ML-Based User Ranking for Software Defect Identification
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Solution Overview
Problem
Software systems face challenges in detecting security defects, which are difficult to identify during the testing stage, leading to potential unauthorized access and resource wastage when a large number of users interact with the system to identify defects through crowd sourcing.
Innovation Solution
A system that assigns targets to users for defect identification using a machine learning model trained on user and target system features, ranking users based on their likelihood to provide valid defect submissions, thereby optimizing resource usage and improving defect detection efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a large number of users are allowed to identify and report system defects through crowd sourcing, then the likelihood of identifying security defects increases, but system resource consumption increases and efficiency decreases
Solution Approach 1:
The system changes the parameter of user selection from random or bulk invitation to precision-based selection using machine learning models. The model predicts user effectiveness scores based on multiple features (defect history, skills, profile attributes) and selects only users above a certain threshold, thereby maintaining high defect identification effectiveness while reducing the total number of users involved and system resource consumption.
Solution Approach 2:
The system creates a virtual model (machine learning model) that copies and analyzes user characteristics and defect patterns from historical data. This virtual model predicts which users are most likely to find defects, allowing the system to identify effective users without needing to engage all potential users, thus reducing resource consumption while maintaining identification effectiveness.
2Productivity
If external users are allowed to identify and report system defects, then defect detection capability improves, but interaction with a large number of users results in waste of system resources
Solution Approach 1:
The system performs preliminary analysis by training machine learning models on historical user data and defect information before actual defect identification campaigns. The model pre-calculates user effectiveness scores and predicts which users are most likely to succeed, allowing the system to proactively select the right users before engaging them, thereby improving defect detection capability while minimizing time and resource waste on ineffective interactions.
Solution Approach 2:
The system replaces the mechanical approach of manually reviewing user profiles or randomly selecting users with an automated machine learning-based prediction system. The ML model automatically scores and ranks users based on their likelihood to find defects, substituting manual or brute-force methods with an intelligent automated system that improves productivity while reducing time and resource consumption.
3Reliability
If crowd sourcing is used to allow external users to identify defects, then more defects can be found, but interacting with a very large number of users increases system resource usage
Solution Approach 1:
The machine learning model acts as an intermediary between the crowd sourcing platform and external users. Instead of the system directly managing and interacting with a large number of users, the ML model mediates by predicting user effectiveness and selecting appropriate users. This intermediary layer simplifies the system architecture by replacing complex user management logic with a predictive model, thereby maintaining high defect identification rates while reducing system complexity.
Data Source
AI summary
A system assigns target systems to users for identifying system defects. The system ranks the users using a machine learning model. The system extracts a feature vector describing a target system. The system extracts features describing each user. The system provides the features describing the user and the features describing the target system as input to a machine learning model. The machine learning model is trained to receive information describing an input user and an input target system and predict a score indicating a likelihood of the input user providing a system defect in the input target system. The system executes the machine learning model to predict a score for each user. The system ranks the users based on the scores. The system ranks the users based on the scores and communicates with a subset of users selected based on the ranking.