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## TL;DR
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PR-Agent uses an **asymmetric and dynamic context strategy** to improve AI analysis of code changes in pull requests.
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Qodo Merge uses an **asymmetric and dynamic context strategy** to improve AI analysis of code changes in pull requests.
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It provides more context before changes than after, and dynamically adjusts the context based on code structure (e.g., enclosing functions or classes).
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This approach balances providing sufficient context for accurate analysis, while avoiding needle-in-the-haystack information overload that could degrade AI performance or exceed token limits.
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@ -43,14 +43,14 @@ Pull requests often encompass multiple changes across many files, potentially sp
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- Increased context expands the token count, increasing processing time and cost, and may prevent the model from processing the entire pull request in a single pass.
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## Asymmetric and dynamic context
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To address these challenges, PR-Agent employs an **asymmetric** and **dynamic** context strategy, providing the model with more focused and relevant context information for each code change.
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To address these challenges, Qodo Merge employs an **asymmetric** and **dynamic** context strategy, providing the model with more focused and relevant context information for each code change.
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**Asymmetric:**
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We start by recognizing that the context preceding a code change is typically more crucial for understanding the modification than the context following it.
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Consequently, PR-Agent implements an asymmetric context policy, decoupling the context window into two distinct segments: one for the code before the change and another for the code after.
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Consequently, Qodo Merge implements an asymmetric context policy, decoupling the context window into two distinct segments: one for the code before the change and another for the code after.
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By independently adjusting each context window, PR-Agent can supply the model with a more tailored and pertinent context for individual code changes.
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By independently adjusting each context window, Qodo Merge can supply the model with a more tailored and pertinent context for individual code changes.
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**Dynamic:**
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# Overview - Impact Evaluation 💎
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Demonstrating the return on investment (ROI) of AI-powered initiatives is crucial for modern organizations.
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To address this need, PR-Agent has developed an AI impact measurement tools and metrics, providing advanced analytics to help businesses quantify the tangible benefits of AI adoption in their PR review process.
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To address this need, Qodo Merge has developed an AI impact measurement tools and metrics, providing advanced analytics to help businesses quantify the tangible benefits of AI adoption in their PR review process.
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## Auto Impact Validator - Real-Time Tracking of Implemented PR-Agent Suggestions
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## Auto Impact Validator - Real-Time Tracking of Implemented Qodo Merge Suggestions
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### How It Works
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When a user pushes a new commit to the pull request, PR-Agent automatically compares the updated code against the previous suggestions, marking them as implemented if the changes address these recommendations, whether directly or indirectly:
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When a user pushes a new commit to the pull request, Qodo Merge automatically compares the updated code against the previous suggestions, marking them as implemented if the changes address these recommendations, whether directly or indirectly:
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1. **Direct Implementation:** The user directly addresses the suggestion as-is in the PR, either by clicking on the "apply code suggestion" checkbox or by making the changes manually.
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2. **Indirect Implementation:** PR-Agent recognizes when a suggestion's intent is fulfilled, even if the exact code changes differ from the original recommendation. It marks these suggestions as implemented, acknowledging that users may achieve the same goal through alternative solutions.
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2. **Indirect Implementation:** Qodo Merge recognizes when a suggestion's intent is fulfilled, even if the exact code changes differ from the original recommendation. It marks these suggestions as implemented, acknowledging that users may achieve the same goal through alternative solutions.
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### Real-Time Visual Feedback
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Upon confirming that a suggestion was implemented, PR-Agent automatically adds a ✅ (check mark) to the relevant suggestion, enabling transparent tracking of PR-Agent's impact analysis.
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PR-Agent will also add, inside the relevant suggestions, an explanation of how the new code was impacted by each suggestion.
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Upon confirming that a suggestion was implemented, Qodo Merge automatically adds a ✅ (check mark) to the relevant suggestion, enabling transparent tracking of Qodo Merge's impact analysis.
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Qodo Merge will also add, inside the relevant suggestions, an explanation of how the new code was impacted by each suggestion.
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{width=512}
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### Dashboard Metrics
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The dashboard provides macro-level insights into the overall impact of PR-Agent on the pull-request process with key productivity metrics.
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The dashboard provides macro-level insights into the overall impact of Qodo Merge on the pull-request process with key productivity metrics.
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By offering clear, data-driven evidence of PR-Agent's impact, it empowers leadership teams to make informed decisions about the tool's effectiveness and ROI.
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By offering clear, data-driven evidence of Qodo Merge's impact, it empowers leadership teams to make informed decisions about the tool's effectiveness and ROI.
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Here are key metrics that the dashboard tracks:
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#### PR-Agent Impacts per 1K Lines
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#### Qodo Merge Impacts per 1K Lines
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{width=512}
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> Explanation: for every 1K lines of code (additions/edits), PR-Agent had on average ~X suggestions implemented.
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> Explanation: for every 1K lines of code (additions/edits), Qodo Merge had on average ~X suggestions implemented.
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**Why This Metric Matters:**
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1. **Standardized and Comparable Measurement:** By measuring impacts per 1K lines of code additions, you create a standardized metric that can be compared across different projects, teams, customers, and time periods. This standardization is crucial for meaningful analysis, benchmarking, and identifying where PR-Agent is most effective.
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1. **Standardized and Comparable Measurement:** By measuring impacts per 1K lines of code additions, you create a standardized metric that can be compared across different projects, teams, customers, and time periods. This standardization is crucial for meaningful analysis, benchmarking, and identifying where Qodo Merge is most effective.
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2. **Accounts for PR Variability and Incentivizes Quality:** This metric addresses the fact that "Not all PRs are created equal." By normalizing against lines of code rather than PR count, you account for the variability in PR sizes and focus on the quality and impact of suggestions rather than just the number of PRs affected.
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3. **Quantifies Value and ROI:** The metric directly correlates with the value PR-Agent is providing, showing how frequently it offers improvements relative to the amount of new code being written. This provides a clear, quantifiable way to demonstrate PR-Agent's return on investment to stakeholders.
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3. **Quantifies Value and ROI:** The metric directly correlates with the value Qodo Merge is providing, showing how frequently it offers improvements relative to the amount of new code being written. This provides a clear, quantifiable way to demonstrate Qodo Merge's return on investment to stakeholders.
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#### Suggestion Effectiveness Across Categories
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{width=512}
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> Explanation: This chart illustrates the distribution of implemented suggestions across different categories, enabling teams to better understand PR-Agent's impact on various aspects of code quality and development practices.
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> Explanation: This chart illustrates the distribution of implemented suggestions across different categories, enabling teams to better understand Qodo Merge's impact on various aspects of code quality and development practices.
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#### Suggestion Score Distribution
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{width=512}
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# Core Abilities
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PR-Agent utilizes a variety of core abilities to provide a comprehensive and efficient code review experience. These abilities include:
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Qodo Merge utilizes a variety of core abilities to provide a comprehensive and efficient code review experience. These abilities include:
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- [Local and global metadata](https://pr-agent-docs.codium.ai/core-abilities/metadata/)
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- [Dynamic context](https://pr-agent-docs.codium.ai/core-abilities/dynamic_context/)
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- [Self-reflection](https://pr-agent-docs.codium.ai/core-abilities/self_reflection/)
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- [Impact evaluation](https://pr-agent-docs.codium.ai/core-abilities/impact_evaluation/)
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- [Interactivity](https://pr-agent-docs.codium.ai/core-abilities/interactivity/)
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- [Compression strategy](https://pr-agent-docs.codium.ai/core-abilities/compression_strategy/)
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- [Code-oriented YAML](https://pr-agent-docs.codium.ai/core-abilities/code_oriented_yaml/)
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- [Static code analysis](https://pr-agent-docs.codium.ai/core-abilities/static_code_analysis/)
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- [Code fine-tuning benchmark](https://pr-agent-docs.codium.ai/finetuning_benchmark/)
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- [Local and global metadata](https://qodo-merge-docs.qodo.ai/core-abilities/metadata/)
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- [Dynamic context](https://qodo-merge-docs.qodo.ai/core-abilities/dynamic_context/)
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- [Self-reflection](https://qodo-merge-docs.qodo.ai/core-abilities/self_reflection/)
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- [Impact evaluation](https://qodo-merge-docs.qodo.ai/core-abilities/impact_evaluation/)
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- [Interactivity](https://qodo-merge-docs.qodo.ai/core-abilities/interactivity/)
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- [Compression strategy](https://qodo-merge-docs.qodo.ai/core-abilities/compression_strategy/)
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- [Code-oriented YAML](https://qodo-merge-docs.qodo.ai/core-abilities/code_oriented_yaml/)
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- [Static code analysis](https://qodo-merge-docs.qodo.ai/core-abilities/static_code_analysis/)
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- [Code fine-tuning benchmark](https://qodo-merge-docs.qodo.ai/finetuning_benchmark/)
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## Local and global metadata injection with multi-stage analysis
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(1)
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PR-Agent initially retrieves for each PR the following data:
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Qodo Merge initially retrieves for each PR the following data:
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- PR title and branch name
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- PR original description
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@ -9,10 +9,10 @@ PR-Agent initially retrieves for each PR the following data:
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- The entire content of the files that were modified in the PR
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!!! tip "Tip: Organization-level metadata"
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In addition to the inputs above, PR-Agent can incorporate supplementary preferences provided by the user, like [`extra_instructions` and `organization best practices`](https://pr-agent-docs.codium.ai/tools/improve/#extra-instructions-and-best-practices). This information can be used to enhance the PR analysis.
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In addition to the inputs above, Qodo Merge can incorporate supplementary preferences provided by the user, like [`extra_instructions` and `organization best practices`](https://qodo-merge-docs.qodo.ai/tools/improve/#extra-instructions-and-best-practices). This information can be used to enhance the PR analysis.
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(2)
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By default, the first command that PR-Agent executes is [`describe`](https://pr-agent-docs.codium.ai/tools/describe/), which generates three types of outputs:
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By default, the first command that Qodo Merge executes is [`describe`](https://qodo-merge-docs.qodo.ai/tools/describe/), which generates three types of outputs:
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- PR Type (e.g. bug fix, feature, refactor, etc)
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- PR Description - a bullet point summary of the PR
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These AI-generated outputs are now considered as part of the PR metadata, and can be used in subsequent commands like `review` and `improve`.
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This effectively enables multi-stage chain-of-thought analysis, without doing any additional API calls which will cost time and money.
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For example, when generating code suggestions for different files, PR-Agent can inject the AI-generated ["Changes walkthrough"](https://github.com/Codium-ai/pr-agent/pull/1202#issue-2511546839) file summary in the prompt:
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For example, when generating code suggestions for different files, Qodo Merge can inject the AI-generated ["Changes walkthrough"](https://github.com/Codium-ai/pr-agent/pull/1202#issue-2511546839) file summary in the prompt:
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```
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## File: 'src/file1.py'
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...
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```
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(3) The entire PR files that were retrieved are also used to expand and enhance the PR context (see [Dynamic Context](https://pr-agent-docs.codium.ai/core-abilities/dynamic-context/)).
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(3) The entire PR files that were retrieved are also used to expand and enhance the PR context (see [Dynamic Context](https://qodo-merge-docs.qodo.ai/core-abilities/dynamic-context/)).
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(4) All the metadata described above represents several level of cumulative analysis - ranging from hunk level, to file level, to PR level, to organization level.
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This comprehensive approach enables PR-Agent AI models to generate more precise and contextually relevant suggestions and feedback.
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This comprehensive approach enables Qodo Merge AI models to generate more precise and contextually relevant suggestions and feedback.
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## TL;DR
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PR-Agent implements a **self-reflection** process where the AI model reflects, scores, and re-ranks its own suggestions, eliminating irrelevant or incorrect ones.
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Qodo Merge implements a **self-reflection** process where the AI model reflects, scores, and re-ranks its own suggestions, eliminating irrelevant or incorrect ones.
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This approach improves the quality and relevance of suggestions, saving users time and enhancing their experience.
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Configuration options allow users to set a score threshold for further filtering out suggestions.
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Given that not all generated code suggestions will be relevant, it is crucial to enable users to review them in a fast and efficient way, allowing quick identification and filtering of non-applicable ones.
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To achieve this goal, PR-Agent offers a dedicated hierarchical structure when presenting suggestions to users:
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To achieve this goal, Qodo Merge offers a dedicated hierarchical structure when presenting suggestions to users:
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- A "category" section groups suggestions by their category, allowing users to quickly dismiss irrelevant suggestions.
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- Each suggestion is first described by a one-line summary, which can be expanded to a full description by clicking on a collapsible.
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Note that presenting all generated suggestions simultaneously provides the model with a comprehensive context, enabling it to make more informed decisions compared to evaluating each suggestion individually.
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To conclude, the self-reflection process enables PR-Agent to prioritize suggestions based on their importance, eliminate inaccurate or irrelevant proposals, and optionally exclude suggestions that fall below a specified threshold of significance.
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To conclude, the self-reflection process enables Qodo Merge to prioritize suggestions based on their importance, eliminate inaccurate or irrelevant proposals, and optionally exclude suggestions that fall below a specified threshold of significance.
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This results in a more refined and valuable set of suggestions for the user, saving time and improving the overall experience.
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## Example Results
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## Overview - Static Code Analysis 💎
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By combining static code analysis with LLM capabilities, PR-Agent can provide a comprehensive analysis of the PR code changes on a component level.
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By combining static code analysis with LLM capabilities, Qodo Merge can provide a comprehensive analysis of the PR code changes on a component level.
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It scans the PR code changes, finds all the code components (methods, functions, classes) that changed, and enables to interactively generate tests, docs, code suggestions and similar code search for each component.
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@ -13,7 +13,7 @@ It scans the PR code changes, finds all the code components (methods, functions,
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### Analyze PR
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The [`analyze`](https://pr-agent-docs.codium.ai/tools/analyze/) tool enables to interactively generate tests, docs, code suggestions and similar code search for each component that changed in the PR.
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The [`analyze`](https://qodo-merge-docs.qodo.ai/tools/analyze/) tool enables to interactively generate tests, docs, code suggestions and similar code search for each component that changed in the PR.
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It can be invoked manually by commenting on any PR:
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```
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/analyze
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@ -27,7 +27,7 @@ Clicking on each checkbox will trigger the relevant tool for the selected compon
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### Generate Tests
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The [`test`](https://pr-agent-docs.codium.ai/tools/test/) tool generate tests for a selected component, based on the PR code changes.
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The [`test`](https://qodo-merge-docs.qodo.ai/tools/test/) tool generate tests for a selected component, based on the PR code changes.
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It can be invoked manually by commenting on any PR:
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```
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/test component_name
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### Generate Docs for a Component
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The [`add_docs`](https://pr-agent-docs.codium.ai/tools/documentation/) tool scans the PR code changes, and automatically generate docstrings for any code components that changed in the PR.
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The [`add_docs`](https://qodo-merge-docs.qodo.ai/tools/documentation/) tool scans the PR code changes, and automatically generate docstrings for any code components that changed in the PR.
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It can be invoked manually by commenting on any PR:
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```
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/add_docs component_name
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@ -49,7 +49,7 @@ Or be triggered interactively by using the `analyze` tool.
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{width=768}
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### Generate Code Suggestions for a Component
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The [`improve_component`](https://pr-agent-docs.codium.ai/tools/improve_component/) tool generates code suggestions for a specific code component that changed in the PR.
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The [`improve_component`](https://qodo-merge-docs.qodo.ai/tools/improve_component/) tool generates code suggestions for a specific code component that changed in the PR.
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It can be invoked manually by commenting on any PR:
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```
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/improve_component component_name
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### Find Similar Code
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The [`similar code`](https://pr-agent-docs.codium.ai/tools/similar_code/) tool retrieves the most similar code components from inside the organization's codebase, or from open-source code.
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The [`similar code`](https://qodo-merge-docs.qodo.ai/tools/similar_code/) tool retrieves the most similar code components from inside the organization's codebase, or from open-source code.
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For example:
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