Automated Software Requirement Validation Engine
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current software development methodologies face significant challenges in validating software development requirements, leading to defects and cost overruns due to poor requirement quality, miscommunication, and manual validation limitations, especially in Agile methodologies where quick sprints and unclear goals hinder effective defect identification.
Innovation Solution
A system and method for validating software development requirements that involves tokenizing and tagging requirements, deriving patterns, determining context and business domain, and analyzing against pre-defined rules and patterns, with a learning process initiated for new or unvalidated requirements, utilizing a requirement validation engine with modules for entity extraction, classification, and intelligence repository access.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual validation methods are used for software requirements, then validation can be performed with simple processes, but the validation process becomes time-consuming and reduces sprint velocity
Solution Approach 1:
The patent replaces manual validation processes with an automated validation engine that uses natural language processing, pattern matching, and rule-based systems to validate software requirements. This substitution of mechanical/manual processes with automated computational processes resolves the contradiction by maintaining validation thoroughness while dramatically reducing time consumption and improving sprint velocity.
Solution Approach 2:
The validation engine performs self-validation of requirements by automatically analyzing requirement texts against predefined patterns, rules, and best practices. The system serves itself by autonomously identifying violations, categorizing defects, and providing validation results without requiring manual intervention for each requirement, thereby maintaining simplicity while enhancing productivity.
2Device complexity
If defects are detected at later stages (testing or post-release), then the validation process becomes less intensive, but the cost of fixing defects increases significantly
Solution Approach 1:
The patent implements preliminary validation of software requirements at the very beginning of the development lifecycle, before design and coding phases. By performing validation actions in advance during the requirements gathering phase, the system detects defects early when they are least costly to fix, resolving the contradiction between validation intensity and defect fixing costs.
Solution Approach 2:
The validation engine provides immediate feedback on requirement quality by automatically detecting violations and defects in requirements as they are created or reviewed. This continuous feedback mechanism enables early defect detection and correction, preventing defects from propagating to later stages where fixing costs would be exponentially higher.
3Measurement precision
If comprehensive validation rules and patterns are applied to all requirements, then validation accuracy improves, but the complexity of the validation system increases
Solution Approach 1:
The patent segments the validation system into distinct modular components: pattern matching modules for different requirement types, rule-based validation modules for specific quality attributes, and defect categorization modules. Each module handles specific aspects of validation independently, which maintains high validation accuracy through comprehensive rule application while managing system complexity through modular architecture.
Solution Approach 2:
The validation engine implements a universal framework that handles multiple types of software requirements (functional, non-functional, use cases, user stories) using a common set of patterns, rules, and validation logic. This multi-functional approach achieves comprehensive validation accuracy across diverse requirement types without proportionally increasing system complexity, as the same core engine adapts to different validation scenarios.
4Productivity
If automated validation is implemented, then sprint velocity improves, but the system requires sophisticated processing and intelligence repositories
Solution Approach 1:
The system performs preliminary population of the intelligence repository with patterns, rules, and best practices before the validation process begins. By pre-loading validation knowledge bases and configuration data in advance, the system enables rapid automated validation execution without requiring complex real-time processing or adaptive learning during the validation itself, thus improving sprint velocity while managing processing sophistication requirements.
Data Source
AI summary
System and method for validating software development requirements are disclosed. The method comprises accessing a software development requirement, extracting a plurality of tokens from the software development requirement, tagging each of the plurality of tokens to a corresponding part of speech, and deriving a pattern based on the plurality of tokens and the plurality of corresponding parts of speech. The method further comprises determining at least one of a context and a business domain of the software development requirement and identifying pre-defined rules for the plurality of tokens and pre-defined patterns for the pattern. In response to a positive identification, the software development requirement are validated by analyzing the plurality of tokens against the pre-defined rules and by analyzing the pattern against the pre-defined patterns. In response to a negative identification, a learning process is initiated based on intelligence gathered from a manual validation of the software development requirement.