TEAM KNOWLEDGE SHARING

• US 20200133441A1
• Filed: 10/25/2019
• Published: 04/30/2020
• Est. Priority Date: 10/25/2018
• Status: Active Grant

• Current Assignee: Affirm, Inc. (Affirm Holdings, Inc.)
• Original Assignee: Manhattan Engineering Incorporated
• Inventors: Smith, Adam, Upadhyaya, Tarak, Lozano, Juan, Hung, Daniel
• Granted Patent: US 11,243,652 B2
• Time in Patent Office: Days
• CPC Class Codes: G06F 11/302 where the computing system component is a software system
• CPC Class Codes: G06F 11/3438 monitoring of user actions tracking the activity of the user H04L67/535
• CPC Class Codes: G06F 11/3476 Data logging G06F11/14, G06F11/2205 take precedence
• CPC Class Codes: G06F 16/2246 Trees, e.g. B+trees
• CPC Class Codes: G06F 16/2379 Updates performed during online database operations; commit processing
• CPC Class Codes: G06F 16/244 Grouping and aggregation
• CPC Class Codes: G06F 16/24573 using data annotations, e.g. user-defined metadata
• CPC Class Codes: G06F 16/24575 using context
• CPC Class Codes: G06F 16/248 Presentation of query results
• CPC Class Codes: G06F 16/252 between a Database Management System and a front-end application
• CPC Class Codes: G06F 16/258 Data format conversion from or to a database
• CPC Class Codes: G06F 3/0481 based on specific properties of the displayed interaction object or a metaphor-based environment, e.g. interaction with desktop elements like windows or icons, or assisted by a cursor's changing behaviour or appearance
• CPC Class Codes: G06F 8/30 Creation or generation of source code
• CPC Class Codes: G06F 9/44526 Plug-ins; Add-ons
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 20/20 Ensemble learning
• CPC Class Codes: G06N 3/042 Knowledge-based neural networks; Logical representations of neural networks
• CPC Class Codes: G06N 3/044 Recurrent networks, e.g. Hopfield networks
• CPC Class Codes: G06N 3/045 Combinations of networks
• CPC Class Codes: G06N 3/084 Backpropagation, e.g. using gradient descent
• CPC Class Codes: G06N 5/022 : Knowledge engineering; Knowledge acquisition
• CPC Class Codes: G06N 5/04 : Inference or reasoning models
• CPC Class Codes: G06N 7/01 : Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: G06Q 10/101 : Collaborative creation, e.g. joint development of products or services

Abstract

Disclosed systems and methods relate to a knowledge sharing system for aggregating and disseminating knowledge related to programming. The knowledge system for aggregating and disseminating knowledge related to programming may organize or index knowledge according to topics. A topic may be any kind of transferrable piece of knowledge that may be shared. In some embodiments, knowledge organized under a topic may be comprised of a record of interactions with computing systems, and in some embodiments, knowledge organized under a topic may be comprised of text documents, code samples, documentation, web sites, records of discussions, or other kinds of reference materials.

Claims

1. A computer-implemented method comprising:

monitoring accesses of source code in a source code editor;

recording the accesses in an electronic journal;

monitoring a result of execution of the source code;

storing the result of execution of the source code in the electronic journal for know-how retrieval.

2. The computer-implemented method of claim 1, wherein the electronical journal comprises data in a time-series format.

3. The computer-implemented method of claim 1, wherein the accesses comprise at least one of an edit event, a navigation event, or a selection event.

4. The computer-implemented method of claim 1, wherein the monitoring accesses in a source code editor is performed by an editor plug-in.

5. The computer-implemented method of claim 1, wherein the monitoring accesses of source code in a source code editor is performed on source code that has not been checked in to a central repository.

6. A computer-implemented method comprising:

monitoring inputs in an operating system terminal;

monitoring outputs in an operating system terminal;

parsing commands input by a user into the operating system terminal to extract a command type;

parsing outputs in the operating system terminal to determine successful completion of the commands or an error;

storing the parsed commands input by the user and parsed outputs in an electronic journal.

7. The computer-implemented method of claim 6, further comprising:

parsing the commands input by the user into an abstract syntax tree.

8. The computer-implemented method of claim 6, further comprising:

determining successful completion of the commands or an error based on a return code.

9. The computer-implemented method of claim 6, further comprising:

determining related topics based on the inputs and outputs;

displaying information about the related topics to a user.

10. A computer-implemented method comprising:

retrieving time-series information about one or more edits made to source code;

identifying one or more topics for the edit based on a code entity or a parameter included in the edit;

displaying the time-series information and the one or more topics to a user.

11. The computer-implemented method of claim 10, further comprising:

displaying a user interface element for the user to add user-edited information about the one or more topics.

12. The computer-implemented method of claim 10, further comprising:

displaying a user interface for editing and annotating the time-series information about one or more edits made to source code;

identifying data in the time-series information as sensitive based on a prediction from a machine learning model;

highlighting the data identified as sensitive;

providing a user interface element for changing the status of the data to sensitive or not sensitive.

13. The computer-implemented method of claim 10, further comprising:

computing an expected utility of prompting the user for entry of additional information about the one or more topics.

14. The computer-implemented method of claim 13, further comprising when the expected utility exceeds a threshold, displaying a user interface element for the user to add user-edited information about the one or more topics.

15. The computer-implemented method of claim 14, wherein the expected utility is based on the frequency that the topic is encountered by other users, the frequency that the topic is viewed by other users, the amount of existing information on the topic, the rate at which users contribute to the topic when prompted, or a computed value of the time of the user.

16. A computer-implemented method comprising:

retrieving time-series information about a command entered in a terminal, wherein the command is multi-layered;

parsing the command into one or more layers;

identifying one or more topics for the command based on the one or more layers;

displaying the time-series information or one or more topics to a user.

17. The computer-implemented method of claim 16, further comprising:

displaying a user interface element for the user to add user-edited information about the one or more topics.

18. The computer-implemented method of claim 16, further comprising:

displaying a user interface for editing and annotating information about a command entered in the terminal.

19. The computer-implemented method of claim 18, further comprising:

identifying data in the time-series information as sensitive based on a prediction from a machine learning model;

highlighting the data identified as sensitive;

providing a user interface element for changing the status of the data to sensitive or not sensitive.

20. The computer-implemented method of claim 16, further comprising:

computing an expected utility of prompting the user for entry of additional information about the one or more topics.

21. The computer-implemented method of claim 20, further comprising when the expected utility exceeds a threshold, displaying a user interface element for the user to add user-edited information about the one or more topics.

22. The computer-implemented method of claim 21, wherein the expected utility is based on the frequency that the topic is encountered by other users, the frequency that the topic is viewed by other users, the amount of existing information on the topic, the rate at which users contribute to the topic when prompted, or a computed value of the time of the user.

23. A computer-implemented method comprising:

aggregating data from a plurality of data sources, the data sources comprising content about a codebase;

the plurality of data sources including at least one of a record of changes in the codebase, text documentation about the codebase, communication channels containing correspondences about the codebase, a crowd-sourced information source about the codebase, a project management data source about the codebase, or analytics about the performance of the codebase;

wherein the plurality of data sources includes an electronic journal storing time-series information about one or more terminal commands entered by other users;

grouping the aggregated data into at least one topic;

displaying the at least one topic to a user.

24. The computer-implemented method of claim 23, further comprising:

displaying a user interface element for the user to add user-edited information about the at least one topic.

25. The computer-implemented method of claim 23, wherein the grouping of aggregated data into at least one topic is performed based on references to a common code entity.

26. The computer-implemented method of claim 23, wherein the grouping of aggregated data into at least one topic is performed based on keywords.

27. The computer-implemented method of claim 23, further comprising:

receiving a request to link two topics covering semantically similar or semantically identical topics;

linking the two topics.