System, method and computer-accessible medium for predicting a team's pull request comments to address program code concerns before submission

Patent metadata

Field	Value
Publication number	US-2021011714-A1
Application number	US-201916508848-A
Country	US
Kind code	A1
Filing date	Jul 11, 2019
Priority date	Jul 11, 2019
Publication date	Jan 14, 2021
Grant date	—

Abstract

An exemplary system, method, and computer-accessible medium for providing feedback on a section(s) of computer code, can include receiving the section(s) of computer code, analyzing a portion(s) of the section(s), and providing the feedback on the analyzed portion using a machine learning procedure. The machine learning procedure can be a recurrent neural network. The portion(s) can be automatically identified (e.g., using a computer). The portion can be identified based on a label(s) associated with the portion(s). The label(s) can be located in a comments section associated with the computer code. The portion(s) can be a topic model associated with the computer code. The feedback can include an approval or a rejection of the portion(s). Semantics of the portion(s) can be identified, and feedback can be provided based on the semantics.

First claim

1 . A non-transitory computer-accessible medium having stored thereon computer-executable instructions for providing feedback on at least one section of computer code, wherein, when a computer arrangement executes the instructions, the computer arrangement is configured to perform procedures comprising:
receiving the at least one section of computer code while the at least one section is being generated by at least one user; identifying at least one portion of the at least one section based on a label associated with the at least one portion, wherein the label is located in a comments section associated with, and separated from, the computer code; analyzing the at least one portion of the at least one section while the at least one section is being generated by the at least one user; generating the feedback on the analyzed portion, in real time while the at least one section is being generated by the at least one user, using a machine learning procedure, wherein the feedback includes at least one of an approval or a rejection of the at least one portion; providing the feedback to the at least one user; and storing the feedback in a file that includes changes made by the at least one user to the at least one portion based on the feedback.

2 . The computer-accessible medium of claim 1 , wherein the machine learning procedure is a recurrent neural network.

3 - 5 . (canceled)

6 . The computer-accessible medium of claim 1 , wherein the at least one portion is a topic model associated with the computer code.

7 . (canceled)

8 . The computer-accessible medium of claim 1 , wherein the computer arrangement is further configured to identify semantics of the at least one portion and provide feedback based on the semantics.

9 . The computer-accessible medium of claim 1 , wherein the computer arrangement is further configured to determine if the at least one portion is expected code or unexpected code using the machine learning procedure.

10 . The computer-accessible medium of claim 1 , wherein the receiving of the at least one section includes continuously receiving the at least one section.

11 . The computer-accessible medium of claim 1 , wherein the computer arrangement is configured to analyze the at least one portion by comparing the at least one portion with a previous portion of a further computer code.

12 . The computer-accessible medium of claim 11 , wherein the computer arrangement is further configured to determine if the at least one portion will be rejected or accepted based on the analysis of the at least one portion.

13 . The computer-accessible medium of claim 1 , wherein the feedback includes highlighting at least one area of the at least one portion using a particular color based on the feedback.

14 . The computer-accessible medium of claim 1 , wherein the feedback includes a text output including a description of any issues determined with the at least one portion.

15 . The computer-accessible medium of claim 1 , wherein the computer arrangement is further configured to generate the machine learning procedure.

16 . The computer-accessible medium of claim 15 , wherein the computer arrangement is configured to generate the machine learning procedure based on manual feedback provided for further computer code.

17 . A method for providing feedback on at least one section of computer code, comprising:
receiving a plurality of further sections of further computer code; generating at least one model for predicting the feedback based on the further sections; receiving at least one section of computer code while the at least one section is being generated by at least one user; identifying at least one portion of the at least one section based on a label associated with the at least one portion, wherein the label is located in a comments section associated with, and separated from, the at least one section of computer code; providing the feedback, in real time while the at least one section is being generated by at least one user, by applying the at least one model to the at least one portion, wherein the feedback includes at least one of an approval or a rejection of the at least one portion; storing the feedback in a file that includes changes made by the at least one user to the at least one portion based on the feedback receiving information regarding a manual review of the feedback; and modifying the at least one model based on the information.

18 . The method of claim 17 , wherein the at least one model is a recurrent neural network.

19 . A system for providing feedback on at least one section of computer code, comprising:
a computer hardware arrangement configured to:
receive the at least one section of computer code from at least one user while the at least one section is being generated by the at least one user;
identifying at least one portion of the at least one section based on a label associated with the at least one portion, wherein the label is located in a comments section associated with, and separated from, the at least one section of computer code;
analyze the at least one portion, while the at least one section is being generated by the at least one user, by comparing the at least one portion with (i) a previous section of a further computer code and (ii) a standard set by an organization associated with the at least one user to determine if the at least one portion will be rejected or accepted;
determine the feedback, in real time while the at least one section is being generated by the at least one user, for the analyzed at least one portion by applying a recurrent neural network to the analyzed at least one section, wherein the feedback includes at least one of an approval or a rejection of the at least one portion; and
store the feedback in a file that includes changes made by the at least one user to the at least one portion based on the feedback.

20 . (canceled)

21 . The computer-accessible medium of claim 1 , wherein the feedback file includes at least one bookmark to the at least one portion.

22 . The method of claim 17 , wherein the feedback file includes at least one bookmark to the at least one portion.

23 . The method of claim 17 , wherein the feedback includes highlighting at least one area of the at least one portion using a particular color based on the feedback.

24 . The system of claim 19 , wherein the feedback file includes at least one bookmark to the at least one portion.

25 . The system of claim 19 , wherein the feedback includes highlighting at least one area of the at least one portion using a particular color based on the feedback.

Assignees

• Capital One Services Llc

Inventors

• Goodsitt Jeremy
• Farivar Reza
• Abad Fardin Abdi Taghi
• Walters Austin

Classifications

• G06N3/044

  Recurrent networks, e.g. Hopfield networks · CPC title

• G06N3/0442

  characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU] · CPC title

• G06N3/09

  Supervised learning · CPC title

• G06N3/08

  Learning methods · CPC title

• G06F8/72

  Code refactoring · CPC title