US 12,436,870 B2
Method and apparatus for detecting outliers in a set of runs of software applications
Lionel Vincent, Brie-et-Angonnes (FR); Trong-Ton Pham, Grenoble (FR); and Salim Mimouni, Grenoble (FR)
Assigned to BULL SAS, Les Clayes Sous Bois (FR); and COMMISARIAT À L'ENERGIE ATOMIQUE ET AUX ENERGIES ALTERNATIVES, Paris (FR)
Filed by BULL SAS, Les Clayes Sous Bois (FR)
Filed on Nov. 17, 2022, as Appl. No. 17/988,937.
Claims priority of application No. 21306637 (EP), filed on Nov. 25, 2021.
Prior Publication US 2023/0161683 A1, May 25, 2023
Int. Cl. G06F 9/44 (2018.01); G06F 11/3604 (2025.01)
CPC G06F 11/3616 (2013.01) 8 Claims
OG exemplary drawing
 
1. A method for detecting outlier behavior in a set of executions of one or several applications on an information processing device, implemented by a computer, the method comprising:
triggering said set of executions in collaboration with a profiling tool in order to collect, for each execution, at least one time series of measurement points assigning, for each measurement point, a value to a measured parameter;
automatically formatting the time series obtained for said set, by adjusting by normalization, for each time series, its length, its values, and its number of measurement points;
said adjusting of its length comprising projecting each measurement point of said time series toward a reference interval, said adjusting of its values comprising dividing each value of said numerical series by a total quantity corresponding to all values for said time series, and said adjusting of its number of measurement points comprises interpolating a set of additional measurement points so that the number of measurement points of said time series is equal to the number of measurement points of a longer time series of said set;
calculating a metric between two time series among the time series collected for said set of executions;
detecting an outlier based on a comparison of said metric with a threshold, said outlier being detected when said metric is greater than said threshold;
excluding, for one of said applications, executions of said set for which an outlier has been detected; and
determining a normal behavior of said application from the remaining executions.