Software Composition Alerts Using Multi-Target ML Predictions
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Solution Overview
Problem
Organizations face challenges in managing open-source software (OSS) due to compliance risks with various licenses, security vulnerabilities, and lack of predictive intelligence in existing software composition analysis tools, leading to potential legal and security threats.
Innovation Solution
A multi-target machine learning (ML) model is employed to predict potential performance and security issues in software applications by analyzing historical software composition metadata, using a neural network to simultaneously output predictions for performance and security concerns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional OSS management tools are used, then basic license compliance can be achieved, but the tools lack intelligence to predict potential performance and security issues
Solution Approach 1:
The patent introduces a multi-target machine learning model as an intermediary between OSS composition data and prediction outcomes. This model processes multiple features (license types, component versions, hosting environments) simultaneously to generate both performance and security predictions, resolving the contradiction by providing intelligent predictions without requiring complex separate analysis systems
Solution Approach 2:
The multi-target ML model performs multiple prediction functions simultaneously - it predicts both performance issues and security vulnerabilities from the same input data. This universal approach improves reliability across multiple prediction tasks while avoiding the complexity of maintaining separate specialized tools for each prediction type
2Measurement precision
If comprehensive OSS analysis is performed, then prediction accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary analysis by extracting relevant features from OSS composition data before the actual prediction phase. By pre-processing and structuring the data (identifying license types, component versions, hosting environments) in advance, the model can generate accurate predictions more quickly when needed, reducing the time loss during critical evaluation phases
Solution Approach 2:
The patent segments the prediction process into distinct feature extraction and prediction phases. The system identifies and extracts specific relevant features from comprehensive OSS data, then feeds only these processed features into the ML model. This segmentation maintains prediction accuracy by using comprehensive data while reducing processing time by focusing computational resources on extracted features rather than raw data
3Adaptability or versatility
If multiple prediction targets are analyzed simultaneously, then comprehensive risk management is achieved, but model complexity increases
Solution Approach 1:
The patent merges multiple prediction targets (performance and security) into a single multi-target ML model. By combining these prediction functions in one unified model that processes shared features simultaneously, the system achieves comprehensive risk management while avoiding the complexity of maintaining and coordinating multiple separate models
Solution Approach 2:
The multi-target ML model serves multiple prediction purposes simultaneously - it predicts both performance issues and security vulnerabilities from the same input features. This universal model structure provides adaptability across different prediction targets while maintaining a single coherent system architecture, thus achieving versatility without proportionally increasing complexity
Data Source
AI summary
An example methodology includes, by a computing device, receiving information regarding a new application from another computing device and determining one or more relevant features from the information regarding the new application, the one or more relevant features influencing predictions of any potential performance issue and any potential security issue. The method also includes, by the computing device, generating, using a multi-target machine learning (ML) model, a first prediction of any potential performance issue for the new application and a second prediction of any potential security issue for the new application based on the determined one or more relevant features, and sending the first and second predictions to the another computing device.