公开(公告)号：US10909022B2

公开(公告)日：2021-02-02

申请号：US15702375

申请日：2017-09-12

Applicant: Facebook, Inc.

Inventor： Hangjin Zhang ,  Donghang Guo ,  Jeffrey Scott Dunn ,  Jonathan Michael Wiepert ,  Luke Reynold Styles

IPC: G06F8/34 ,  G06F8/65 ,  G06F8/71 ,  G06F11/36

Abstract: A computer-implemented method for identifying and tracking application performance incidents may include (1) receiving, by an incident tracking system, data representative of a time series, the time series including a time-ordered plurality of values of a performance metric associated with a program, (2) identifying, by the incident tracking system, a discontinuity in the time series, (3) associating, by the incident tracking system, the identified discontinuity in the time series with a change in source code associated with the program, and (4) executing, by the incident tracking system, an automated action based on the association of the identified discontinuity with the change in the source code. Various other methods, systems, and computer-readable media are also disclosed.

公开(公告)号：US20190079850A1

公开(公告)日：2019-03-14

申请号：US15702375

申请日：2017-09-12

Applicant: Facebook, Inc.

Inventor： Hangjin Zhang ,  Donghang Guo ,  Jeffrey Scott Dunn ,  Jonathan Michael Wiepert ,  Luke Reynold Styles

IPC: G06F11/36 ,  G06F9/44

Abstract: A computer-implemented method for identifying and tracking application performance incidents may include (1) receiving, by an incident tracking system, data representative of a time series, the time series including a time-ordered plurality of values of a performance metric associated with a program, (2) identifying, by the incident tracking system, a discontinuity in the time series, (3) associating, by the incident tracking system, the identified discontinuity in the time series with a change in source code associated with the program, and (4) executing, by the incident tracking system, an automated action based on the association of the identified discontinuity with the change in the source code. Various other methods, systems, and computer-readable media are also disclosed.

公开(公告)号：US10372585B2

公开(公告)日：2019-08-06

申请号：US15806292

申请日：2017-11-07

Applicant: Facebook, Inc.

Inventor： Jeffrey Scott Dunn ,  Hangjin Zhang ,  Eun Chang Lee ,  David Ross Harrington ,  Joel F. Beales ,  Lifei Huang

IPC: G06F9/44 ,  G06F11/36

Abstract: A system and method that automatically detects that a regression incident has occurred, and identifies the particular revision(s) of source code that introduced the regression incident is disclosed herein. The system and method execute one or more simulations of each revision in a window of revisions of the source code to compute values for one or more metrics. The metric values are then used to compute regression confidence values as well as a regression confidence interval—to then detect a regression incident and identify the revision that caused the regression incident. The system and method can also identify subsequent revision(s) that sufficiently fix regression incident(s) caused by a revision such that a software developer can be informed of these fixing revisions.

公开(公告)号：US20190138426A1

公开(公告)日：2019-05-09

申请号：US15806292

申请日：2017-11-07

Applicant: Facebook, Inc.

Inventor： Jeffrey Scott Dunn ,  Hangjin Zhang ,  Eun Chang Lee ,  David Ross Harrington ,  Joel F. Beales ,  Lifei Huang

IPC: G06F11/36

CPC classification number: G06F11/3608 ,  G06F11/36 ,  G06F11/3664

Abstract: A system and method that automatically detects that a regression incident has occurred, and identifies the particular revision(s) of source code that introduced the regression incident is disclosed herein. The system and method execute one or more simulations of each revision in a window of revisions of the source code to compute values for one or more metrics. The metric values are then used to compute regression confidence values as well as a regression confidence interval—to then detect a regression incident and identify the revision that caused the regression incident. The system and method can also identify subsequent revision(s) that sufficiently fix regression incident(s) caused by a revision such that a software developer can be informed of these fixing revisions.