View source for 18433574. DEVELOPMENT TEST AUTOMATION FRAMEWORK simplified abstract (Bank of America Corporation)

=DEVELOPMENT TEST AUTOMATION FRAMEWORK=


==Organization Name==

[[:Category:Bank of America Corporation|Bank of America Corporation]]


[[Category:Bank of America Corporation]]

==Inventor(s)==

[[:Category:Charanjit Singh Gurnasinghani of West Hills CA (US)|Charanjit Singh Gurnasinghani of West Hills CA (US)]][[Category:Charanjit Singh Gurnasinghani of West Hills CA (US)]]

[[:Category:Nadeem Panjwani of Carrollton TX (US)|Nadeem Panjwani of Carrollton TX (US)]][[Category:Nadeem Panjwani of Carrollton TX (US)]]

[[:Category:Kurt R. Schultz of Newbury Park CA (US)|Kurt R. Schultz of Newbury Park CA (US)]][[Category:Kurt R. Schultz of Newbury Park CA (US)]]

==DEVELOPMENT TEST AUTOMATION FRAMEWORK - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18433574 titled 'DEVELOPMENT TEST AUTOMATION FRAMEWORK

==Simplified Explanation==

The patent application describes a computing platform that automatically detects, analyzes, and corrects errors in an application. Here are some key points from the abstract:

* The computing platform identifies errors in an application and corrects them based on predetermined actions.
* A confidence score is determined for each action, representing the likelihood of successfully correcting the error.
* If the confidence score is above a predetermined threshold range, the computing platform automatically corrects the error.
* Results and feedback are input into a machine learning model to improve the accuracy and reliability of the platform over time.

== Potential Applications ==
This technology could be applied in various industries where error detection and correction are critical, such as software development, quality assurance, and data analysis.

== Problems Solved ==
This technology addresses the challenge of manual error detection and correction, saving time and resources for businesses. It also improves the overall performance and reliability of applications.

== Benefits ==
The benefits of this technology include increased efficiency, improved accuracy, and enhanced user experience. It can help businesses deliver high-quality products and services to their customers.

== Potential Commercial Applications ==
One potential commercial application of this technology could be in the software development industry, where companies can use it to streamline their testing processes and ensure the quality of their products.

== Possible Prior Art ==
One possible prior art for this technology could be automated testing tools that are commonly used in software development to detect and correct errors in code.

=== Unanswered Questions ===

=== How does the computing platform determine the confidence score for each action? ===
The abstract mentions that a confidence score is assigned to each action, but it does not provide details on the specific criteria or algorithms used to calculate this score.

=== What types of errors can the computing platform detect and correct? ===
The abstract does not specify the range or nature of errors that the computing platform is capable of detecting and correcting. It would be helpful to know if there are limitations to the types of errors that can be addressed by this technology.


==Original Abstract Submitted==

Aspects of the disclosure relate to automatically detecting, analyzing, and correcting errors in an application. The computing platform may identify errors in an application, and subsequently correct the errors based on corresponding actions that have been determined to correct the errors. The computing platform may determine a confidence score that corresponds to a particular action, representing the level of confidence that the particular action will successfully correct the corresponding error. The computing platform may set a predetermined threshold range that is compared to the confidence score, in which a confidence score greater than the threshold range will cause the computing platform to automatically correct the error. The computing platform may input results and feedback into a machine learning model to further refine the accuracy and reliability of the computing platform over time.



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