Automated Software Artifact Relationship Traceability
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for maintaining relationships between software design artifacts are labor-intensive, require significant computing resources, and are prone to errors due to the need for manual link establishment, making it difficult to assess the impact of changes across multiple artifacts throughout the software development lifecycle.
Innovation Solution
An automated tool that extracts and compares key terms from various software design artifacts to quantify relationships based on similarity analysis, using techniques such as Inverse Document Frequency (IDF) and domain ontology to determine semantic meanings, and combines explicit and implicit relationships to provide a comprehensive traceability matrix.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual link establishment between software design artifacts is used, then relationship traceability is achieved, but substantial overhead in computing resources and personnel time is required
Solution Approach 1:
The system automatically extracts concepts from software artifacts and establishes relationships between them without requiring manual intervention. The automated concept extraction and relationship establishment processes enable the system to serve itself, eliminating the need for developers and analysts to manually create and maintain links between artifacts throughout the software development lifecycle.
Solution Approach 2:
The patent replaces the manual mechanical process of establishing and maintaining links between software artifacts with an automated computational system. The system uses concept extraction, term frequency analysis, and semantic similarity computation to automatically determine relationships, substituting human labor with algorithmic processing.
2Reliability
If manual link establishment between software design artifacts is used, then relationship traceability is achieved, but substantial computing resources are required
Solution Approach 1:
The system extracts only the essential concepts and key terms from software artifacts that are necessary for establishing relationships. By focusing on extracting meaningful concepts rather than processing entire artifacts, the system reduces the computational burden while maintaining effective relationship traceability.
Solution Approach 2:
The system computes semantic similarity using term frequency and selected key terms rather than analyzing complete artifact contents. This partial action approach processes only the most relevant portions of artifacts (key terms and concepts) to establish relationships, reducing overall computing resource requirements while achieving sufficient traceability.
3Reliability
If explicit links are continuously established and maintained throughout the software development lifecycle, then relationship traceability is improved, but gaps in relationships may still be missed
Solution Approach 1:
The system continuously analyzes software artifacts and automatically updates relationship information as new artifacts are created or existing ones are modified. This ongoing automated feedback process ensures that relationships are detected and maintained throughout the software development lifecycle, reducing the likelihood of missing relationships that manual processes might overlook.
Solution Approach 2:
The automated concept extraction and similarity computation system replaces manual link establishment processes. This substitution enables more comprehensive and consistent relationship detection across all artifacts, as the automated system can analyze artifacts systematically without the gaps and errors that occur in manual processes.
4Reliability
If manual link establishment is used, then relationship information is captured, but the process does not lend itself easily to ascertaining relationships in advanced stages when many artifacts are already in place
Solution Approach 1:
The system extracts and stores concept information from software artifacts as they are created, preparing relationship data in advance. This preliminary action of concept extraction and storage enables efficient relationship analysis at any stage of the software development lifecycle, including advanced stages when many artifacts already exist, without requiring retroactive manual link establishment.
Data Source
AI summary
Tools and methods are described herein that allows for measuring and using the relationship between artifacts of a software design, such as requirements, test plans, and so on. The relationship can be quantified by determining a relationship quotient for quantifying a similarity between components of software design artifacts and presenting the quantified relationships to a user, such as a software designer, so that he or she can account for the relationship between such components during design changes and so on. The relationship quotient is made more representative of substantive similarity by selecting the key terms that are to be submitted to a similarity analysis such that words that are too common in the English language, such as conjunctions, articles, etc., are not used. Ubiquity of certain key terms in an enterprise is accounted for by adding a term significance weight to the similarity analysis. The similarity analysis is made contextual, for instance, by the use of inputs from domain ontology including Entity Descriptions, and Entity Relationships.