Automated Task Identification in Software Lifecycle
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Solution Overview
Problem
Software developers face challenges in identifying and prioritizing task and security requirements throughout the software lifecycle due to the complexity of integrating existing code, libraries, and network architecture, which often leads to vulnerabilities and security breaches, with existing tools lacking context-specific and timely guidance.
Innovation Solution
A system and method for automated extraction of task and security requirements based on software project context, using context repositories and machine learning to generate a prioritized task list that guides developers and mitigates risks throughout the software lifecycle.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If developers use multiple standalone ALM and project management tools to identify requirements and track vulnerabilities, then they can comprehensive coverage of security requirements, but the complexity of managing these tools and coordinating between them increases significantly
Solution Approach 1:
The patent merges multiple standalone ALM and project management tools into a unified integrated platform. This integration allows developers to manage security requirements, track vulnerabilities, and coordinate tasks within a single system, eliminating the complexity of managing multiple separate tools while maintaining comprehensive security coverage through centralized data storage and cross-tool coordination capabilities.
Solution Approach 2:
The integrated platform provides universal functionality by combining security requirement tracking, vulnerability management, project coordination, and compliance monitoring within a single system. This multi-functional approach allows the system to handle diverse security tasks across different phases of the software lifecycle without requiring developers to switch between specialized tools.
2Measurement precision
If developers manually review and prioritize task requirements from multiple sources, then they can ensure accuracy of requirement identification, but the time and effort required for manual analysis increases significantly
Solution Approach 1:
The system employs automated algorithms that self-service the requirement identification process by automatically analyzing software context, extracting security requirements, and prioritizing tasks without extensive manual intervention. The system maintains accuracy through intelligent parsing of requirements, automatic classification of security issues, and algorithmic prioritization based on risk assessment, thereby reducing manual analysis time while preserving identification accuracy.
Solution Approach 2:
The patent replaces manual mechanical analysis processes with automated computational systems. Instead of developers manually reviewing and prioritizing requirements from multiple sources, the system uses automated algorithms to parse requirements, extract security concerns, and prioritize tasks based on calculated risk levels, significantly reducing the time and effort required while maintaining or improving accuracy through systematic analysis.
3Difficulty of detecting and measuring
If developers focus on detecting vulnerabilities in source code using static analysis tools, then they can identify security flaws, but the cost and difficulty of fixing vulnerabilities after coding increases
Solution Approach 1:
The system performs preliminary action by automatically identifying and prioritizing security requirements and vulnerabilities during the design and planning phases of software development, before coding begins. Through automated requirement extraction and risk assessment, the system enables developers to address security issues in advance, making them easier and less costly to fix, rather than attempting to detect and remediate vulnerabilities after the coding is complete.
4Reliability
If developers maintain large repositories of security and regulatory information, then they can ensure compliance with current standards, but the difficulty of navigating and keeping up to date with new information increases
Solution Approach 1:
The system extracts and isolates relevant security and regulatory information from large repositories, automatically parsing and filtering the data to identify only the requirements pertinent to the specific software project. This extraction process maintains compliance by keeping developers informed of current standards while reducing the complexity of navigating through extensive information repositories by presenting only the essential, actionable requirements in an organized manner.
Data Source
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AI summary
A system and method for automation of task identification and control in a software lifecycle. Software context for a software asset is extracted from context repositories of the software asset during software development and operation, the extracted context data is matched to relevant tasks in a knowledge database to select tasks for the software asset, and task prioritization and orchestration are presented in a prioritized task list during a software lifecycle.