RRT Merge Conflict: Lab3 Q5 Vs Lab4 - How To Resolve?

by Alex Johnson 54 views

Introduction

In collaborative software development, especially within academic or research environments, managing code merges effectively is crucial. This article addresses a specific scenario encountered during the USC-Fall-2025-CSCI-545 course, specifically in the context of lab assignments involving motion planning with a 6-DOF manipulator (LUV Botics). The core issue revolves around conflicting requirements between Lab3 Q5 and Lab4 concerning the Rapidly-exploring Random Tree (RRT) algorithm. Understanding the nuances of this conflict and strategizing a resolution is vital for maintaining code integrity and ensuring smooth progress for all team members.

Identifying the Issue: Merged Code and Conflicting Lab Requirements

At the heart of the matter is a code merge that introduced modifications from Lab3 Q5 into the main branch. These modifications include the addition of the _get_random_sample_near_goal() function and the implementation of goal-bias sampling logic within the RRT algorithm. While these enhancements were pertinent to Lab3 Q5, they pose a problem for Lab4. The Lab4 version of adarrt.py does not define _get_random_sample_near_goal(). This discrepancy leads to runtime errors when the current main branch is executed in the context of Lab4, specifically when reaching the RRT section in Q4.

Furthermore, a clarification from the instructor on Piazza explicitly states that Lab4 should utilize the original Lab4 version of adarrt.py. This directive implies that goal-bias sampling and the _get_random_sample_near_goal() function should not be included in Lab4. Therefore, the merged changes from Lab3 Q5 inadvertently introduce incompatibility issues for Lab4, necessitating a resolution strategy.

Understanding the Implications of the Conflict

This situation highlights a common challenge in collaborative coding environments: the need to manage code changes across different branches or versions of a project. Without careful coordination, features or modifications intended for one part of a project can inadvertently disrupt other parts. In this case, the merge of Lab3 Q5 features into the main branch, while beneficial for that specific task, created a conflict with the requirements of Lab4. This conflict underscores the importance of:

  • Clear communication: Ensuring that all team members are aware of changes and their potential impact.
  • Version control: Utilizing Git branching strategies to isolate features and changes.
  • Testing: Implementing thorough testing procedures to identify integration issues early on.

The consequences of not addressing this conflict could include:

  • Runtime errors: Lab4 code failing to execute correctly due to missing functions.
  • Incorrect results: The RRT algorithm behaving differently than intended in Lab4 due to the presence of goal-bias sampling.
  • Wasted time: Team members spending time debugging issues that stem from the code conflict rather than focusing on the core objectives of Lab4.

Proposed Solutions: Two Paths to Resolution

To navigate this challenge and ensure the successful completion of Q4–Q7, two primary solutions have been proposed. Each approach has its own merits and potential drawbacks, and the optimal choice depends on the specific context of the project and the team's preferences.

Option 1: Reverting the Lab3 Goal-Bias Changes

The first proposed solution involves reverting the changes introduced from Lab3 Q5. This would essentially undo the merge that added the _get_random_sample_near_goal() function and goal-bias sampling logic to the main branch. This approach has the advantage of directly addressing the conflict by removing the problematic code from the main branch. This ensures that Lab4 can operate as intended, using the original adarrt.py version without the extraneous features.

However, reverting changes can have implications for other parts of the project that may rely on the merged code. If any other functionalities or tasks have been built upon the Lab3 Q5 modifications, reverting those changes could introduce new issues or regressions. Therefore, it is essential to carefully assess the potential impact of a revert before proceeding. This assessment should involve:

  • Identifying dependencies: Determining if any other code components or features rely on the _get_random_sample_near_goal() function or goal-bias sampling logic.
  • Communicating with the team: Discussing the proposed revert with all team members to gather input and identify potential conflicts.
  • Testing: Performing thorough testing after the revert to ensure that no new issues have been introduced.

Option 2: Creating a Follow-Up Fix PR

The second proposed solution involves creating a follow-up Pull Request (PR) that specifically restores the clean Lab4 version of adarrt.py. This approach entails introducing a new set of changes that overwrite the merged Lab3 Q5 modifications, effectively bringing the adarrt.py file back to its original state for Lab4. This method offers a more targeted approach, addressing the conflict without necessarily undoing other changes that may have been merged into the main branch.

The advantage of this approach lies in its precision. It allows the team to address the specific issue without affecting other parts of the codebase. This can be particularly beneficial if there are concerns about the potential impact of a full revert. Furthermore, a follow-up fix PR provides a clear record of the resolution, making it easier to understand the changes that were made and why.

However, this approach also has its considerations. It adds complexity to the project history, as it introduces a new set of changes that essentially undo previous ones. This can make it more challenging to track the evolution of the code over time. Additionally, it requires careful attention to detail to ensure that the fix PR accurately restores the Lab4 version of adarrt.py without introducing any new issues.

Recommendation and Next Steps

Given the information available, a strong recommendation leans towards creating a follow-up fix PR that restores the clean Lab4 version of adarrt.py. This approach appears to be the most targeted and least disruptive way to resolve the conflict. By specifically addressing the adarrt.py file, the team can avoid potential regressions that might arise from a full revert. However, the final decision should be made collaboratively, considering the specific context of the project and the team's expertise.

To proceed effectively, the following steps are recommended:

  1. Team Discussion: Initiate a discussion among team members to weigh the pros and cons of each solution and arrive at a consensus.
  2. Implement the Chosen Solution: Based on the team's decision, either revert the changes or create a follow-up fix PR.
  3. Thorough Testing: Conduct comprehensive testing to ensure that the chosen solution resolves the conflict without introducing new issues.
  4. Documentation: Document the resolution process and the rationale behind the chosen approach to provide context for future reference.

Conclusion

Resolving code conflicts effectively is a critical skill in collaborative software development. The scenario presented here, involving conflicting requirements between Lab3 Q5 and Lab4, highlights the importance of clear communication, version control, and careful consideration of the potential impact of code changes. By thoughtfully evaluating the proposed solutions and following a systematic approach, the team can successfully navigate this challenge and ensure the smooth progress of their project. Remember, collaborative coding is not just about writing code; it's about communicating, coordinating, and working together to build robust and reliable software.

For more information on version control and collaborative coding best practices, consider exploring resources like the Git documentation.