Abstract: A software performance verification system extracts a partial code as part of a code of a program configuring software, generates a feature vector based on the partial code, and generates, as a verification result of the partial code, information based on output obtained from a performance verification model through input of the partial code as a verification target to the performance verification model that is a machine learning model having been trained by use of learning data that includes the feature vector of the partial code for learning and performance information indicative of the performance of software implemented on the basis of the partial code. The software performance verification system generates, for example, a feature vector having metrics values acquired from the partial code as the elements of the feature vector. The performance verification model outputs, for example, the probability of there being a problem with a process to be implemented on the basis of the partial code.

Abstract: A learning dataset generation support device 100 is configured to include: a storage device 101 that is configured to store a plurality of pieces of learning data used for supervised machine learning along with correct answer labels; and a computing device 104 that is configured to perform a process of sequentially acquiring the pieces of learning data from the storage device to extract feature vectors, an editing process of adding and/or deleting a feature vector according to a predetermined algorithm, and a process of generating learning data from the edited feature vectors.