Root cause analysis for correlated development and operations data

1. A computer-implemented method for root cause analysis, the method comprising:

receiving, by a processor, operations data associated with a plurality of applications;

performing a trend analysis on the operations data to determine an operations issue associated with at least one of the plurality of applications; and

performing a root-cause analysis on the operations issue to identify a set of candidate applications from the plurality of applications that may be a cause of the operations issue by:

displaying, on a user dashboard, call graph data associated with the operations data for each candidate application, wherein the call graph data includes one or more weighted features of each candidate application;

selecting, by a user, a root cause feature from the one or more weighted features from a root cause application based at least in part on the call graph data displayed on the user dashboard.

2. The computer-implemented method of claim 1 , wherein performing the trend analysis on the operations data comprises:

applying a peak detection algorithm to the operations data to determine one or more peaks in a trend; and

identifying the operations issue based at least in part on the one or more peaks in the trend.

3. The computer-implemented method of claim 1 , wherein performing the trend analysis on the operations data comprises:

applying a peak prediction algorithm to the operations data to predict one or more future peaks in a trend; and

identifying the operations issue based at least in part on the one or more future peaks in the trend, wherein the trend is a performance characteristic of at least one of the plurality of applications and is part of the operations data.

4. The computer-implemented method of claim 1 , further comprising:

accessing a rules knowledge base; and

determining an actionable recommendation for the root cause feature based at least in part on the root-cause analysis and a rules knowledge base.

5. The computer-implemented method of claim 1 , wherein performing the root cause analysis on the operations issue comprises:

providing root-cause training data as an input to a machine learning model;

applying the machine learning model to the operations issue associated with the plurality of applications to determine the set of candidate applications that may be the cause of the operations issue; and

determining, based at least in part on the machine learning model, a set of features that are likely to be a root cause of the operations issue for each of the candidate applications.

6. The computer-implemented method of claim 5 , further comprising

analyzing the set of features for each candidate application in the set of candidate applications to determine a candidate score for each candidate application and a feature score for each feature in the set of features for each candidate application;

determining a root cause application based at least in part on the candidate score for each candidate application; and

determining a root cause feature for the root cause application based at least in part on the feature score for the root cause feature.

7. The computer-implemented method of claim 1 , wherein the operation issue is selected from a set of pre-defined operation issues.

8. The computer-implemented method of claim 5 , wherein the set of features comprise at least one of a maintainability index, a complexity index, lines of code, calls to a database, recent changes, and code coverage.

9. The computer-implemented method of claim 5 , wherein the root cause training data comprises historical root cause data.

10. A system for root cause analysis comprising:

a processor communicatively coupled to a memory, the processor configured to:

receive operations data associated with a plurality of applications;

perform a trend analysis on the operations data to determine an operations issue associated with at least one of the plurality of applications; and

perform a root-cause analysis on the operations issue to identify a set of candidate applications from the plurality of applications that may be a cause of the operations issue by:

displaying, on a user dashboard, call graph data associated with the operations data for each candidate application, wherein the call graph data includes one or more weighted features of each candidate application;

selecting, by a user, a root cause feature from the one or more weighted features from a root cause application based at least in part on the call graph data displayed on the user dashboard.

11. The system of claim 10 , wherein performing the trend analysis on the operations data comprises:

applying a peak detection algorithm to the operations data to determine one or more peaks in a trend; and

identifying the operations issue based at least in part on the one or more peaks in the trend.

12. The system of claim 10 , wherein performing the trend analysis on the operations data comprises:

applying a peak prediction algorithm to the operations data to predict one or more future peaks in a trend; and

identifying the operations issue based at least in part on the one or more future peaks in the trend, wherein the trend is a performance characteristic of at least one of the plurality of applications and is part of the operations data.

13. The system of claim 10 , further comprising:

accessing a rules knowledge base; and

wherein the processor is further configured to determine an actionable recommendation for the root cause feature based at least in part on the root-cause analysis and a rules knowledge base.

14. A computer program product for root cause analysis, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, wherein the computer readable storage medium is not a transitory signal per se, the program instructions executable by a processor to cause the processor to perform a method comprising:

receiving, by a processor, operations data associated with a plurality of applications;

performing a trend analysis on the operations data to determine an operations issue associated with at least one of the plurality of applications; and

performing a root-cause analysis on the operations issue to identify a set of candidate applications from the plurality of applications that may be a cause of the operations issue by:

displaying, on a user dashboard, call graph data associated with the operations data for each candidate application, wherein the call graph data includes one or more weighted features of each candidate application;

selecting, by a user, a root cause feature from the one or more weighted features from a root cause application based at least in part on the call graph data displayed on the user dashboard.

15. The computer program product of claim 14 , wherein performing the root cause analysis on the operations issue further comprises:

providing root-cause training data as an input to a machine learning model;

applying the machine learning model to the operations issue associated with the plurality of applications to determine the set of candidate applications that may be the cause of the operations issue; and

determining, based at least in part on the machine learning model, a set of features that are likely to be a root cause of the operations issue for each of the candidate applications.

16. The computer program product of claim 15 , further comprising

analyzing the set of features for each candidate application in the set of candidate applications to determine a candidate score for each candidate application and a feature score for each feature in the set of features for each candidate application;

determining a root cause application based at least in part on the candidate score for each candidate application; and

determining a root cause feature for the root cause application based at least in part on the feature score for the root cause feature.

17. The computer program product of claim 14 , wherein the operation issue is selected from a set of pre-defined operation issues.

18. The computer program product of claim 15 , wherein the set of features comprise at least one of a maintainability index, a complexity index, lines of code, calls to a database, recent changes, and code coverage.