CodeReviewer ML Performance#
-Code review is a crucial aspect of the software development process, ensuring that code changes are thoroughly examined
+for quality, security, and adherence to coding standards. However, the code review process can be time-consuming, and
+human reviewers may overlook certain issues. To address these challenges, we have developed a Code Review Automation
+system powered by language models. Our system leverages state-of-the-art language models to generate code reviews automatically. These models are trained
+on a vast corpus of code and can provide insightful feedback on code changes. By automating part of the code review
+process, our system aims to: Speed up the code review process. Identify common code issues and provide recommendations. Assist developers in producing higher-quality code. Our system collects code review data from popular GitHub repositories. This data includes code changes and associated
+human-authored code reviews. By leveraging this data, our models learn to generate contextually relevant code reviews. We use pre-trained language models and fine-tune them on code review datasets. Fine-tuning allows the models to
+specialize in generating code reviews, making them more effective in this task. Once the models are trained, they can generate code reviews for new code changes. These generated reviews can highlight
+potential issues, suggest improvements, and provide feedback to developers. We use the BLEU-4 score metric to assess the quality of generated code reviews. This metric measures the similarity
+between model-generated reviews and target human reviews. While our models provide valuable assistance, they are
+designed to complement human reviewers. To get started with our Code Review Automation system, follow these steps: Clone this repository to your local machine: Set up the required dependencies and environment (see Run the provided notebooks to explore data collection, model inference, and evaluation. Integrate the code review automation system into your development workflow. You can use our pre-trained models or
+fine-tune them on your specific codebase for even better results. This project is licensed under the Apache 2.0 License - see the LICENSE file for details. For any questions or inquiries, please contact inbox@alexkovrigin.me.
Code Review Automation with Language Models#
+
Overview#
+
+
+Key Features#
+1. Data Collection#
+2. Model Inference and Fine-Tuning#
+3. Evaluation Metrics#
+Getting Started#
+
+
+git clone https://github.com/waleko/Code-Review-Automation-LM.git
+cd Code-Review-Automation-LM
+
requirements.txt).License#
+Contact#
+
Additionally, we will be using the test data from [LLG+22] and their dataset on zenodo. This dataset is available at data/msg-test.csv.
Additionally, we will be using the test data from [LLG+22] and their dataset on zenodo. This dataset is available at data/msg-test.csv.
CodeReviewer ML Performance
+Code Review Automation with Language Models
@@ -207,7 +208,7 @@As we can see, the fine-tuned model performs slightly better than the HF model on all datasets.
-Nevertheless, the score is still pretty low (as the authors of [LLG+22] put it: “it is a hard task”).
+Nevertheless, the score is still pretty low (as the authors of [LLG+22] put it: “it is a hard task”).
@@ -863,7 +864,7 @@Quantitative Evaluation
diff --git a/objects.inv b/objects.inv
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diff --git a/search.html b/search.html
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+++ b/search.html
@@ -7,7 +7,7 @@
- Search - CodeReviewer ML Performance
+ Search - Code Review Automation with Language Models
@@ -146,7 +146,7 @@
-
CodeReviewer ML Performance
+Code Review Automation with Language Models
@@ -206,7 +207,7 @@