Case Study

SDLC Analyzer

Backend system that analyzes software development lifecycle inputs and maps them to security, compliance, and engineering requirements.

Problem

Teams often struggle to translate high-level requirements into concrete security and compliance considerations during the SDLC. Important risks are discovered late, increasing cost and delivery time.

The challenge was to build a backend system that could analyze requirement inputs early and provide meaningful insights in a structured, repeatable way.

My Role & Responsibilities

• Designed backend APIs for requirement analysis and insight generation
• Integrated ML-based models for text analysis
• Structured the system using Clean Architecture principles
• Ensured secure handling of inputs and outputs

Architecture & Design Decisions

The system follows Clean Architecture to isolate domain logic from infrastructure concerns such as machine learning models and external services.

• API Layer: Accepts requirement inputs and exposes analysis results
• Application Layer: Coordinates analysis workflows
• Domain Layer: Represents SDLC concepts and rules
• Infrastructure: ML.NET models and external integrations

Security & Compliance Considerations

• Validated and sanitized user inputs before processing
• Restricted access to analysis endpoints
• Avoided exposing internal model details via APIs
• Centralized error handling for safe failure responses

Performance & Reliability

• Optimized request processing to avoid blocking operations
• Stateless API design for scalability
• Clear separation between analysis and transport layers

Future Improvements

• Add asynchronous processing for heavy analysis workloads
• Introduce caching for repeated analysis inputs
• Enhance observability with structured logging
• Expand model support for additional SDLC stages

SDLC Analyzer reflects my ability to combine backend engineering with analytical and security-focused thinking.