View source for 18223627. INTELLIGENT SCORE BASED OOM TEST BASELINE MECHANISM simplified abstract (Dell Products L.P.)

=INTELLIGENT SCORE BASED OOM TEST BASELINE MECHANISM=


==Organization Name==

[[:Category:Dell Products L.P.|Dell Products L.P.]]


[[Category:Dell Products L.P.]]

==Inventor(s)==

[[:Category:Huijuan Fan of Chengdu (CN)|Huijuan Fan of Chengdu (CN)]][[Category:Huijuan Fan of Chengdu (CN)]]

==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.

=== How does this technology compare to traditional methods of testing for memory-related issues? ===

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.



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