Automated Project Component Reusability via Semantic Analysis
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Solution Overview
Problem
Existing techniques for identifying reusable project components are complex, time-consuming, and not user-friendly, often requiring manual effort and generating software taxonomies that are difficult to manage, which hinders efficient project reusability.
Innovation Solution
A method and system utilizing Natural Language Processing (NLP) to extract current project requirements, perform semantic analysis with pre-existing requirements, and determine similarity scores to retrieve reusable project components, thereby automating the process and reducing complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If software taxonomy is generated and software profiles are created based on the taxonomy for comparison, then reusable project components can be identified, but the complexity level becomes high and the process becomes time-consuming
Solution Approach 1:
The patent replaces the manual mechanical process of generating software taxonomy and profiles with an automated machine learning system. The NLP-based semantic analysis automatically extracts requirements from project documents and compares them with existing components, eliminating the need for manual taxonomy creation and profile generation while maintaining high accuracy in identifying reusable components.
Solution Approach 2:
The system enables self-service by automatically performing semantic analysis and similarity scoring without requiring manual intervention. The machine learning models autonomously process project requirements, compare them with existing components, and generate similarity scores, allowing the system to serve itself in identifying reusable components without human expertise in taxonomy creation.
2Reliability
If manual effort is used to generate software taxonomy and software profile, then reusable components can be identified, but the technique becomes time-consuming
Solution Approach 1:
The patent replaces manual mechanical processes with automated computational systems. Natural Language Processing algorithms automatically analyze project requirements and generate semantic representations, while machine learning models perform similarity comparisons, eliminating the time-consuming manual taxonomy generation process while maintaining or improving matching accuracy.
Solution Approach 2:
The system performs preliminary action by pre-processing project requirements through NLP to extract semantic meaning before comparison. The machine learning models are pre-trained on domain-specific data, enabling them to quickly and accurately compare new requirements with existing components without requiring manual preparation of taxonomy and profiles for each new project.
3Reliability
If dependencies between components are analyzed by generating intermediary components such as surrogates, then reusable components can be identified, but the complexity of the process increases
Solution Approach 1:
The patent extracts the essential semantic meaning of component dependencies directly from project requirements using NLP, eliminating the need to generate complex intermediary surrogate components. The semantic analysis directly identifies dependency relationships by understanding the meaning of requirement statements, simplifying the dependency analysis process while maintaining accuracy.
Solution Approach 2:
The system replaces the mechanical process of generating intermediary surrogate components with a direct semantic analysis approach using NLP and machine learning. The models directly analyze the semantic relationships between requirements and existing components, identifying dependencies without requiring complex intermediary representations, thereby reducing process complexity while maintaining reliability.
4Extent of automation
If existing techniques are used to determine reusable project components, then some level of automation is achieved, but the techniques are not user-friendly and require manual intervention
Solution Approach 1:
The system achieves self-service by automatically performing all steps from requirement extraction to component recommendation without requiring manual user intervention. The NLP-based semantic analysis and machine learning models autonomously process inputs and generate results, making the system fully automated and user-friendly by eliminating the need for users to understand or manage complex taxonomy and profile configurations.
Data Source
AI summary
Disclosed subject matter is related to project reusability including method and system for identifying reusable project components for building a new project. The method comprises extracting current requirement of the new project from one or more data sources, using NLP and detect pre-existing requirements associated with pre-existing projects, similar to the current requirement by performing semantic analysis of the current requirement and the pre-existing requirements using a predefined machine learning technique such as Latent Semantic Analysis (LSA) technique. Further, a similarity score is determined for each of the one or more pre-existing requirements based on degree of similarity between the current requirement and the pre-existing requirements. Finally, the reusability detecting system retrieves project components associated with one of the pre-existing requirements based on the similarity score that are the reusable project components for building the new project, thereby reducing time, cost and resources required for developing the new project.