International Business Machines Corporation

Manish Anand

Sweta Singh

Pathan Niyazulla Khan

Vaibhav Murlidhar Kulkarni

One or more computer processors identify one or more similar, historical regression tests and historical builds utilizing a computed similarity measure between a regressed build and one or more historical builds conducted on a same release cycle, wherein the identified one or more similar historical regression tests and historical builds are K closest neighbors to the regressed build; predict an elapsed time of the one or more profiled regression tests utilizing a KNN algorithm comprising the K closest neighbors each weighted by a corresponding average distance from a test point and the elapsed time as a target variable; responsive to the predicted elapsed time exceeding an actual elapsed time associated with the regressed build, determine that the regressed build is an actual regression; responsive to determining that the regressed build is not due to variability, apply one or more mitigation actions to the regressed build.

Mitigating regression test variability

A method, computer program product, and system for analyzing performance of a computer system where at a plurality of time intervals, a set of KPIs of a database and database performance outputs are monitored. A database performance value is calculated based on the monitored KPIs and one or more database performance outputs. In response to determining the database performance value is below a first threshold, a KPI correlation coefficient is calculated. The calculated KPI correlation coefficient with the greatest value is selected as a target KPI. In response to determining that a count of functions of the target KPI meets a second threshold, a dynamic tabular representation of the determined target KPI is generated. A correlation matrix is generated based on the generated tabular representation and the plurality of time intervals and a first set of target functions are identified based on the generated correlation matrix.

Root cause analysis of performance problems

A method for analyzing performance of a computer system where at a plurality of time intervals, a set of KPIs of a database and database performance outputs are monitored. A database performance value is calculated based on the monitored KPIs and one or more database performance outputs. In response to determining the database performance value is below a first threshold, a KPI correlation coefficient is calculated. The calculated KPI correlation coefficient with the greatest value is selected as a target KPI. In response to determining that a count of functions of the target KPI meets a second threshold, a dynamic tabular representation of the determined target KPI is generated. A correlation matrix is generated based on the generated tabular representation and the plurality of time intervals and a first set of target functions are identified based on the generated correlation matrix.

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Irving, TX, United States of America

Manish Anand