Executable Code Fault Scoring for Adaptive Validation
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Solution Overview
Problem
Conventional executable code validation techniques are inflexible, inaccurate, and costly in terms of human and computational resources due to manual quality gates and lack of predictive insight.
Innovation Solution
A code development system utilizing a machine-learning model generates a fault score based on metadata to quantify the probability of faults in code units, allowing dynamic control of review processes and resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual quality gates and conventional validation techniques are used, then code validation is performed, but the process is inflexible, inaccurate, and costly in terms of human and computational resources
Solution Approach 1:
The patent replaces manual quality gate reviews with an automated machine learning model that analyzes code unit metadata and generates fault probability scores. This substitution of mechanical human review processes with an automated intelligent system directly addresses the contradiction by improving validation accuracy through consistent algorithmic analysis while reducing the complexity and resource costs of manual review processes.
Solution Approach 2:
The system enables code units to be automatically evaluated by the machine learning model without requiring manual intervention. The model processes metadata, generates fault scores, and provides predictive insights automatically, allowing the validation process to serve itself rather than relying on external human resources, thereby improving accuracy while reducing process complexity.
2Reliability
If manual quality gates are used for code validation, then code review is performed, but human and computational resources are excessively consumed
Solution Approach 1:
The patent replaces resource-intensive manual quality gate processes with an automated machine learning system that processes code metadata efficiently. The model generates fault probability scores without requiring extensive human computational effort, directly addressing the contradiction by maintaining high fault detection accuracy while dramatically reducing the loss of human and computational resources.
Solution Approach 2:
The system uses metadata copies and representations of code units rather than requiring full manual review of actual code. The machine learning model processes structured metadata representations, which are lighter copies of the full code, enabling accurate fault detection while minimizing resource consumption associated with handling and analyzing complete code bases manually.
3Adaptability or versatility
If conventional validation techniques are used, then code units are validated, but the process lacks flexibility and predictive insight
Solution Approach 1:
The patent implements preliminary action by using the machine learning model to predict fault probabilities before code units are merged. The system analyzes metadata and generates fault scores in advance, providing predictive insight that enables flexible, data-driven decisions about which code units require review and which can be merged with confidence, directly addressing both adaptability and information loss concerns.
Solution Approach 2:
The system implements feedback mechanisms where fault probability scores from the machine learning model inform subsequent validation decisions. The predictive insights are fed back into the validation process to dynamically adjust review requirements, enabling flexible resource allocation and preventing loss of predictive information by continuously utilizing model outputs to guide validation workflows.
4Reliability
If extensive manual review is performed on all code units, then validation thoroughness is improved, but productivity and development speed decrease
Solution Approach 1:
The patent applies partial action by using the machine learning model to identify only those code units that require review based on their fault probability scores. Instead of performing extensive manual review on all code units, the system selectively applies review resources to high-risk cases, maintaining validation thoroughness for critical code while improving overall development productivity by avoiding unnecessary reviews of low-risk code.
Solution Approach 2:
The system implements dynamics by making validation thoroughness adaptive rather than static. The machine learning model dynamically determines the level of review required for each code unit based on its specific characteristics and fault probability, allowing the validation process to be thorough where needed and streamlined where not needed, thereby maintaining reliability while improving productivity.
Data Source
AI summary
Executable code fault detection techniques are described. In one or more implementations, a request is received to include a code unit as part of executable code and metadata is obtained that is associated with the code unit. A fault score is generated that is indicative of a probability that the code unit introduces a fault as part of execution with the executable code. The fault score is generated using a machine-learning model by processing the metadata. Testing of the code unit as part of the executable code is controlled based on the fault score.