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Generative AI-Based Source Code Analysis and Automated Technical Issue Detection for Software Code Analysis Applications

Description

This project is about building a Generative AI-based Software Code Analysis Application that automatically analyzes source code and identifies technical issues. The application uses AI to understand code, detect potential bugs, code-quality problems, security concerns, and suggest possible improvements. The application provides developers with centralized analysis results so they can identify and fix issues before the code is deployed.

Aim

To design and implement a Generative AI-based Software Code Analysis Application that automatically analyzes source code, detects technical issues, and provides useful recommendations to developers using cloud-based AI and machine learning technologies.

Objectives

01 Automatically analyze source code using Generative AI.
02 Detect bugs, code-quality issues, and potential security problems.
03 Identify problematic or inefficient code sections.
04 Generate explanations and recommendations for detected issues.
05 Provide centralized analysis results through an application interface.
06 Reduce manual code-review effort.
07 Improve software quality before deployment.
08 Maintain analysis history for future reference.

Application Workflow

01

Stage 1. User Login & Authentication

Process

The developer logs into the Software Code Analysis Application using registered credentials.

Tools
Keycloak Kubernetes
Implementation

The application sends the login request to Keycloak. Keycloak verifies the user's identity and provides access to the appropriate application functions.

02

Stage 2. Source Code Upload

Process

The developer uploads source code or provides a source-code repository for analysis.

Tools
Python PostgreSQL Kubernetes
Implementation

The application receives the source code, validates the project information, and stores the required project and analysis details in PostgreSQL.

03

Stage 3. Source Code Preprocessing

Process

The application prepares the source code before sending it to the AI analysis engine.

Tools
Python Python Libraries Kubernetes
Implementation

Python reads the source files, identifies the programming language, extracts relevant code sections, and prepares the code for AI-based analysis.

04

Stage 4. AI-Based Code Analysis

Process

The application analyzes the source code and identifies technical issues.

Tools
Python LLM/Generative AI Model Kubernetes
Implementation

Python sends the relevant code to the Generative AI model. The model analyzes the code and identifies issues such as potential bugs, poor coding practices, security concerns, and inefficient implementations.

05

Stage 5. Issue Classification & Recommendation

Process

The application organizes detected issues and generates recommendations for fixing them.

Tools
Python PostgreSQL
Implementation

Python processes the AI response, categorizes the identified issues based on severity and type, and stores the analysis results and recommendations in PostgreSQL.

06

Stage 6. Results & Dashboard

Process

The developer views the analysis results through the application dashboard.

Tools
Python PostgreSQL Kubernetes
Implementation

The application retrieves the stored analysis results from PostgreSQL and displays detected issues, severity, affected code sections, explanations, and recommended improvements.

07

Stage 7. Report Generation

Process

The developer generates a report containing the source-code analysis results.

Tools
Python PostgreSQL
Implementation

Python retrieves the analysis information from PostgreSQL, formats the results, and generates the required code analysis report.

08

Stage 8. Analysis History & Logout

Process

The developer reviews previous analysis results and logs out of the application.

Tools
PostgreSQL Keycloak Kubernetes
Implementation

The application retrieves previous analysis records from PostgreSQL. After completing the required activities, the developer logs out and the session is terminated through Keycloak.

Cloud Infrastructure and Tools

Generative AI Engine LLM / Generative AI Model

Analyzes source code, identifies technical issues, explains problems, and generates recommendations for improving the code.

Database PostgreSQL

Stores project information, source-code analysis results, issue details, recommendations, and analysis history.

Database Query Language SQL

Retrieves and filters project and analysis information from PostgreSQL.

Identity & Access Management Keycloak

Provides authentication and role-based access for developers using the application.

Container Orchestration Platform Kubernetes

Runs and manages the containerized Software Code Analysis Application and its supporting services.

Container Platform Docker

Packages the application and its dependencies into containers for consistent deployment.

Target Compute Infrastructure Cloud EC2 Instances

Provides the virtual servers used to run the self-hosted Kubernetes cluster and application services.

Server Operating System Ubuntu Server 24.04 LTS

Provides the Linux operating system for the EC2 servers.

Cloud Networking Cloud VPC

Provides the private network for the Kubernetes infrastructure and separates public and private application traffic.

Security Controls Cloud Security Groups + Network ACLs

Control network access and protect the application and Kubernetes infrastructure from unauthorized traffic.

Cloud Storage Cloud EBS

Provides persistent block storage for EC2 servers and required application data.

Implementation Process

01
Step 1 – Analyze the Application and Define AI Requirements
  • Review the source-code analysis application requirements and supported programming languages.
  • Define the types of technical issues that the AI should identify.
  • Select the appropriate Generative AI/LLM model.
  • Define AI analysis prompts, response formats, and severity levels.
  • Define required CPU, memory, storage, and AI-processing resources.
02
Step 2 – Create Cloud and Application Infrastructure
  • Create an cloud VPC with the required public and private subnets.
  • Create cloud EC2 servers using Ubuntu Server 24.04 LTS.
  • Configure cloud Security Groups, Network ACLs, and cloud EBS storage.
  • Install and configure the self-hosted Kubernetes cluster.
  • Configure application namespaces, networking, and resource limits.
03
Step 3 – Develop and Deploy the Code Analysis Application
  • Develop the application using Python.
  • Configure PostgreSQL for project and analysis-result storage.
  • Configure Keycloak for user authentication.
  • Integrate the Generative AI/LLM model with the Python application.
  • Package the application using Docker and deploy it to Kubernetes.
04
Step 4 – Implement AI Code Analysis
  • Configure Python to receive and preprocess source-code files.
  • Send relevant code sections to the Generative AI model.
  • Configure the AI model to identify bugs, quality issues, security concerns, and inefficient code.
  • Process and categorize AI-generated findings using Python.
  • Store analysis results and recommendations in PostgreSQL.
05
Step 5 – Test and Operate the Application
  • Upload sample source-code projects and perform analysis.
  • Verify that the AI identifies known technical issues correctly.
  • Verify that analysis results and recommendations are stored in PostgreSQL.
  • Test dashboard and report generation functions.
  • Monitor application performance and continuously improve AI prompts and analysis accuracy.

Proposed Solution

The proposed solution provides a Generative AI-based Software Code Analysis Application that automatically analyzes source code and identifies technical issues. Python preprocesses the code and communicates with the Generative AI/LLM, while PostgreSQL stores analysis results and Keycloak manages authentication. The application runs in Docker containers on Kubernetes hosted on cloud EC2. This solution helps developers detect code problems earlier, understand technical issues, and improve software quality with less manual code-review effort.

Benefits

Automated Code Analysis: AI automatically analyzes source code.
Issue Detection: Identifies bugs, quality problems, and security concerns.
AI Recommendations: Provides explanations and possible fixes.
Reduced Manual Review: Reduces repetitive code-analysis work.
Faster Problem Detection: Finds issues earlier in development.
Centralized Results: Stores analysis results and history in one place.
Improved Code Quality: Helps developers produce better-quality code.
Consistent Analysis: Applies the same analysis approach across projects.

Challenges

AI Accuracy: AI may sometimes produce incorrect or incomplete findings.
False Positives: Some reported issues may require manual verification.
Large Codebases: Analyzing large projects can require significant processing resources.
AI Cost: Frequent use of large AI models can increase infrastructure or API costs.
Data Privacy: Source code must be protected during AI-based analysis.
Prompt Management: AI prompts need continuous improvement for reliable results.
Model Performance: AI response time may increase for complex code analysis.
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