The core methodology of this prototype centers around leveraging large language models (LLMs) to perform intelligent code analysis. This approach allows for the evaluation of complex, context-dependent guidelines that are challenging to assess using traditional static analysis tools.
The system's foundation is built on a flexible, JSON-based rule configuration that defines guidelines, their applicability criteria, and evaluation patterns. This configuration allows for easy management and updating of the guideline set, ensuring the system can evolve with changing best practices.
Upon receiving a code repository, the prototype begins by analyzing its structure to identify relevant files and determine which guidelines are applicable based on the project's characteristics. This initial analysis sets the stage for a more targeted and efficient evaluation process.
One of the key features in this approach is the distinction between "simple" rules that can be evaluated on individual files and "context" rules that require information from multiple files. This differentiation allows for more sophisticated guideline evaluations that consider project-wide patterns and structures, going beyond the capabilities of traditional line-by-line analysis tools.
flowchart TD
A[Repository Ingestion] --> B[Repository Analysis]
B --> C{Rule Type}
C -->|Simple Rules| D[File-by-File Evaluation]
C -->|Context Rules| E[Context Aggregation]
E --> F[Multi-File Evaluation]
D --> G[AI Model Evaluation]
F --> G
G --> H[Findings Generation]
At the heart of the code review process is the AI-powered evaluation engine. Leveraging Amazon Bedrock, the prototype uses advanced language models to assess code against the defined guidelines. This enables the evaluation of nuanced, semantic aspects of code quality that are difficult to capture with traditional rule-based systems. The AI models' natural language understanding capabilities allow them to interpret guideline descriptions and apply them to code in a way that mimics human expert judgment.
To optimize performance and cost-efficiency, the system implements Bedrock batch inference. This allows for parallel evaluation of multiple files and guidelines, significantly reducing the overall processing time.
flowchart LR
A[Repository] --> B[File Grouping]
B --> C[Batch 1]
B --> D[Batch 2]
B --> E[Batch 3]
C --> F[AI Evaluation]
D --> F
E --> F
F --> G[Results Aggregation]
G --> H[Final Report]
The prototype supports two primary AI models through Amazon Bedrock: Anthropic's Claude 3+ and Amazon's Nova. These models were chosen for their strong natural language understanding capabilities and their ability to comprehend and reason about code structures. Importantly, the system is designed with flexibility in mind, allowing for the addition of new models through a ModelAdapter interface. This extensibility ensures that the prototype can evolve alongside advancements in AI technology.
During the development process, one of the main challenges faced was balancing the need for comprehensive code analysis with the practical limitations of AI model context windows. To address this, the system implements a multi-tiered approach. For simple rules, individual files are evaluated in isolation. For context rules, related files are included in the prompt to the AI model.
The prototype leverages several AWS services to create a scalable, serverless architecture that can handle code reviews efficiently and cost-effectively. At the core of the system is Amazon S3, which serves multiple crucial roles. It acts as the storage solution for input code repositories, allowing development teams to easily submit their projects for review. Additionally, S3 houses the configuration files that define the guidelines and rules for code evaluation. Once the review process is complete, S3 also stores the output findings reports, making them readily accessible for further analysis or integration with other tools.
AWS Lambda functions play a vital role in handling various processing tasks throughout the review pipeline. These serverless functions are responsible for initial repository analysis, determining which guidelines are applicable to a given project, and aggregating findings from multiple evaluations. The use of Lambda ensures that the system can scale seamlessly to handle varying workloads without requiring manual intervention or resource management.
At the heart of the AI-powered code analysis is Amazon Bedrock, which provides access to state-of-the-art language models. Bedrock allows the system to leverage powerful AI capabilities without the need for managing complex ML infrastructure, enabling the prototype to focus on its core code review functionality.
The data and process flow within this architecture begins when code repositories are uploaded to the S3 input bucket. The developer starts a Step Functions workflow, initiating the analysis process. A Lambda function then analyzes the repository structure to determine which guidelines are applicable based on the project's characteristics. Bedrock batch inference is then used to perform the AI-based code analysis, applying the selected guidelines to the prepared code snippets. The results from these AI evaluations are aggregated and processed by Lambda functions, which compile the findings into a structured format. Finally, a comprehensive findings report is generated and stored in the S3 output bucket, ready for review by the development team.
This architecture ensures a scalable, efficient, and cost-effective solution for performing expert code reviews across a wide range of projects and guidelines.
The prototype harnesses the power of large language models accessed through Amazon Bedrock to perform sophisticated code analysis. Currently, the system supports two primary model families: Anthropic's Claude 3+ and Amazon's Nova. These models were selected for their exceptional natural language understanding capabilities and their ability to comprehend and reason about complex code structures.
The system's interaction with these models is facilitated through a carefully crafted prompting strategy. This strategy begins with a base prompt template that sets the stage for the code review task, providing the AI with necessary context and instructions. For each evaluation, this template is then dynamically populated with the specific code to be reviewed, the relevant guidelines to evaluate, and any necessary context from other files (particularly important for context rules). This approach ensures that the AI model has all the information it needs to perform a thorough and accurate code review.
flowchart TD
A[Base Template] --> E[Final Prompt]
B[Code Snippet] --> E
C[Applicable Guidelines] --> E
D[Context Information] --> E
E --> F[AI Model]
The prompts themselves are engineered with several key objectives in mind. First, they provide clear and unambiguous instructions on the evaluation task, ensuring that the AI model understands exactly what is expected. Second, they specify the required format for the output, utilizing a JSON schema to enforce structure and consistency in the AI's responses. This structured output is crucial for efficient parsing and integration of the AI's findings into the broader code review system. Finally, the prompts include guidance on how to approach the analysis, encouraging the AI to consider both compliant and non-compliant aspects of the code. This balanced approach helps ensure a comprehensive review that not only identifies issues but also recognizes good coding practices.
To accommodate the unique characteristics and capabilities of different AI models, the prototype implements a ModelAdapter interface. This abstraction layer allows for customized interaction with each supported model, enabling the system to leverage model-specific features or work around particular limitations. Moreover, this modular design facilitates the easy addition of new models as they become available, ensuring that the prototype can evolve alongside advancements in AI technology.
Following is the rule evaluation sequence:
sequenceDiagram
participant FM as FileManager
participant RD as RuleDetector
participant RM as RuleMapper
participant ER as EvaluateRules
participant BB as BedrockBatchInputProcessor
FM ->> RD: Provide file list
RD ->> RM: Determine applicable rules
RM ->> ER: Map rules to files
ER ->> BB: Prepare batch input
BB ->> Bedrock: Submit batch job
Bedrock -->> ER: Return results
ER ->> S3: Store findings