Software validation framework

Patent metadata

Field	Value
Publication number	US-12001273-B2
Application number	US-202117225229-A
Country	US
Kind code	B2
Filing date	Apr 8, 2021
Priority date	Apr 8, 2021
Publication date	Jun 4, 2024
Grant date	Jun 4, 2024

Abstract

A method comprises receiving a request for validation of software comprising one or more applications, analyzing the request and generating one or more validation steps based at least in part on the analysis. In the method, a time to complete the one or more validation steps is predicted. The predicting is performed using one or more machine learning models, and is based at least in part on a type and a number of the one or more applications.

First claim

What is claimed is:

1. A method comprising:
receiving a request for validation of software comprising one or more applications;
analyzing the request and generating one or more validation steps based at least in part on the analysis; and
predicting a time to complete the one or more validation steps, wherein:
the predicting is performed using one or more machine learning models, and is based at least in part on a type and a number of the one or more applications; and
the predicting comprises inputting a multi-dimensional feature vector comprising at least the type and the number of the one or more applications to the one or more machine learning models;

wherein the one or more machine learning models are trained with at least a first dataset comprising data corresponding to times to complete previous software validations and corresponding to types and numbers of applications associated with the previous software validations;
wherein the one or more machine learning models are trained with at least a second dataset generated through a feedback loop and based at least in part on the predicted time to complete the one or more validation steps; and
wherein the steps of the method are executed by a processing device operatively coupled to a memory.

2. The method of claim 1 wherein the one or more validation steps are performed by one or more microservices.

3. The method of claim 2 wherein the one or more micro services comprise at least one of a cloud deployment service, a message queuing service, a change tracking service, and a workflow orchestration service.

4. The method of claim 2 further comprising calling the one or more microservices to perform the one or more validation steps.

5. The method of claim 1 wherein the predicting is further based at least in part on a type of the one or more validation steps.

6. The method of claim 1 wherein the one or more machine learning models comprise a Random Forest classifier.

7. The method of claim 1 further comprising determining a root cause for failure of the one or more applications during performance of the one or more validation steps, wherein the determining is performed using the one or more machine learning models.

8. The method of claim 7 wherein the one or more machine learning models used in connection with performing the determining comprises a Naive Bayes classifier.

9. The method of claim 7 wherein the determining comprises analyzing one or more failed application error logs.

10. The method of claim 9 wherein the determining further comprises:
parsing the one or more failed application error logs using at least one of tokenization and vectorization; and
inputting data resulting from at least one of the tokenization and the vectorization to the one or more machine learning models.

11. The method of claim 7 further comprising training the one or more machine learning models with data corresponding to at least one of success and failure of applications in the previous software validations.

12. The method of claim 7 wherein the root cause for failure of the one or more applications is determined in real-time following the failure of the one or more applications.

13. The method of claim 1 wherein the one or more validation steps are generated in real-time in response to receiving the request for validation of the software and are based at least in part on software release instructions.

14. An apparatus comprising:
a processing device operatively coupled to a memory and configured to:
receive a request for validation of software comprising one or more applications;
analyze the request and generate one or more validation steps based at least in part on the analysis; and
predict a time to complete the one or more validation steps, wherein:
the predicting is performed using one or more machine learning models, and is based at least in part on a type and a number of the one or more applications; and
in performing the predicting, the processing device is further configured to input a multi-dimensional feature vector comprising at least the type and the number of the one or more applications to the one or more machine learning models;

wherein the one or more machine learning models are trained with at least a first dataset comprising data corresponding to times to complete previous software validations and corresponding to types and numbers of applications associated with the previous software validations; and
wherein the one or more machine learning models are trained with at least a second dataset generated through a feedback loop and based at least in part on the predicted time to complete the one or more validation steps.

15. The apparatus of claim 14 wherein the processing device is further configured to determine a root cause for failure of the one or more applications during performance of the one or more validation steps, and wherein the determining is performed using the one or more machine learning models.

16. The apparatus of claim 15 wherein, in determining the root cause for failure of the one or more applications, the processing device is further configured to analyze one or more failed application error logs.

17. The apparatus of claim 15 wherein the root cause for failure of the one or more applications is determined in real-time following the failure of the one or more applications.

18. An article of manufacture comprising a non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code of the one or more software programs, when executed by at least one processing device, causes the at least one processing device to perform the steps of:
receiving a request for validation of software comprising one or more applications;
analyzing the request and generating one or more validation steps based at least in part on the analysis; and
predicting a time to complete the one or more validation steps, wherein:
the predicting is performed using one or more machine learning models, and is based at least in part on a type and a number of the one or more applications; and
in performing the predicting, the program code of the one or more software programs further causes the at least one processing device to perform the step of inputting a multi-dimensional feature vector comprising at least the type and the number of the one or more applications to the one or more machine learning models;

wherein the one or more machine learning models are trained with at least a first dataset comprising data corresponding to times to complete previous software validations and corresponding to types and numbers of applications associated with the previous software validations; and
wherein the one or more machine learning models are trained with at least a second dataset generated through a feedback loop and based at least in part on the predicted time to complete the one or more validation steps.

19. The article of manufacture of claim 18 wherein the program code of the one or more software programs further causes the at least one processing device to perform the step of determining a root cause for failure of the one or more applications during performance of the one or more validation steps, and wherein the determining is performed using the one or more machine learning models.

20. The article of manufacture of claim 18 wherein the one or more validation steps are performed by one or more microservices.

Assignees

• Dell Products Lp

Inventors

• Roy Baishali
• Tripathy Antarlina
• Sekhar Panguluru Vijaya
• Geetanjali Shivangi
• Kumar Sowmya
• Gaddam Sambasivarao
• Maharana Shivangi
• Panda Sashibhusan
• Parhi Shishir Kumar
• Reyyi Harikrishna
• Srinivasan Seshadri
• Mohanty Bijan Kumar
• Dinh Hung
• Kumari Sweta

Classifications

• G06F11/079 Primary

  Root cause analysis, i.e. error or fault diagnosis (in a hardware test environment G06F11/22; in a software test environment G06F11/36) · CPC title

• G06F11/0769

  Readable error formats, e.g. cross-platform generic formats, human understandable formats · CPC title

• G06F18/24

  Classification techniques · CPC title

• G06N20/00

  Machine learning · CPC title

• G06N20/20 Primary

  Ensemble learning · CPC title