Method for detecting outlier behavior in a set of executions of one or more applications on an information processing device, said method being implemented by an outlier behavior detection device, said devices being connected to each other via communication means and comprised in an information processing platform integrated in a centralized server or distributed over a set of servers, said method comprising the steps of - triggering said set of executions in collaboration with a profiling tool comprised in said information processing platform and connected to said devices, 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 normalization of the length comprising projecting the measurement points of said time series onto a reference interval, said normalization of its values comprising dividing the values of said numerical series by a total quantity corresponding to the set of values for said time series and said normalization of the number of measurement points comprising 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 from a comparison of said metric with a threshold; - excluding, for the same application, executions of said set for which an outlier was detected; and - determining normal application behavior from the remaining executions. - triggering said set of executions in collaboration with a profiling tool comprised in said information processing platform and connected to said devices, 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 normalization of the length comprising projecting the measurement points of said time series onto a reference interval, said normalization of its values comprising dividing the values of said numerical series by a total quantity corresponding to the set of values for said time series and said normalization of the number of measurement points comprising 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 from a comparison of said metric with a threshold; - excluding, for the same application, executions of said set for which an outlier was detected; and - determining normal application behavior from the remaining executions.