Tackling Duplicate Code: A Deep Dive

Oct 10, 2025 by ADMIN 37 views

Hey guys, let's talk about something that can be a real headache in software development: duplicate code. This isn't just about aesthetics; it's a serious issue that can slow you down, cause bugs, and generally make your life harder. We're going to dive deep into a specific instance where a tool, the Duplicate Code Detector, flagged some significant code duplication in a project. We'll break down what was found, why it's a problem, and, most importantly, how to fix it. Understanding and addressing duplicate code is key to writing clean, maintainable, and efficient code. It's a fundamental aspect of good software engineering practices, and it's something that every developer should be aware of and actively work to avoid. This article will provide you with the insights and actionable steps needed to tackle duplicate code head-on. Let's get started, shall we?

The Problem: Code Duplication Uncovered

So, what exactly did the Duplicate Code Detector find? Well, the analysis, specifically focusing on commit 41307d196b260695bd3570f6e327728a9176e655, revealed a significant amount of duplicate code across different files within the project. This duplication wasn't minor; it involved hundreds of lines of identical code in multiple locations. The tool identified the following patterns of code duplication that is a serious concern for maintainability and future development. It's like having to update the same instructions or prompts in two different places every time you make a change. This introduces a high risk of inconsistencies and errors. The more code you have, the higher the likelihood of bugs and, with duplicate code, this risk is doubled. In this case, the duplication occurred between files in the .github/ directory and the pkg/cli/templates/ directory. Let's get into the specifics of the duplication and understand why this is a problem for software development.

Pattern 1: Duplicated gh-aw Instructions

This is the first and most severe instance of duplication that was discovered. The analysis pointed out that the instruction files for gh-aw were duplicated across two different directories. The github-agentic-workflows.instructions.md file, which contains the instructions, was present in both .github/instructions/ and pkg/cli/templates/. The really scary part is that each instance of the file contains 1021 lines of identical code. This is a lot of code, and any updates would require changes across two separate files. If a change is made to one, it must also be replicated in the other, which introduces a risk of errors.

Pattern 2: Duplicated Shared Workflow Prompt Template

Next up, the analysis found that the prompt template for creating shared agentic workflow components was also duplicated. The file create-shared-agentic-workflow.prompt.md was present in both .github/prompts/ and pkg/cli/templates/, and it contained 355 lines of identical code. While the code duplication isn't as extensive as the first pattern, it still presents the same problem of duplicated efforts in editing, increasing the chance of errors and inconsistencies. The purpose of the prompt template is to create shared agentic workflow components, which are used to help in wrapping MCP servers using GitHub Agentic Workflows (gh-aw) with Docker best practices. Imagine the challenges in making changes or improvements.

Pattern 3: Duplicated Workflow Designer Prompt Template

Finally, the analysis highlighted the duplication of the workflow designer prompt template. The file create-agentic-workflow.prompt.md was found in both .github/prompts/ and pkg/cli/templates/, and each instance had 128 lines of identical code. This duplication affects any updates or changes made to the prompts used by the workflow designer, which is designed to streamline the creation and management of GitHub Agentic Workflows. This is another instance of unnecessary duplication that can lead to errors.

The Impact: Why Code Duplication Matters

So, why should we care about duplicate code? Well, it has a few significant impacts, guys. Firstly, it hurts maintainability. When you have the same code in multiple places, any updates or bug fixes need to be applied in all those locations. This increases the risk of forgetting to update one copy, leading to inconsistencies and bugs. Imagine having to fix a bug in a piece of code and then realizing you need to fix it in two or three other places as well. It's time-consuming and error-prone. Secondly, it increases the bug risk. Every time you copy and paste code, you're multiplying the chance of introducing new bugs. And if a bug is present in the original code, it will be replicated in all the copies. This can make debugging a nightmare, because you have to trace the issue through multiple instances of the same code. And finally, code bloat. Duplication increases the overall size of the repository, which can make it slower to load, search, and navigate. It makes it harder to understand the code and adds to the complexity of the project. In short, duplicate code makes your project harder to maintain, increases the risk of bugs, and bloats the codebase.

Recommendations: Solutions to the Problem

Now that we know the problem, let's talk about solutions. The good news is that there are several ways to address code duplication. The key is to reduce redundancy and centralize the source of truth for instructions and prompts. This will reduce the chances of errors and improve the maintainability of the code. The goal is to have a single source of truth for instructions and prompts, so any updates are automatically reflected everywhere they are needed. The following actions will help solve the problem.

Single Source for Instructions

One of the most effective solutions is to establish a single source for the instructions. Instead of having duplicate instruction files, you could store the main instructions in pkg/cli/templates/ and then either reference them, use a symbolic link, or embed them from the .github/ directory. This way, any changes to the instructions will automatically propagate to both the CLI templates and the GitHub prompts. This approach dramatically simplifies updates, as you only have to edit the instruction once.

Shared Prompt Template Loader

Another approach is to create a shared prompt template loader. This means generating the prompt files in .github/prompts/*.prompt.md from the Go template directory or using //go:embed to read a single file path. This means you only need to edit the prompt templates in one place. This also guarantees that the prompts used in the CLI and GitHub remain consistent. Imagine the benefits of being able to make a change to a prompt template and knowing that it is immediately reflected in all the instances where it is used. This approach will save you a lot of time and reduce the risk of making an error.

Implementation Checklist: Putting the Solutions into Action

Okay, guys, we've identified the problem, discussed the impact, and proposed some solutions. Now, let's get down to the practical steps of addressing the code duplication. Here's an implementation checklist to guide the refactoring process. Remember, the goal is to streamline the codebase, reduce the risk of errors, and make it easier to maintain. It's all about ensuring efficiency and best practices within the project. Let's dive into the specific steps required to implement these changes and improve the project's structure and maintainability.

[ ] Review duplication findings: Make sure you fully understand the extent of the code duplication, where it exists, and what files are affected. This step involves a careful examination of the analysis results provided by the Duplicate Code Detector tool. By thoroughly reviewing the findings, you can gain a clear picture of the specific instances of code duplication that need to be addressed. This step is critical for ensuring that the refactoring efforts are focused on the most significant areas of concern, and for prioritizing the tasks that will have the most impact on improving code quality and maintainability.
[ ] Prioritize refactoring tasks: Decide which of the duplication patterns to tackle first, based on the severity and impact of the duplication. This step involves analyzing the identified instances of code duplication to determine which ones require immediate attention and which ones can be addressed later. Prioritization should consider factors such as the number of lines of duplicated code, the frequency with which the duplicated code is used, and the potential consequences of any errors or inconsistencies in the duplicated code. This step helps in focusing efforts on the most critical areas, maximizing the benefits of the refactoring process.
[ ] Create refactoring plan: Develop a detailed plan that outlines how you'll implement the chosen solutions. This includes specifying the exact steps, the files you'll modify, and the testing procedures to ensure your changes don't break anything. A well-defined refactoring plan serves as a roadmap, providing a clear sequence of actions to be taken during the refactoring process. It helps to minimize the risk of introducing errors and ensures that the changes are made systematically and efficiently. The plan typically includes a breakdown of the tasks involved, the estimated time for each task, and a checklist of the necessary steps to be completed.
[ ] Implement changes: Apply the refactoring techniques (single source for instructions, shared prompt template loader, etc.) based on the refactoring plan. This involves modifying the code according to the chosen approach, such as creating shared instruction files or setting up a shared prompt template loader. This step involves the actual modification of the codebase to eliminate duplicated code and improve its structure and maintainability. It requires careful attention to detail, ensuring that the changes are implemented correctly and do not introduce any new errors or issues.
[ ] Update tests: Ensure that all relevant tests pass after implementing the changes. This step is very important for validating the impact of refactoring on code behavior. It ensures that the changes have not introduced any regressions or broken any existing functionality. By running tests after making changes, you can confirm that the refactored code behaves as expected and that any new issues can be identified early in the development process.
[ ] Verify no functionality broken: Once the changes are implemented and tests have been updated, verify that all the project's functionality is working as expected. This might involve manual testing or automated testing. This involves verifying that all the functionalities of the software are working correctly after refactoring. This step confirms that the refactored code behaves as expected, ensuring that there are no unexpected side effects or regressions that could have occurred during the refactoring process. By thoroughly verifying functionality, you can ensure the stability and reliability of the software.

Analysis Metadata: Understanding the Details

To wrap things up, let's take a quick look at the analysis metadata. This information provides context about the detection and the tools used. It can be helpful in understanding the scope and limitations of the analysis. Knowing the details of the analysis can help you understand how the duplicate code was detected. This includes the date of the analysis, the tool used, and the specific commit being analyzed.

Analyzed Files: The analysis covered a total of 3 files, which means the tool scanned these files for duplication. Understanding this context helps to focus on the refactoring on the most relevant files. This helps to prioritize refactoring efforts on the most critical files that are impacted.
Detection Method: The tool used Serena semantic code analysis. This method suggests that the tool identifies duplication based on the semantic similarity of code, which goes beyond simple line-by-line comparison. This ensures accurate identification of code duplication.
Commit: The specific commit under analysis was 41307d196b260695bd3570f6e327728a9176e655. This commit is the snapshot of the project at the time of the analysis. This commit is useful for understanding the changes made. This information helps to understand the specific changes that led to the code duplication.
Analysis Date: The analysis was performed on 2025-10-10T05:03:00Z. This provides a timestamp for when the analysis was conducted, useful for tracking changes over time. Understanding when the analysis was performed can help in tracking the changes over time.

Alright guys, that's a wrap! Tackling duplicate code can feel like a lot of work, but trust me, it's worth it. By taking these steps, you'll not only clean up your code but also make it easier to maintain, debug, and extend in the future. Keep it clean, keep it simple, and happy coding!