As businesses increasingly rely on technology to drive growth, technical debt has become a major challenge for software development teams. A survey conducted across 200 technology companies stated that an average of 25-40% of a developer’s time is spent addressing technical debt. Additionally, 79% of tech leaders highlight it as a significant hurdle in achieving their business objectives.
AI is proving to be a valuable tool for companies looking to reduce technical debt and improve the quality of their software systems. Below, we’ll explore the ways that AI can help reduce technical debt and improve the health of your software systems.
Technical debt refers to the costs incurred from taking shortcuts in the development process to get a product or service out to market quickly. Think of it as a temporary fix, like masking tape on a small bathtub hole – sooner or later, it’s going to give away. Suboptimal code and system design can slow down development, increase the occurrence of bugs, and raise maintenance costs.
We’ve all faced situations where meeting a deadline requires sacrifices. Similarly, developers may sacrifice best practices to deliver a feature on time. While these trade-offs can seem worthwhile, they can lead to challenges in future development cycles. Common causes of technical debt include:
Taking shortcuts during development creates vulnerabilities that can escalate over time. If left unaddressed, technical debt can jeopardize the success of an entire project.
AI offers speed, consistency, scalability, and continuous monitoring advantages in software development. By harnessing the power of AI, you can automate various aspects of the development cycle. Here are a few examples of how AI can help minimize technical debt:
Automated code reviews: AI can automate code reviews to help identify and fix technical debt early on. It can also monitor and identify problem areas in your codebase, such as duplicate code.
Refactoring codebase: When you refactor code, you’re essentially improving your code without changing its functionality. AI can help you make small changes to your code's structure, readability, and maintainability.
Prioritized workflows: AI can predict which areas of the codebase are most likely to develop technical debt, which can help you prioritize development stages effectively.
Automated testing: AI can automate testing to help ensure new code does not introduce new technical debt.
Legacy system analysis: When it’s time for system migration, AI can analyze legacy systems to identify areas where technical debt can be reduced.
Task prioritization: AI can prioritize tasks related to technical debt, which helps developers address critical issues quickly.
Test case generation: AI-enabled test case generation automates SQL output checks against expected results in your test case generation. Based on the results, you can either modify the test case or keep it as-is, which can help lower technical debt.
Document maintenance: AI assists in managing document upkeep, including data collection, updates, sharing, and retrieval, supporting business outcomes and workflow progress.
Deployment automation generation: Automated deployment reduces debt while standardizing and streamlining code changes throughout software development lifecycle stages.
Migration to Kubernetes containers: AI combined with Kubernetes enables rapid and efficient deployment and management of containerized applications, further alleviating technical debt.
While AI has great potential for managing technical debt, it also brings challenges that require careful consideration. The lack of clear explanations regarding AI's decision-making processes can leave developers seeking greater transparency. Additionally, although AI automates numerous processes, human oversight and expertise remain essential for handling complex development scenarios.
To mitigate these risks, you should implement features that allow developers to understand and interpret AI-driven decisions. It’s also important to foster ongoing collaboration between AI systems and human developers to ensure effective code management.
Leveraging AI to manage technical debt is like having a helpful assistant – it boosts the speed and effectiveness of tasks like code analysis, document creation, and bug detection. Many large corporations are already benefiting from AI’s integration into the software development process, highlighting its potential to streamline operations.
As AI continues to advance, we can anticipate even greater enhancements in technical debt management. Developers and leaders must adopt AI responsibly, ensuring transparency and retaining human involvement in decision-making. Staying updated with the latest advancements will allow you to fully capitalize on AI’s capabilities in software development.
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If you’re looking to harness the power of AI to reduce your technical debt, contact Concord. Our team can consult on the strategy and deliver solutions that align with your business goals.
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