other algorithms. This is especially important for cross-departmental data sharing, with best practices including conducting assessments, integrations, and data governance and integration plans to improve projects.

All of this requires continuous learning among your employees. To encourage teams to take advantage of these opportunities, leaders should dedicate time to training workshops that provide direct access to the latest tools. These training courses can revolve around certifications such as Amazon Web Services (AWS), which can greatly motivate employees to improve their skills. By doing so, a more efficient and innovative software development environment can be achieved.

Another way to drive training adoption in your organization is to implement continuous learning rewards. For example, a senior software engineer may think they already know everything about data analysis, but they can be incentivized to participate in additional training with a bonus. These opportunities can expose employees to career paths they may have overlooked, benefiting both individuals and organizations through the use of the latest solutions for broader skill development.

Like any new technology adoption, generative AI requires thoughtfulness. Therefore, when deploying genAI, there should be as much planning and preparation as developing coding standards around hand-coding, which can help minimize technical debt. This also applies to solutions such as integrated self-healing code, which require strict settings to be effective in the long term.