Intelligent DevOps recommendation of insights across software applications and landscapes

Patent metadata

Field	Value
Publication number	US-12561225-B2
Application number	US-202318488159-A
Country	US
Kind code	B2
Filing date	Oct 17, 2023
Priority date	Oct 17, 2023
Publication date	Feb 24, 2026
Grant date	Feb 24, 2026

Abstract

In an implementation of a computer-implemented method: to create extracted data records, an extract filter is instructed to extract relevant data records from log messages of two runs of a software pipeline. To create diff records using the extracted data records, a diff filter is instructed to compare and identify differences in messages between the two runs, where the diff records are amended with labeled data status information of a software pipeline run the extracted data records have been taken from. A recommendation engine is instructed to execute a machine-learning model training with the diff records. The recommendation engine is called to analyze the diff records for a failure-indicator. A determination is made that a failure causing the failure-indicator has been corrected in a later run of the software pipeline. A change is identified in a configuration or version of a software application associated with a correction. A failure-indicator-solution combination is generated.

First claim

What is claimed is:

1 . A computer-implemented method, comprising:
instructing, to create extracted data records, an extract filter to extract relevant data records from log messages of two runs of a software pipeline; instructing, to create diff records using the extracted data records, a diff filter to compare and identify differences in messages between the two runs of a software pipeline, wherein the diff records are amended with labeled data status information of a software pipeline run the extracted data records have been taken from; instructing a recommendation engine to execute a machine-learning model training with the diff records; calling, using the diff records, the recommendation engine to analyze the diff records for a failure-indicator, wherein the failure-indicator is identified as one or more of alerts in log messages of a production software landscape, crash dump recordings, service downtime, service degradation, or alerting events; scanning, using the machine-learning model and from a set of pipeline runs of concurrently executing different software products, use of the failure-indicator of the software pipeline for a solution-indicator-parameter; determining, based on the analysis and a later run of the software pipeline, that a failure causing the failure-indicator has been corrected; identifying a change in a configuration or version of a software application associated with a correction; generating a failure-indicator-solution combination; and determining automatically during a later run of the software pipeline, suspending deployment of the software application if the machine-learning model returns an increased likelihood execution of the software application for failures.

2 . The computer-implemented method of claim 1 , wherein the relevant data records include deployed software application versions and components, error messages, and status information of extracted tasks.

3 . The computer-implemented method of claim 1 , wherein the software pipeline includes deploy, test, and production.

4 . The computer-implemented method of claim 1 , comprising:
generating, upon detection of a same failure-indicator in a different software pipeline execution, a notification of the same failure-indicator; and recommending, using the failure-indicator-solution combination, a recommended solution to the same failure-indicator.

5 . The computer-implemented method of claim 1 , comprising:
monitoring, using the failure-indicator, landscapes, and software pipelines of other software applications for a same failure-indicator.

6 . The computer-implemented method of claim 5 , comprising:
generating, upon detection of the same failure-indicator in landscapes and software pipelines of other software applications, a notification of the same failure-indicator; and recommending, using the failure-indicator-solution combination, a recommended solution to the same failure-indicator.

7 . The computer-implemented method of claim 1 , comprising:
instructing the recommendation engine to re-execute the machine-learning model training after a specified number of software pipeline runs.

8 . A non-transitory, computer-readable medium storing one or more instructions executable by a computer system to perform one or more operations, comprising:
instructing, to create extracted data records, an extract filter to extract relevant data records from log messages of two runs of a software pipeline; instructing, to create diff records using the extracted data records, a diff filter to compare and identify differences in messages between the two runs of a software pipeline, wherein the diff records are amended with labeled data status information of a software pipeline run the extracted data records have been taken from; instructing a recommendation engine to execute a machine-learning model training with the diff records; calling, using the diff records, the recommendation engine to analyze the diff records for a failure-indicator, wherein the failure-indicator is identified as one or more of alerts in log messages of a production software landscape, crash dump recordings, service downtime, service degradation, or alerting events; scanning, using the machine-learning model and from a set of pipeline runs of concurrently executing different software products, use of the failure-indicator of the software pipeline for a solution-indicator-parameter; determining, based on the analysis and a later run of the software pipeline, that a failure causing the failure-indicator has been corrected; identifying a change in a configuration or version of a software application associated with a correction; generating a failure-indicator-solution combination; and determining automatically during a later run of the software pipeline, suspending deployment of the software application if the machine-learning model returns an increased likelihood execution of the software application for failures.

9 . The non-transitory, computer-readable medium of claim 8 , wherein the relevant data records include deployed software application versions and components, error messages, and status information of extracted tasks.

10 . The non-transitory, computer-readable medium of claim 8 , wherein the software pipeline includes deploy, test, and production.

11 . The non-transitory, computer-readable medium of claim 8 , comprising:
generating, upon detection of a same failure-indicator in a different software pipeline execution, a notification of the same failure-indicator; and recommending, using the failure-indicator-solution combination, a recommended solution to the same failure-indicator.

12 . The non-transitory, computer-readable medium of claim 8 , comprising:
monitoring, using the failure-indicator, landscapes, and software pipelines of other software applications for a same failure-indicator.

13 . The non-transitory, computer-readable medium of claim 12 , comprising:
generating, upon detection of the same failure-indicator in landscapes and software pipelines of other software applications, a notification of the same failure-indicator; and recommending, using the failure-indicator-solution combination, a recommended solution to the same failure-indicator.

14 . The non-transitory, computer-readable medium of claim 8 , comprising:
instructing the recommendation engine to re-execute the machine-learning model training after a specified number of software pipeline runs.

15 . A computer-implemented system, comprising:
one or more computers; and one or more computer memory devices interoperably coupled with the one or more computers and having tangible, non-transitory, machine-readable media storing one or more instructions that, when executed by the one or more computers, perform one or more operations, comprising:
instructing, to create extracted data records, an extract filter to extract relevant data records from log messages of two runs of a software pipeline;
instructing, to create diff records using the extracted data records, a diff filter to compare and identify differences in messages between the two runs of a software pipeline, wherein the diff records are amended with labeled data status information of a software pipeline run the extracted data records have been taken from;
instructing a recommendation engine to execute a machine-learning model training with the diff records;
calling, using the diff records, the recommendation engine to analyze the diff records for a failure-indicator, wherein the failure-indicator is identified as one or more of alerts in log messages of a production

Assignees

• Sap Se

Inventors

• Eberlein Peter
• Driesen Volker

Classifications

• G06F8/77

  Software metrics · CPC title

• G06N20/00

  Machine learning · CPC title

• G06F11/0793

  Remedial or corrective actions (recovery from an exception in an instruction pipeline G06F9/3861; by retry G06F11/1402; for recovering from a failure of a protocol instance or entity H04L69/40) · CPC title

• G06F11/302

  where the computing system component is a software system · CPC title

• G06F11/3604

  Analysis of software for verifying properties of programs (testing of software G06F11/3668) · CPC title