The podcast explores the potential of AI in code refactoring, discussing challenges and successes in improving code quality. They delve into the impact of AI adoption on code writing, understanding, and refactoring. The episode highlights the importance of maintaining code quality in older systems and using AI tools to navigate complex code structures. Strategies for enhancing success rates of refactorings and evaluating AI tools in software development are also discussed.
AI can boost code quality by refining refactoring success rates from 37% to potentially 90%.
Promoting code readability is essential for efficient comprehension and swift code modification, emphasizing its crucial role in software quality.
Deep dives
Challenges in Code Quality Measurement
Code quality is a multidimensional concept difficult to capture with a single metric. The development of a code health metric, incorporating a range of correlated metrics, aims to address this complexity. By examining 25 metrics that complicate code understanding, a method has been devised to categorize code health as healthy or unhealthy based on a combination of factors, facilitating insights into good versus poor quality code.
Importance of Code Readability for Development
Despite AI tools' emphasis on enhancing code throughput, the majority of developers' time is spent understanding existing code rather than writing new code. Readability plays a crucial role in code maintenance and modification. Human-centric readability standards become vital for fostering efficient code comprehension and enabling swift and effective code changes, underlining the importance of prioritizing readability for improved software quality.
The Impact of Code Quality on Business Operations
Code quality significantly influences business outcomes by facilitating quicker feature implementation, bug fixes, and adaptability to market changes. Scientific studies reveal that healthy code bases lead to a twofold increase in development pace and a 15-fold reduction in production defects. The correlation between healthy code and rapid software evolution underscores the substantial business advantages of maintaining high-quality code bases.
AI in Refactoring Challenges and Opportunities
AI presents opportunities in identifying and enhancing code quality but faces challenges in refactoring accurately. Testing AI-led refactoring revealed that only 37% of cases resulted in improved code health, with complexities arising in ensuring semantic equivalence and correctness. Integrating machine learning models with abstract syntax trees offers a promising path for refining AI's refactoring capabilities and mitigating risks associated with compositional code changes.
Can AI improve the quality of our code? A recent white paper published by code analysis company CodeScene — "Refactoring vs. Refuctoring: Advancing the state of AI-automated code improvements" — highlighted some significant challenges: in tests, AI solutions only delivered functionally correct refactorings 37% of the time. However, there are nevertheless opportunities. The white paper suggests it might be possible to dramatically boost the success rate of AI refactoring to 90%.
In this episode of the Technology Podcast, Adam Tornhill, CTO and Founder of CodeScene, joins Thoughtworks' Rebecca Parsons (CTO Emerita), Birgitta Böckeler (Global Lead for AI-assisted software delivery) and Martin Fowler (Chief Scientist and author of the influential Refactoring book) to discuss all things AI and code. From refactoring and code quality to the benefits and limitations of coding assistants, this is an essential conversation for anyone that wants to understand how AI is going to shape the way we build software.