SDLC Knowledge Fabric for Correlating Artifacts and Actionable Insights
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Solution Overview
Problem
Existing SDLC processes face challenges in integrating diverse AI, ML, and NLP technologies, leading to increased complexity, security vulnerabilities, and inefficiencies in software development due to lack of integrated project control, inefficient data management, and insufficient tools for identifying and addressing vulnerabilities and requirements.
Innovation Solution
A method and system leveraging AI, ML, and NLP to build a Knowledge Fabric that correlates and clusters SDLC artifacts, generating actionable insights for improved decision-making and productivity through semantic and contextual data analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If diverse AI, ML, and NLP technologies are integrated into SDLC processes, then software delivery productivity and decision-making quality are improved, but system complexity and integration difficulty increase
Solution Approach 1:
The patent combines multiple AI technologies (ML, NLP, clustering algorithms) into a unified system architecture that processes SDLC artifacts through a coordinated pipeline. The knowledge fabric integration layer merges data from heterogeneous sources (code repositories, issue trackers, documentation) into a consolidated knowledge representation, resolving the contradiction by providing integrated functionality without linearly increasing complexity
Solution Approach 2:
The patent introduces intermediary components including a knowledge fabric layer that mediates between raw SDLC data and AI processing engines, and an abstraction layer that handles technology-specific variations. These intermediaries shield the core processing logic from integration complexity, allowing productivity improvements while managing system complexity through modular architecture
2Adaptability or versatility
If manual or script-based methods are used to assemble and manage AI applications, then flexibility and customization are maintained, but provisioning logic portability and project control decrease
Solution Approach 1:
The patent implements a universal platform architecture that can execute diverse AI workloads across different infrastructure environments. The system provides multi-functional capabilities including automated provisioning, unified data access, and standardized processing pipelines that work across cloud, on-premise, and hybrid environments, maintaining flexibility through configuration rather than customization
3Reliability
If security vulnerabilities are identified and fixed after coding, then software security is improved, but development time and costs increase
Solution Approach 1:
The patent implements preliminary security analysis by integrating vulnerability detection capabilities into the SDLC pipeline before code completion and deployment. AI models analyze code patterns, dependency relationships, and architectural decisions during development phases to identify potential security issues early, preventing rather than merely detecting vulnerabilities
Solution Approach 2:
The system establishes continuous feedback loops where AI models continuously analyze SDLC artifacts and provide real-time security assessments. The feedback mechanism delivers actionable security insights to developers during the coding process, enabling immediate correction of vulnerabilities without disrupting the development flow or requiring post-coding security audits
4Quantity of substance
If large repositories of security and regulatory information are maintained, then comprehensive coverage is achieved, but navigation difficulty and quality control increase
Solution Approach 1:
The patent extracts only the relevant security and regulatory information needed for specific SDLC contexts from large external repositories. The system selectively retrieves and processes pertinent requirements, standards, and vulnerability data based on the current project context, artifact type, and risk profile, rather than processing entire information repositories
Data Source
AI summary
Provided is a method and system (108) for building and leveraging a knowledge fabric (110) in a Software Development Lifecycle (SDLC). A plurality of SDLC artifacts are received from a plurality of heterogeneous data sources (102). The plurality of SDLC artifacts are then correlated to build an end-to-end correlation and are clustered to generate an SDLC knowledge fabric (110). This includes extracting semantic and contextual data from the plurality of SDLC artifacts using Natural Language Processing (NLP) and deep text analytics and transforming the extracted semantic and contextual data to knowledge graphs. One or more actionable items (112) are then derived using the SDLC knowledge fabric (110) and the one or more actionable items (112) are used to improve overall process efficiency and accelerate software delivery in the SDLC.