Advanced Source Code Analysis Using Generative AI: A Comprehensive Approach

Photo by Igor Omilaev on Unsplash

Introduction

As the size and complexity of software projects continue to grow, traditional methods of source code analysis are often insufficient to handle the demands of modern development. Generative AI (GenAI) offers a revolutionary approach to this challenge by automating the process of understanding, analyzing, and querying large codebases. This blog delves into a project that leverages GenAI to conduct advanced source code analysis, exploring the key concepts, methodologies, and tools used to achieve this.

The project focuses on analyzing source code from GitHub repositories using a combination of GenAI models and natural language processing (NLP) techniques. The primary goal is to automate the extraction of meaningful information from codebases, enabling developers to gain insights into the structure, functionality, and intricacies of the code with minimal manual effort.

Key Components:

1. Repository Cloning: Automatically clone the target repository for analysis.
2. File Loading and Parsing: Load and parse Python files from the repository.
3. Chunking: Split the code into manageable pieces while preserving context.
4. Embedding Generation: Create vector representations of code snippets using embedding models.
5. Knowledge Base Creation: Store embeddings in a vector database for efficient retrieval.
6. Querying with LLM: Use a language model to answer specific questions related to the code.

1. Repository Cloning

The first step in the process is to clone the target GitHub repository. This allows us to work with the latest version of the code and ensures that the analysis is performed on the full codebase. In this project, the repository is cloned using the gitpython library, which provides a convenient interface for interacting with Git repositories programmatically.

from git import Repo

repo_path = "test_repo/"
Repo.clone_from("https://github.com/kshitijkutumbe/Visa-Sanction-Prediction.git", repo_path)

2. File Loading and Parsing

Once the repository is cloned, the next step is to load and parse the relevant Python files. This is crucial for understanding the structure and content of the code. The GenericLoader is used to load files from the specified directory, with the LanguageParser applied to parse Python files based on specific thresholds. This ensures that only files meeting certain criteria are processed, optimizing the analysis.

from langchain.document_loaders import GenericLoader
from langchain.parsers import LanguageParser
from langchain.text_splitter import Language

loader = GenericLoader.from_filesystem(
    repo_path+'/us_visa_prediction/pipeline',
    glob="**/*",
    suffixes=[".py"],
    parser=LanguageParser(language=Language.PYTHON, parser_threshold=500)
)
documents = loader.load()

Explanation of Concepts:

• GenericLoader: This component is responsible for loading documents (in this case, Python files) from the file system. It uses a specified pattern (glob) to match files, allowing for flexible selection based on file structure.
• LanguageParser: The LanguageParser is crucial for extracting meaningful content from the files. It parses the code according to the specified programming language (Python in this case) and applies a threshold to filter out unnecessary details.
• Parser Threshold: This parameter sets a limit on what is considered relevant code. For example, a threshold of 500 might mean that only code blocks with more than 500 characters are included, ensuring that trivial or irrelevant code snippets are excluded from the analysis.

3. Chunking the Code

With the files loaded and parsed, the next step is to split the code into smaller, manageable chunks. This is done using the RecursiveCharacterTextSplitter, which is designed to preserve the contextual meaning of the code while ensuring that each chunk is of a suitable size for further processing. This step is particularly important when dealing with large files, as it helps maintain the relevance of the analysis by keeping the chunks contextually coherent.

documents_splitter = RecursiveCharacterTextSplitter.from_language(
    language=Language.PYTHON,
    chunk_size=200,
    chunk_overlap=50
)
texts = documents_splitter.split_documents(documents)

Explanation of Concepts:

• RecursiveCharacterTextSplitter: This tool is designed to split documents (or code) into smaller pieces. It operates recursively, meaning it can break down large sections of code into increasingly smaller parts until the desired chunk size is achieved.
• Chunk Size and Overlap: The chunk_size defines the maximum length of each chunk, while chunk_overlap ensures that there is some overlap between consecutive chunks. This overlap is crucial for maintaining context, especially when analyzing sequential or related pieces of code.

4. Generating Embeddings

After chunking, each code snippet is converted into a vector representation using an embedding model. Embeddings are numerical representations of the code that capture its semantic meaning, making it possible to perform advanced searches and comparisons. In this project, the OpenAIEmbeddings model is used to generate embeddings for each chunk of code.

from langchain.embeddings import OpenAIEmbeddings

embeddings = OpenAIEmbeddings(disallowed_special=())

Explanation of Concepts:

• Embeddings: Embeddings are dense vector representations of text (or code) that capture its semantic meaning. They are essential for tasks like similarity search, clustering, and other forms of analysis that require understanding the content at a deeper level.
• OpenAIEmbeddings: This specific embedding model from OpenAI is designed to generate high-quality embeddings for a wide range of textual inputs, including code. It helps transform the code chunks into vectors that can be used in subsequent analysis.

5. Creating a Knowledge Base

The generated embeddings are then stored in a vector database, such as Chroma. This database serves as a knowledge base, allowing for efficient retrieval of relevant code snippets during the querying process. The use of a vector database enables sophisticated querying capabilities, where the model can search for and retrieve code snippets based on their semantic similarity.

from langchain.vectorstores import Chroma

vectordb = Chroma.from_documents(texts, embedding=embeddings, persist_directory='./data')

Explanation of Concepts:

• Vector Database: A vector database is a specialized database that stores data in vector format, enabling efficient similarity searches. In the context of this project, the vector database stores the embeddings of the code chunks, allowing for quick retrieval based on semantic similarity.
• Persistence: The persist_directory parameter indicates where the data should be stored. This ensures that the knowledge base can be reused across different sessions without needing to regenerate the embeddings.

6. Querying with an LLM

Finally, the project integrates a language model (LLM) to interact with the knowledge base. This allows users to ask questions about the code and receive human-like responses generated by the LLM. The ConversationalRetrievalChain is employed to handle both the retrieval of relevant code snippets and the generation of answers.

from langchain.chains import ConversationalRetrievalChain
from langchain.memory import ConversationSummaryMemory
from langchain.llms import ChatOpenAI

llm = ChatOpenAI()
memory = ConversationSummaryMemory(llm=llm, memory_key="chat_history", return_messages=True)

qa = ConversationalRetrievalChain.from_llm(llm, retriever=vectordb.as_retriever(search_type="mmr", memory=memory))

Explanation of Concepts:

• ConversationalRetrievalChain: This chain is designed to combine retrieval-based methods with conversational AI. It retrieves relevant chunks from the vector database and uses the LLM to generate coherent, contextually appropriate answers.
• Language Model (LLM): A language model like ChatOpenAI is used to process natural language queries and generate responses based on the retrieved code snippets. It adds a layer of human-like understanding to the analysis, making it easier to interact with the codebase.
• Memory Management: The ConversationSummaryMemory component ensures that the context of previous interactions is maintained, allowing for more coherent and context-aware conversations.

Example Use Case: Understanding a Training Pipeline

A practical application of this project is querying the codebase to understand specific components, such as a machine learning training pipeline. By asking the system what happens in the training pipeline, the LLM retrieves relevant code snippets and generates a detailed explanation.

question = "what is happening in training pipeline?"
result = qa(question)
print(result['answer'])

Conclusion

Integrating GenAI models into source code analysis represents a significant advancement in how developers can interact with and understand large codebases. By automating the analysis process and providing intuitive querying capabilities, this project demonstrates the potential of GenAI to enhance software development workflows, improve code quality, and facilitate maintenance. The detailed implementation covered in this blog highlights the power of GenAI in transforming traditional code analysis into a more efficient, accurate, and user-friendly process.

Future Work

Looking forward, there are several avenues for further development. Enhancing the parsing logic to better handle edge cases, integrating more domain-specific language models, and expanding the scope to include real-time code analysis are just a few possibilities. Additionally, integrating this solution directly into development environments could provide even more value by allowing developers to query their codebases without leaving their IDEs.

Feel free to connect on:

kshitijkutumbe@gmail.com