Software Supply Chain Version Scoring for API and Vulnerability Risk
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Solution Overview
Problem
The complexity of software supply chain analysis is exacerbated by the need to select the optimal version of open-source or proprietary components, as switching to the latest version may not always be the best choice due to various factors including API changes, vulnerabilities, and quality history.
Innovation Solution
A system that identifies new versions of external components, analyzes API changes, vulnerabilities, and quality history, generating a composite score to assist developers in making informed decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the latest version of an external component is switched to, then the software application may benefit from new features and improvements, but it may also introduce API changes, new vulnerabilities, or reduced quality stability
Solution Approach 1:
The system segments the complex supply chain analysis into distinct evaluation dimensions: API compatibility analysis, vulnerability assessment, quality history evaluation, and composite scoring. Each dimension is analyzed separately through automated processes, breaking down the overwhelming complexity into manageable, structured components that can be evaluated independently and aggregated into an overall recommendation
Solution Approach 2:
The patent introduces an intermediary automated analysis system that mediates between the developer's need for reliable software and the complexity of evaluating multiple component versions. This intermediary system performs comprehensive analyses including API change detection, vulnerability scanning, and quality history review, then presents synthesized results through a user interface, shielding developers from the underlying analytical complexity
2Measurement precision
If comprehensive analysis of API changes, vulnerabilities, and quality history is performed, then the accuracy of version selection is improved, but the time and computational resources required increase
Solution Approach 1:
The system performs preliminary actions by pre-computing and storing quality metrics, vulnerability data, and API compatibility information for multiple component versions before they are needed for decision-making. This includes maintaining databases of known vulnerabilities, pre-analyzing API interfaces across versions, and calculating quality history metrics in advance, so that when a developer needs to evaluate versions, the heavy computational lifting has already been completed
Solution Approach 2:
The patent transforms the analysis from a qualitative, time-consuming manual process into a quantitative, automated process by changing key parameters: converting subjective quality assessments into objective numerical scores, transforming vulnerability identification into automated pattern recognition, and converting API compatibility evaluation into structured data comparison. This parameter transformation enables rapid, precise evaluation without manual intervention
Data Source
AI summary
A current a version of an external component (e.g., an open-source component or a third-party component) that is used in a software application is identified. A new version of the current version of the external component is identified (supply chain components). For example, the new version may have been just released by an open-source community. In response to identifying the new version of the current version of the of the external component, a series of actions are implemented that include: identifying changes to Application Programming Interfaces (APIs) in the new version of the current version of the external component; identifying new vulnerabilities in the new version of the current version of the external component; and determining a quality history associated with the new version of the current version of the external component. Based on the actions, a composite score is generated and displayed to a developer.