Experience The Power of JetBrains AI Assistant for Code Refactoring
Table of Contents
- Introduction
- Initial Experience with JetBrains AI Assistant
- Using a Different Approach
- The Impressive Results
- testing the Code
- Dealing with Inadequate Test Results
- The Importance of Having Tests
- Introducing Manual Modifications
- Refactoring for Improved Understandability
- Simplifying Code with the AI Assistant
- Exploring Functional and Terse Code
- The Stress of Working with AI
- Making Manual Adjustments
- Ensuring Code Consistency
- Fixing Dependencies
- Concluding Thoughts
- FAQ
🤖 Initial Experience with JetBrains AI Assistant
Duncan starts by sharing his experience with using JetBrains AI Assistant to generate a Package Diagram. While he appreciates the increased productivity it offers, he mentions that the resulting code does not meet his expectations. He also finds the suggested refactorings to be unhelpful. However, he decides to take a different approach and manually make modifications to the code to improve it.
🔄 Using a Different Approach
To address the issues he encountered with the code generated by the AI Assistant, Duncan manually examines the code and identifies areas for improvement. He begins by analyzing the code in detail and identifying what he doesn't like about it. He then asks the AI Assistant to fix these issues, which proves to be a successful tactic. Duncan is highly impressed by the improvements made by the AI Assistant.
🌟 The Impressive Results
Duncan is thoroughly impressed with the AI Assistant's ability to make the code more understandable and effective. He demonstrates the changes made to the code, such as removing duplication and restructuring the code logic. Despite encountering a few setbacks along the way, Duncan finds that the AI Assistant is able to provide valuable suggestions for improving the code.
✅ Testing the Code
Duncan acknowledges the importance of having tests for the code. Realizing that the diagram could change with future refactorings, he decides to ask the AI Assistant to write tests. However, the initially generated tests prove to be inadequate. Duncan attempts to provide Hints to the AI Assistant to generate better tests but eventually decides to write the tests manually. He concludes that writing the tests by HAND is a better option in this case.
❓ Dealing with Inadequate Test Results
Duncan encounters challenges in getting satisfactory tests from the AI Assistant. He discovers that the generated tests do not actually test the code and may even contain errors. Despite giving hints and clarifications to the AI Assistant, the results remain unsatisfactory. Duncan ultimately decides to write the tests himself to ensure accuracy and reliability.
🔍 The Importance of Having Tests
Duncan emphasizes the importance of having tests for code, especially when it comes to diagrams that can change with refactorings. He explains that without proper tests, it can be difficult to detect changes and potential issues. Duncan notes that even though generating tests with the AI Assistant may seem appealing, manual testing is essential to ensure the correctness of the code.
💡 Introducing Manual Modifications
Duncan decides to remove the AI Assistant and manually make modifications to the code. He starts by creating a new function to check the file contents. He refactors the code to eliminate duplication and improve readability. Duncan discusses the benefits and drawbacks of manual modifications, highlighting the importance of considering the consequences and thinking through the changes being made.
🔧 Refactoring for Improved Understandability
Duncan identifies areas of the code that need improvement for better understandability. He asks the AI Assistant to assist him in removing the duplication of file paths. The AI Assistant suggests using a constant, but Duncan prefers a parameter approach. He demonstrates the changes made to the code, ensuring that the resulting code is more concise and readable.
🔄 Simplifying Code with the AI Assistant
Despite some challenges in working with the AI Assistant, Duncan continues to explore the AI Assistant's capabilities. He asks the AI Assistant to simplify the code further and is impressed with the results. The AI Assistant suggests changes to eliminate unnecessary code and improve the overall structure. Duncan acknowledges the differences in coding styles but appreciates the AI Assistant's proficiency in refactoring to his preferred style.
😅 The Stress of Working with AI
Duncan expresses the stress and challenge he experiences while working with the AI Assistant. He compares the experience to pair programming with another developer who has different preferences and coding styles. Duncan finds it tiring to explain his intentions to the AI Assistant and interpret its responses correctly. He also notes that occasionally it's easier to make changes manually rather than relying on the AI Assistant.
🔧 Making Manual Adjustments
Duncan continues to manually adjust the code to Align with his preferences. He moves certain parts of the code to improve the structure and understandability. He shares his insights on the benefits and limitations of working with the AI Assistant, including the need for clear communication and understanding of its suggestions. Duncan highlights the importance of finding the right balance between manual modifications and utilizing the AI Assistant's capabilities.
🔄 Ensuring Code Consistency
Throughout his interactions with the AI Assistant, Duncan identifies inconsistencies in the code and aims to address them. He improves the code by addressing issues such as deprecations, exception handling, and code organization. Duncan emphasizes the importance of maintaining consistency and readability in the codebase.
📦 Fixing Dependencies
Duncan realizes that certain dependencies in the code need to be fixed. He examines the code base to identify dependencies that are not desired or misplaced. By moving certain packages and files to more appropriate locations, Duncan resolves these dependency issues. He demonstrates his approach to ensuring the codebase is clean and free from unwanted dependencies.
🎯 Concluding Thoughts
Duncan concludes his experience with the AI Assistant and the manual modifications made to the code. He acknowledges the impressive capabilities of the AI Assistant in refactoring code and making it more concise. However, he also notes the need for careful management, clear communication, and manual adjustments to ensure the code meets his expectations. Duncan highlights the potential of the AI Assistant as a powerful tool for developers but emphasizes the importance of understanding its limitations and maintaining human intervention when needed.
❓ FAQ
Q: Can the AI Assistant generate tests satisfactorily?
A: While the AI Assistant can generate tests, the quality and accuracy may vary. In Duncan's experience, the initially generated tests were inadequate, and he had to manually write tests to ensure correctness.
Q: How does the AI Assistant perform in Code Refactoring?
A: The AI Assistant demonstrates proficiency in code refactoring, especially in aligning the code with a preferred coding style. However, human intervention and manual adjustments may still be necessary to achieve the desired outcome.
Q: What challenges did Duncan face while working with the AI Assistant?
A: Duncan found it challenging to communicate his intentions to the AI Assistant accurately and interpret its responses correctly. The need for constant collaboration and adjustment caused some stress during the process.
Q: How does Duncan ensure code consistency while working with the AI Assistant?
A: Duncan manually examines the code for inconsistencies and makes adjustments to maintain consistency. He addresses issues such as deprecations, exception handling, and code organization to ensure a clean and coherent codebase.
Q: Does the AI Assistant handle all code modifications effectively?
A: While the AI Assistant has shown impressive capabilities, it may not always generate the desired code modifications. Duncan emphasizes the importance of careful consideration and manual adjustments to ensure the code aligns with expectations.
