IP Library Granted Patent US 12,367,277
Granted Patent B2
US 12,367,277 · App. 17/515,826 · Granted Jul 22, 2025

Systems and methods for automated change review for enhanced network and data security

Inventors: Samanthapudi Subba Raju (Telangana, IN); Praveen Rayapudi (Telangana, IN); Ajay Suryadevara (Telangana, IN)
Assignee: BANK OF AMERICA CORPORATION
G06F21/554G06F2221/033
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,367,277
App. No.
17/515,826
Granted
Jul 22, 2025
Kind
B2
Abstract

Systems, methods, and computer program products are provided for providing coding change review. The method includes receiving a code change indicator. The code change indicator is associated with a changed code that has been changed by at least one user. The method also includes determining a coding language of the changed code based on at least one of a keyword library or a code pattern. The method further includes determining a change review indication based on the determined coding language. The change review indicator indicates whether one or more errors exist in the changed code. The method still further includes determining one or more change review actions to carry out based on the change review indication.

Claims (49)

1. A system for providing coding change review, the system comprising:

at least one non-transitory storage device; and

at least one processing device coupled to the at least one non-transitory storage device, wherein the at least one processing device is configured to:

receive a code change indicator, wherein the code change indicator is associated with a changed code that has been changed by at least one user and wherein the code change indicator provides the changed code;

lock the changed code to prevent access by the at least one user;

determine a coding language of the changed code based on a keyword library and a code pattern, wherein the keyword library is compiled via machine learning and the code pattern is learned via machine learning, wherein the keyword library and the code pattern are stored in a single repository;

analyze the changed code to determine an unknown code pattern and execute an update utilizing machine learning;

based on the determined coding language, determine a change review indication, wherein the change review indication indicates whether one or more errors exist in the changed code and the code change indicator comprises a notification with a location of the changed code and changes made in the changed code;

based on the change review indication, determine one or more change review actions to carry out;

determine to bypass the coding change review by a reviewer when the change review indication indicates no errors exist in the changed code and determine one or more remedial measures when the change review indication indicates one or more errors exist in the changed code, wherein the remedial measures identify a location of the one or more errors and a correction to code for implementation;

transfer automatically the one or more change review actions to carry out to the reviewer, wherein the reviewer is selected based on a complexity of a code change associated with the one or more change review actions, an availability of the reviewer, a reviewer rating, and reviewer previously completed change reviews;

reassign the one or more change review actions to carry out to a second reviewer based on a second user input, wherein the second user input comprises manual intervention based on an unexpected unavailability of the reviewer; and

generate a request for rating for the at least one user and a request for rating for the second reviewer, wherein the request for rating for the at least one user comprises a code complexity, an error total, an error resolution difficulty, and a quality of the changed code and the request for rating for the second reviewer comprises a second reviewer language knowledge, a completed review elapsed time, and an error solution quality and the request for rating for the second reviewer is executed by a second user.

2. The system of claim 1 , wherein the keyword library comprises a plurality of language keyword indicators, wherein each language keyword indicator comprises one or more keyword terms associated with a different coding language.

3. The system of claim 1 , wherein the at least one processing device is configured to cause a transmission of the one or more remedial measures to at least one reviewer for review.

4. The system of claim 1 , wherein the one or more change review actions comprise selecting a reviewer to analyze the changed code in response to the change review indication.

5. The system of claim 1 , wherein in an instance in which the change review indication indicates that no errors exist in the changed code, the one or more change review actions comprise approving the changed code for implementation, and

wherein in an instance in which the change review indication indicates that one or more errors exist in the changed code, the one or more change review actions comprise determining a reviewer to analyze the changed code.

6. A computer program product for providing coding change review, the computer program product comprising at least one non-transitory computer-readable medium having computer-readable program code portions embodied therein, the computer-readable program code portions comprising:

an executable portion configured to receive a code change indicator, wherein the code change indicator is associated with a changed code that has been changed by at least one user and wherein the code change indicator provides the changed code;

an executable portion configured to lock the changed code to prevent access by the at least one user;

an executable portion configured to determine a coding language of the changed code based on a keyword library and a code pattern, wherein the keyword library is compiled via machine learning and the code pattern is learned via machine learning, wherein the keyword library and the code pattern are stored in a single repository;

an executable portion configured to determine a change review indication based on at least one of a keyword library and a code pattern, wherein the keyword library is compiled via machine learning and the code pattern is learned via machine learning, wherein the keyword library and the code pattern are stored in a single repository;

an executable portion configured to analyze the changed code to determine an unknown code pattern and execute an update utilizing machine learning;

an executable portion configured to determine one or more change review actions to carry out based on the change review indication;

an executable portion configured to determine to bypass the coding change review by a reviewer when the change review indication indicates no errors exist in the changed code and determine one or more remedial measures when the change review indication indicates one or more errors exist in the changed code, wherein the remedial measures identify a location of the one or more errors and a correction to code for implementation;

an executable portion configured to transfer automatically the one or more change review actions to carry out to the reviewer, wherein the reviewer is selected based on a complexity of a code change associated with the one or more change review actions, an availability of the reviewer, a reviewer rating, and reviewer previously completed change reviews;

an executable portion configured to reassign the one or more change review actions to carry out to a second reviewer based on a second user input, wherein the second user input comprises manual intervention based on an unexpected unavailability of the reviewer; and

an executable portion configured to generate a request for rating for the at least one user and a request for rating for the second reviewer, wherein the request for rating for the at least one user comprises a code complexity, an error total, an error resolution difficulty, and a quality of the changed code and the request for rating for the second reviewer comprises a second reviewer language knowledge, a completed review elapsed time, and an error solution quality and the request for rating for the second reviewer is executed by a second user.

7. The computer program product of claim 6 , wherein the keyword library comprises a plurality of language keyword indicators, wherein each language keyword indicator comprises one or more keyword terms associated with a different coding language.

8. The computer program product of claim 6 , wherein the computer-readable program code portions further comprise an executable portion configured to cause a transmission of the one or more remedial measures to at least one reviewer for review.

9. The computer program product of claim 6 , wherein the one or more change review actions comprise selecting a reviewer to analyze the changed code in response to the change review indication.

10. The computer program product of claim 6 , wherein in an instance in which the change review indication indicates that no errors exist in the changed code, the one or more change review actions comprise approving the changed code for implementation, and

wherein in an instance in which the change review indication indicates that one or more errors exist in the changed code, the one or more change review actions comprise determining a reviewer to analyze the changed code.

11. A computer-implemented method for providing coding change review, the method comprising:

receiving a code change indicator, wherein the code change indicator is associated with a changed code that has been changed by at least one user and wherein the code change indicator provides the changed code;

locking the changed code to prevent access by the at least one user;

determining a coding language of the changed code based on at least one of a keyword library and a code pattern, wherein the keyword library is compiled via machine learning and the code pattern is learned via machine learning, wherein the keyword library and the code pattern are stored in a single repository;

analyzing the changed code to determine an unknown code pattern and execute an update utilizing machine learning;

based on the determined coding language, determining a change review indication, wherein the change review indication indicates whether one or more errors exist in the changed code and the code change indicator comprises a notification with a location of the changed code and changes made in the changed code;

based on the change review indication, determining one or more change review actions to carry out;

determining to bypass the coding change review by a reviewer when the change review indication indicates no errors exist in the changed code and determine one or more remedial measures when the change review indication indicates one or more errors exist in the changed code, wherein the remedial measures identify a location of the one or more errors and a correction to code for implementation;

transferring automatically the one or more change review actions to carry out to the reviewer, wherein the reviewer is selected based on a complexity of a code change associated with the one or more change review actions, an availability of the reviewer, a reviewer rating, and reviewer previously completed change reviews;

reassigning the one or more change review actions to carry out to a second reviewer based on a second user input, wherein the second user input comprises manual intervention based on an unexpected unavailability of the reviewer; and

generating a request for rating for the at least one user and a request for rating for the second reviewer, wherein the request for rating for the at least one user comprises a code complexity, an error total, an error resolution difficulty, and a quality of the changed code and the request for rating for the second reviewer comprises a second reviewer language knowledge, a completed review elapsed time, and an error solution quality and the request for rating for the second reviewer is executed by a second user.

12. The method of claim 11 , wherein the keyword library comprises a plurality of language keyword indicators, wherein each language keyword indicator comprises one or more keyword terms associated with a different coding language.

13. The method of claim 11 , further comprising causing a transmission of the one or more remedial measures to at least one reviewer for review.

14. The method of claim 11 , wherein in an instance in which the change review indication indicates that no errors exist in the changed code, the one or more change review actions comprise approving the changed code for implementation, and

wherein in an instance in which the change review indication indicates that one or more errors exist in the changed code, the one or more change review actions comprise determining a reviewer to analyze the changed code.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Nov 1, 2021
From: RAJU, SAMANTHAPUDI SUBBA; RAYAPUDI, PRAVEEN; SURYADEVARA, AJAY
To: BANK OF AMERICA CORPORATION
Reel/Frame 057981/0141 →
Continuity (1)
Related Publication 20230137566A1 · May 4, 2023
References Cited (41)
US 6269474B1 · Price · 2001 [cited by applicant]
US 7650339B2 · Cho et al. · 2010 [cited by applicant]
US 8200775B2 · Moore · 2012 [cited by applicant]
US 8307351B2 · Weigert · 2012 [cited by applicant]
US 8856725B1 · Anderson et al. · 2014 [cited by applicant]
US 9021458B1 · Jacob et al. · 2015 [cited by applicant]
US 9182979B2 · Odenheimer et al. · 2015 [cited by applicant]
US 9542176B2 · Bird et al. · 2017 [cited by applicant]
US 9600275B2 · Fan et al. · 2017 [cited by applicant]
US 9632771B2 · Toub · 2017 [cited by applicant]
US 9886269B2 · Grillo et al. · 2018 [cited by applicant]
US 9898280B2 · Balachandran · 2018 [cited by examiner]
US 9916224B2 · Macleod · 2018 [cited by examiner]
US 9952961B2 · Atyam et al. · 2018 [cited by applicant]
US 10761810B2 · Ramakrishna et al. · 2020 [cited by applicant]
US 10776498B2 · Wasiq et al. · 2020 [cited by applicant]
US 10901727B2 · Biddle et al. · 2021 [cited by applicant]
US 10915316B1 · Turner et al. · 2021 [cited by applicant]
US 11024424B2 · Sun et al. · 2021 [cited by applicant]
US 11392844B1 · Rao · 2022 [cited by examiner]
US 20070168946A1 · Drissi · 2007 [cited by examiner]
US 20160179505A1 · Grillo · 2016 [cited by examiner]
US 20180285103A1 · Jha · 2018 [cited by examiner]
US 20180349789A1 · Chen · 2018 [cited by examiner]
US 20190228319A1 · Gupta et al. · 2019 [cited by applicant]
US 20190303140A1 · Kelly · 2019 [cited by examiner]
US 20200379879A1 · Plotnik · 2020 [cited by examiner]
US 20200394588A1 · Sanchez · 2020 [cited by examiner]
US 20210019249A1 · Gnaneswaran · 2021 [cited by examiner]
US 20210089992A1 · Xu · 2021 [cited by examiner]
US 20210124561A1 · Pezaris · 2021 [cited by examiner]
US 20210124830A1 · Dinh · 2021 [cited by examiner]
US 20210149666A1 · Goern · 2021 [cited by examiner]
US 20210311729A1 · Gupta · 2021 [cited by examiner]
US 20210334367A1 · Chibon · 2021 [cited by examiner]
US 20220043646A1 · James · 2022 [cited by examiner]
US 20220164183A1 · Talmor · 2022 [cited by examiner]
US 20220198269A1 · Canedo · 2022 [cited by examiner]
US 20220245055A1 · Chen · 2022 [cited by examiner]
US 20230418725A1 · Freeling · 2023 [cited by examiner]
Rebai, S., Amich, A., Molaei, S. et al. Multi-objective code reviewer recommendations: balancing expertise, availability and collaborations. Autom Softw Eng 27, No. 3-4, 301-328. Sep. 5, 2020. pp. 1, 2, and 6 (Year: 202… [cited by examiner]
Cited By (1)
US 12,645,561