The AI Time Bomb: Unveiling the Cost of Ignoring Technical Debt (Part II)

Technical debt should be viewed as a loan—one that isn't inherently bad and can even serve as a powerful tool for business growth, provided it is managed responsibly. Just as ignoring or mismanaging financial loans can lead to disastrous consequences, neglecting or miscalculating technical debt can undermine the quality of your products and services. Failing to address technical debt is as unwise as ignoring your financial obligations while spending all your income on luxuries.

We have developed a method that measures technical debt through comprehensive code scanning, reports the risks associated with different components of that debt, prioritizes risk mitigation, and establishes a clear framework for keeping technical debt affordable and manageable. Although the implications of technical debt are well understood among developers, business stakeholders often perceive it as an amorphous monster where infinite resources can be spent without visible improvement—much like Don Quixote's battle with windmills or the endless toil of Sisyphus. Our method gives technical debt a solid shape, enabling the C-suite to precisely target and address its root causes, turning what once seemed like an insurmountable challenge into a manageable and strategic initiative.

In our first article, The AI Time Bomb, we highlighted the critical issue of neglecting technical debt within widely used AI frameworks. We outlined our study's goals and shared our analysis's initial findings. In this second article, we delve into the details of our methodology, exploring how technical debt correlates with the estimated costs of maintenance, extension, and modification. We aim to define a methodology for technical debt management that keeps these costs under control and can be easily applied across industries. The third and final article will propose a methodology for cost optimization and risk minimization.

This article provides a detailed exploration of our methodology, offering actionable insights for managing technical debt effectively in AI frameworks.

Methodology Overview

Our methodology comprises several key steps designed to thoroughly assess code quality and understand the impact of technical debt on modern frameworks. Although we use AI frameworks as our use case, this method can be applied to any industry [DEPIN Tech Debt ]

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Assessing Code Quality

We began our analysis by systematically assessing the code quality of modern AI frameworks through a rigorous technical debt calculation and classification process. As Sylvain Tiset aptly describes, technical debt is "everything that stops us from developing software fast." Over time, this debt accumulates, diminishing the capacity to introduce new features and making the codebase increasingly difficult to maintain. Identifying the specific areas within the codebase that contribute most to technical debt was crucial for understanding how these factors impact the software's overall health.

While there is ample literature discussing the balance between technical debt and business value, we found a lack of numerical justification for these claims. The core objective of this series of articles is to bridge that gap by providing consistent, data-driven measurements that validate this relationship.

Our goal was not only to identify technical debt but also to quantify and monitor it—wherever quantification is feasible. We focused on issues that could be clearly enumerated and for which we could accurately estimate the resolution effort. These included defects, code smells, duplications, vulnerabilities, outdated packages, and license violations. These elements were chosen because they represent measurable risks that can be mitigated with targeted, quantifiable efforts.

However, we deliberately excluded certain aspects of technical debt from our analysis that, while significant, are challenging to measure accurately. For instance, improvements in areas such as architecture and cyclomatic complexity cannot be easily segmented into discrete steps, nor can their progress be effectively monitored. Unlike other code quality issues, you cannot simply measure a "10% improvement" in architecture or cyclomatic complexity and then safely estimate the effort required to reduce the remaining "90%." The inherently complex and interdependent nature of these factors makes it difficult to quantify their impact and the effort needed for their resolution. By focusing on quantifiable elements, we aimed to create a more precise and actionable assessment of the technical debt present in these frameworks.

Defining a Quality Benchmark

Establishing a reliable Code Quality Benchmark was a critical step in our methodology, enabling us to objectively evaluate and compare the code quality across various AI frameworks. We achieved this by calculating the average of our assessment findings while meticulously excluding any quality outliers that could skew the results. This benchmark serves as a standardized reference point against which other frameworks can be measured, offering a clear and consistent perspective on their relative code quality and technical debt levels.

In practical applications, such benchmarks can be adapted to meet the specific needs of different organizations. For example, a private equity firm managing a diverse portfolio of companies and products could select its top-performing products—those with the highest return on investment (ROI) measured by the development investment-to-revenue ratio—as the basis for its quality benchmark. By comparing other products against this customized standard, the firm can identify and understand the factors contributing to lower ROI in underperforming products. This comparative analysis illuminates areas where targeted improvements can yield significant benefits, guiding strategic decisions on resource allocation and development priorities.

To refine our benchmark further and ensure it accounts for the multifaceted nature of technical debt, we incorporated Martin Fowler's Technical Debt Quadrant classification. This framework categorizes technical debt along two axes: reckless versus prudent and deliberate versus inadvertent. By applying this classification, we could differentiate between intentional and unintentional sources of debt as well as assess the thoughtfulness behind the incurred debt. This nuanced approach allows for a more comprehensive understanding of the codebase's health, recognizing that not all technical debt is inherently detrimental and that, when managed prudently, it can be a strategic tool for balancing short-term delivery pressures with long-term maintainability.

Comparing Costs and Prioritizing Technical Debt

A key part of our analysis involved comparing the costs of technical debt with the hypothetical cost of redeveloping each library from scratch. This allowed us to classify the debt based on its financial impact, prioritizing areas that need immediate attention versus those manageable over time.

To enhance accuracy, we utilized a machine learning model built on GPT, which estimates the effort required for different issues—such as defects and outdated packages—across various tech stacks. The model also tailors cost estimates to specific industries like healthcare and automotive, allowing users to refine risk remediation costs dynamically and accurately. This approach enables organizations to adjust their technical debt strategies as needs evolve, ensuring efficient resource allocation.

In addition to financial impact, our cost comparison also considers broader business implications, such as customer satisfaction and brand reputation. Addressing technical debt can prevent delays in feature delivery and reduce the risk of bugs, ultimately protecting customer trust and brand value. Just as interest on loan compounds over time, so too does technical debt, potentially leading to exponential increases in maintenance costs if left unchecked.

Unchecked technical debt can escalate operational costs, lead to revenue leakage, and hinder talent retention. Burdensome technical debt makes systems harder to maintain and less appealing to top developers, creating a cycle that further complicates development efforts. Given a fixed development team capacity, the more we spend on bug fixing and fighting with the source code complexity, the less availability we have for new feature development and effective customer support.

By integrating machine learning-driven cost estimation, we offer a more precise and comprehensive view of technical debt's true cost. This perspective is crucial for aligning technical debt management with both immediate needs and long-term strategic goals.

Establishing a Dynamic Technical Debt Standard

We developed a Technical Debt Standard that not only reflects industry trends but also leverages advanced technology to provide more accurate and dynamic estimates. Traditionally, static code analysis findings are translated into the effort required—expressed in working hours—to mitigate identified risks. The Technical Debt Ratio (TDR), as described by Tiset, is a key metric in this process, calculated by comparing remediation costs to development costs, with a value under 10% considered acceptable.

However, our approach goes beyond static estimation. We utilize a machine learning model built on GPT to accurately estimate the required effort for each type of issue—whether it's defects, outdated packages, or vulnerabilities—tailored to specific tech stacks and industries like healthcare or automotive. This allows users to select relevant parameters and receive precise, context-specific risk remediation costs. Unlike traditional methods, this is not a static effort estimation but the result of a carefully designed machine learning process that adapts to various factors, ensuring that cost estimates are both accurate and dynamically adjusted to the specific needs of the project.

This advanced approach provides a practical, actionable framework for quantifying the cost of addressing technical debt, helping organizations maintain a manageable debt level while aligning efforts with their unique industry requirements.

Analyzing Commits History

We conducted an extensive analysis of the complete commit history for each selected open-source AI framework. By comparing the number of bug fix commits with new feature development commits, we identified trends and calculated the standard deviation to gain deeper insights into the development focus over time. This analysis was crucial in determining whether a project is continuously accumulating technical debt or actively working to reduce it.

Our findings revealed a significant correlation between the level of technical debt and the patterns observed in commit histories. Frameworks with low technical debt (below 10%) exhibited a consistent and balanced ratio of bug fixes to feature development commits. In these cases, the variation in this ratio was minimal, indicating a highly stable development process. This stability allows for more accurate planning and budgeting for product extensions and modifications.

For example, frameworks like TensorFlow and OpenCV exhibit a balance between bug fixes and new feature development (1:3) with a very low standard deviation (<4%) throughout their history. This facilitates development planning and allows an increase in feature development without a negative impact on technical debt.

Conversely, codebases with high technical debt, such as FastAI and Theano, exhibit significant difficulties in maintaining a balance between bug fixes and new feature development.?

For FastAI and Theano, new development became increasingly unpredictable, with a significant portion of development capacity consumed by bug fixes. As the standard deviation in their bug fix-to-feature development ratio rose to 21% and 41%, respectively, this variability led to an unsustainable development process. Ultimately, this instability contributed to the end-of-life (EOL) status for both projects, as their commit histories clearly indicate.

Conversely, codebases with high technical debt demonstrated a much more erratic bug fixes to feature development ratio, with significant variability. This unpredictability makes it challenging to plan and budget for future development accurately, as the cost of extensions and modifications becomes difficult to forecast. As Tiset emphasizes, regular refactoring and updates are essential for managing inadvertent technical debt. Our commit history analysis supports this view, showing that frameworks with disciplined, consistent development practices are better positioned to manage technical debt and maintain a stable, predictable development process.

Correlation of Technical Debt with Maintainability

In the final phase of our analysis, we correlated the technical debt classification with average development costs, establishing clear connections between the level of technical debt and the overall maintainability of the framework. This provided valuable insights into how technical debt impacts long-term development efficiency. Our findings suggest that technical debt calculation should be combined with commit history analysis to ensure an accurate risk mitigation strategy and a reliable future development plan.

For instance, codebases like NVIDIA's Deep Learning and Scikit-learn, which rank in the middle of our analysis, illustrate this point well. While both have acceptable levels of technical debt, their development histories tell different stories. NVIDIA's core development was largely completed by mid-2023, which could signal a plateau in new feature development, making future planning more challenging. In contrast, Scikit-learn exhibits a more balanced development activity history, allowing for more predictable and manageable planning for future development.

Our Methodology Beyond Identification

Our methodology goes beyond merely identifying technical debt; it also pinpoints the specific factors that contribute most to high debt levels and should be prioritized for improvement. By providing this targeted approach, we can recommend the optimal level of technical debt that should be maintained to ensure a smooth and efficient development and maintenance process. As Tiset highlights, the consequences of unmanaged technical debt—such as slower development speeds, customer dissatisfaction, and financial losses—underscore the critical importance of proactive management. In today’s competitive landscape, where product benchmarks are constantly shifting, companies can easily find themselves in a perpetual struggle, reminiscent of Don Quixote tilting at windmills. Our approach offers a clear strategy to avoid this by focusing on the most impactful areas, enabling companies to streamline their development efforts and maintain their competitive edge.

Conclusion

Through our detailed technical debt analysis of widely-used AI frameworks, we have established a methodology that can be applied to any software product. Our approach not only identifies and quantifies technical debt but also connects it directly to maintenance and development costs, offering a clear path to managing and mitigating the risks associated with technical debt. This adaptable methodology ensures that organizations across various industries can apply these insights to their unique challenges, preventing technical debt from hindering their growth and innovation, and ensuring their AI systems remain robust and adaptable in the face of future challenges.

Quick Takeaways

• Strategic Technical Debt Management: Implementing a structured approach to identify, quantify, and prioritize technical debt is essential for maintaining the long-term health of AI frameworks and ensuring sustainable development.
• AI Evaluates AI Codebases!: We leverage OpenAI's ChatGPT-4 to precisely estimate the effort needed to address various issues, tailored to specific tech stacks and industries. Additionally, we employ machine learning models to identify patterns in technical debt and commit history.
• Dynamic Cost Estimation: Leveraging machine learning models allows for accurate and context-specific cost estimates, enabling organizations to efficiently allocate resources and plan for risk remediation.
• Importance of Consistent Development Practices: Regular refactoring and a balanced focus on bug fixes and feature development are key to managing technical debt effectively and ensuring predictable, stable project outcomes.
• Targeted Improvements: Focus on addressing the most impactful areas of technical debt to avoid the pitfalls of unchecked debt, such as reduced development speed, customer dissatisfaction, and financial losses.
• Adaptable Methodology: The methodology presented can be tailored to various industries and product types, making it a versatile tool for managing technical debt across different organizational contexts.

References

Tiset, Sylvain. "Technical Debt: From a Nightmare to an Old Memory." Published on Medium, May 2, 2024.

"Demystifying digital dark matter: A new standard to tame technical debt" McKinsey, June 2022

“The AI Time Bomb: The Hidden Risk No One's Talking About (Part I)” Aug, 2024

A Lesson from the Global IT Outage Caused by the CrowdStrike Falcon Update: The Importance of Continuous Source Code Audits in SDLC!, LinkedIn, July 2024

Cracking the Code: DePIN Ecosystem Source Analysis VS Financial Success, LinkedIn, May 2024, https://www.dhirubhai.net/pulse/cracking-code-depin-ecosystem-source-analysis-vs-success-voliotis-996mf/?trackingId=6O6Ni35%2FSK6os6dqYDuyJg%3D%3D