Dell products l.p. (20240126667). INTELLIGENT SCORE BASED OOM TEST BASELINE MECHANISM simplified abstract
Contents
- 1 INTELLIGENT SCORE BASED OOM TEST BASELINE MECHANISM
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 INTELLIGENT SCORE BASED OOM TEST BASELINE MECHANISM - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
INTELLIGENT SCORE BASED OOM TEST BASELINE MECHANISM
Organization Name
Inventor(s)
INTELLIGENT SCORE BASED OOM TEST BASELINE MECHANISM - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240126667 titled 'INTELLIGENT SCORE BASED OOM TEST BASELINE MECHANISM
Simplified Explanation
The patent application describes a system and method for creating an out of memory (OOM) test baseline system using machine learning to predict test scores for unexecuted test cases.
- The method involves executing a subset of test cases on a system and calculating test case scores for each.
- A machine learning system is trained using the subset test scores to predict baseline test scores for unexecuted test cases.
- The predicted baseline test scores are used to tune the unexecuted test cases to identify OOM issues when they are executed on a test system.
Potential Applications
The technology described in this patent application could be applied in software development, quality assurance, and system testing to predict and prevent out of memory issues in test cases.
Problems Solved
1. Predicting baseline test scores for unexecuted test cases. 2. Identifying out of memory issues in test cases before execution.
Benefits
1. Improved efficiency in test case execution. 2. Early detection of potential out of memory problems. 3. Enhanced accuracy in predicting test case scores.
Potential Commercial Applications
Predictive testing software tools, quality assurance systems, software development platforms.
Possible Prior Art
There may be prior art related to machine learning systems used in software testing to predict test outcomes and identify potential issues before execution.
What are the potential limitations of this technology?
The technology may have limitations in accurately predicting test scores for all types of test cases and in tuning configurations for complex systems.
How does this technology compare to existing methods for predicting test outcomes?
This technology stands out by using machine learning to predict baseline test scores for unexecuted test cases, which can help in identifying out of memory issues before test case execution.
Original Abstract Submitted
methods, system, and non-transitory processor-readable storage medium for an out of memory test baseline system are provided herein. an example method includes executing a plurality of test cases on a system. a test score calculation module calculates a test case score for each of the executed test cases in a subset of the plurality of test cases. an out of memory (oom) test baseline configuration system trains a machine learning system, using the subset test scores, to predict a baseline test score for an unexecuted test case. a test case score prediction module predicts the baseline test score for the unexecuted test case. a test case configuration tuning module tunes the unexecuted test case to determine a baseline configuration for the unexecuted test case, to identify oom issues when the unexecuted test case is executed on a test system.
