18223627. INTELLIGENT SCORE BASED OOM TEST BASELINE MECHANISM simplified abstract (Dell Products L.P.)
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 18223627 titled 'INTELLIGENT SCORE BASED OOM TEST BASELINE MECHANISM
Simplified Explanation
The abstract describes a method, system, and storage medium for an Out of Memory test baseline system. It involves executing test cases, calculating test scores, training a machine learning system, predicting baseline test scores, and tuning test case configurations to identify Out of Memory issues.
- The method involves executing a plurality of test cases on a system.
- A test score calculation module calculates test case scores for each executed test case in a subset of the test cases.
- An Out of Memory (OOM) test baseline configuration system trains a machine learning system to predict baseline test scores for unexecuted test cases.
- 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 identifying OOM issues.
Potential Applications
This technology can be applied in software development and quality assurance processes to predict and identify Out of Memory issues in test cases.
Problems Solved
This technology helps in proactively identifying Out of Memory issues in software systems before they occur during actual execution, leading to more stable and reliable software products.
Benefits
The benefits of this technology include improved software quality, reduced debugging time, and enhanced user experience by preventing Out of Memory errors.
Potential Commercial Applications
Commercial applications of this technology include software development tools, quality assurance software, and performance testing tools.
Possible Prior Art
Prior art in this field may include existing methods for predicting and preventing memory-related issues in software systems, as well as machine learning techniques for test case optimization.
What is the impact of this technology on software development processes?
This technology can significantly improve the efficiency and effectiveness of software development processes by proactively identifying and addressing Out of Memory issues before they impact end-users.
This technology goes beyond traditional methods of manual testing by leveraging machine learning to predict baseline test scores and identify potential Out of Memory issues in unexecuted test cases.
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.
