Software Defect Prediction Using Worker Signature Vectors
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Solution Overview
Problem
Current software product development techniques face challenges in efficiently and objectively identifying defects, particularly those involving human factors and complex issues across multiple components, during the development process, as existing methods often fail to account for human physiological and psychological states effectively.
Innovation Solution
A software product development defect and issue prediction and diagnosis system that utilizes quantitative and qualitative information from human activities, incorporating worker profile and state information, along with product state data, to generate individual and general worker signatures and product signatures, which are then analyzed using a neural network model to diagnose defects and provide explanations for their causes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional defect identification methods are used, then the development process is simpler, but defect detection precision and ability to identify human-factor defects deteriorates
Solution Approach 1:
The system segments defect detection into multiple specialized components: worker profile analysis module, worker state monitoring module, product state tracking module, and neural network diagnosis module. Each component handles a specific aspect of defect identification, improving overall detection precision while organizing complexity into manageable segments
Solution Approach 2:
The patent introduces worker signature vectors as an intermediary representation that bridges worker characteristics and product defects. These signature vectors serve as a mediator between human factors and defect analysis, enabling precise defect detection without requiring direct complex analysis of all worker states
2Measurement precision
If comprehensive worker and product data is collected, then defect diagnosis accuracy improves, but information processing time and resource utilization increases
Solution Approach 1:
The system performs preliminary actions by continuously collecting and preprocessing worker profile information, worker state data, and product state information during normal development activities. This pre-processing prepares the data for rapid defect diagnosis without adding significant processing time when defects need to be identified
Solution Approach 2:
The patent implements feedback mechanisms where defect diagnosis results are fed back into the system to refine worker signatures and improve future diagnoses. This continuous learning process enhances diagnosis accuracy over time while the system becomes more efficient at processing information
3Reliability
If human physiological and psychological states are monitored, then defect prediction capability improves, but privacy concerns and system complexity increase
Solution Approach 1:
The system extracts only the essential and relevant features from worker physiological and psychological states that correlate with defect-prone conditions. By taking out only the critical information needed for prediction rather than monitoring all aspects of worker states, the system improves defect prediction capability while limiting complexity and privacy intrusion
Data Source
AI summary
According to an example, with respect to software product development defect and issue prediction and diagnosis, worker profile information and worker state information for a plurality of workers involved in development of a product may be ascertained. A general worker signature that includes a plurality of clusters for all of the plurality of workers may be generated. For each of the plurality of workers, an individual worker signature vector that represents at least one cluster of the plurality of clusters that an individual worker is aligned to may be generated. A product signature vector may be generated based on product state information. Further, an output that includes an explanation for a defect associated with the development of the product may be generated based on a neural network model based analysis of the individual worker signature vectors and the product signature vector over a temporal dimension.