Tracing Domain Services in Application Code using Generative AI
Revolutionizing Software Traceability with Advanced AI
This paper introduces a novel GenAI-driven approach to semantically match domain model services with application classes, significantly enhancing software modernization and documentation efforts. By leveraging context-aware GenAI, it bridges the gap between high-level business abstractions and low-level code implementations, improving traceability and reducing manual effort. Experimental results show a substantial increase in f1 score and consistent gains in precision and service-level recall compared to baselines.
Transforming Enterprise AI Outcomes
Our analysis reveals the profound impact of strategic AI implementation on key business drivers.
0%
F1 Score Increase
0%
Service-Level Recall (OF)
0%
Average F1 Score (O)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Introduction & Problem
Methodology
Evaluation & Results
Introduction & Problem
The introduction outlines the critical challenge of tracing domain services in complex application code, highlighting the limitations of traditional methods and the necessity for AI-driven solutions to improve traceability, integration, and modernization.
$2.5M
Average annual cost of poor traceability in large enterprises.
Methodology
This section details the innovative Generative AI-driven approach, from identifying domain services and application classes to contextual representation using LLMs, semantic matching, and expert feedback-based refinement.
Enterprise Process Flow
Domain Services Identification
→
Application Classes Identification
→
Contextual Representation (GenAI)
→
Semantic Matching
→
Optimization
→
Expert Feedback Refinement
| Feature | Traditional VSM | GenAI (Proposed) |
|---|---|---|
| Context Awareness |
|
|
| Documentation Dependency |
|
|
| Scalability |
|
|
| Accuracy |
|
|
Evaluation & Results
The evaluation section presents a case study on an open-source ERP system, detailing experimental setup across different modes and discussing the superior performance of the GenAI approach in precision and recall.
Case Study: JAllInOne ERP System
A real-world case study on the JAllInOne open-source ERP application, using 40 application classes and 18 domain services, validated the effectiveness of the GenAI approach.
Challenge: Manual matching was time-consuming and error-prone due to independent development and poor documentation.
Solution: Implemented context-aware GenAI to infer domain intents and establish semantic matches.
Outcome: Achieved +20% F1 score improvement and 100% service-level recall with expert feedback.
100%
Precision at k=1 with expert feedback.
Calculate Your Potential ROI
See how leveraging advanced AI for traceability can translate into significant operational savings for your enterprise.
Your AI Implementation Roadmap
A structured approach to integrating advanced AI into your software development lifecycle.
Phase 1: Discovery & Assessment
Initial analysis of your existing systems, domain models, and application codebase. Identify key integration points and define project scope.
Phase 2: AI Model Integration
Deployment and fine-tuning of Generative AI and embedding models with your specific enterprise context. Establish semantic matching pipelines.
Phase 3: Validation & Refinement
Iterative testing and expert feedback incorporation to achieve high precision and recall. Validate traceability links against business requirements.
Phase 4: Operationalization & Scaling
Integrate the AI-driven traceability solution into your CI/CD pipelines and scale across various projects. Provide ongoing support and monitoring.
Ready to Transform Your Traceability?
Connect with our AI experts to discuss a tailored strategy for your enterprise and unlock new levels of software understanding.
Ready to Get Started?
Book Your Free Consultation.
Let's Discuss Your AI Strategy!
Lets Discuss Your Needs
Select a Date
Sun
Mon
Tue
Wed
Thu
Fri
Sat