NLP Security Requirements Identification for Early Software Testing
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Solution Overview
Problem
Software development often overlooks implicit security requirements in user stories, leading to security risks that are revealed later in the process, making retrofitting expensive and ineffective.
Innovation Solution
A system utilizing natural language processing (NLP) to analyze individual words in user stories, identify functional words, and map them to security requirements, generating reports and automated testing playbooks to ensure early integration of security considerations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If security is addressed retrofitting protection into applications post completion, then security protection is achieved, but cost and effectiveness deteriorate significantly
Solution Approach 1:
The system performs preliminary action by automatically analyzing user stories during the requirements phase to identify security requirements before development begins. This early identification allows security to be built into the application from the start, avoiding expensive retrofits and rework that would be needed if security was addressed after completion.
Solution Approach 2:
The system implements feedback by continuously monitoring and analyzing user stories to identify security risks, then providing feedback to developers through generated reports and automated testing playbooks. This feedback loop enables real-time security awareness and adjustment during the development process, preventing security issues from escalating into costly problems.
2Measurement precision
If manual analysis of user stories for security requirements is performed, then accuracy can be maintained, but time consumption and labor requirements increase
Solution Approach 1:
The system performs self-service by automatically analyzing user stories to identify security requirements without requiring manual intervention. The automated NLP-based system processes user stories, identifies functional words, maps them to security requirements, and generates reports independently, maintaining high accuracy while eliminating time-consuming manual analysis.
Solution Approach 2:
The system replaces the mechanical manual analysis process with an automated computational system. Instead of human analysts reading and interpreting user stories, the system uses NLP algorithms to automatically extract functional words, map them to security requirements, and generate comprehensive security reports, significantly reducing time consumption while maintaining precision.
3Reliability
If comprehensive security analysis is performed during development, then security coverage is improved, but complexity of the development process increases
Solution Approach 1:
The system applies segmentation by breaking down the complex security analysis process into discrete, manageable steps: analyzing user stories, identifying functional words, mapping to security requirements, and generating reports. This segmentation makes the security integration process more structured and less overwhelming, improving security coverage without proportionally increasing development complexity.
Solution Approach 2:
The system introduces an intermediary layer between user stories and security requirements. The automated analysis system acts as a mediator that translates functional words from user stories into corresponding security requirements, simplifying the connection between business needs and security considerations without requiring developers to manually navigate complex security mapping.
4Productivity
If security requirements are identified early in development, then rework is avoided, but identification accuracy may be compromised
Solution Approach 1:
The system replaces manual security requirement identification with automated NLP-based analysis that processes user stories to extract functional words and map them to security requirements. This mechanical substitution maintains high identification accuracy even when performed early in development, as the automated system consistently applies defined mapping rules without the variability inherent in manual analysis.
Solution Approach 2:
The system uses feedback mechanisms to ensure identification accuracy by continuously monitoring user stories and comparing identified security requirements against the functional words extracted from the stories. This feedback loop allows the system to verify and adjust its identifications in real-time, maintaining precision even during early development stages before requirements are fully finalized.
Data Source
AI summary
The proposed systems and methods apply natural language processing to identify implicit security requirements flowing from input text narratively describing desired features for a software project. These systems and methods can identify hidden security requirements that may not be readily apparent from the features described in the input text. For example, a story may include a feature of a return URL (Uniform Resource Locator), which is the URL for the website to which a user will be redirected. A security vulnerability that would not be obvious from this feature is that a user might be directed to an attacker controlled site instead of the originally intended site. A security requirement that could counteract this vulnerability would be to include the feature of verifying all redirects go to Whitelisted Sites. The proposed systems and methods provide a framework for automated security requirements analysis capable of identifying unstated security requirements early on in a software development lifecycle using artificial intelligence techniques.