Abstract: With the development of the Internet,network security is increasingly concerned,and cracking down on malicious code authors is an important part of it.At present,author identification through malicious code writing style has achieved remarkable results.However,if we want to understand the author's real information,we need to analyze his social attributes and form a perfect portrait.Gender,as the key classification index of human social attributes,is an important part of individual real information.Other social attributes are basically associated with gender characteristics,and the distinction of gender has become a necessary prerequisite for further exploring other social attributes.This study conducts an in-depth analysis of programmers' source code writing styles and summarizes 22 gender recognition association features of source code authors.Based on the author's gender re-cognition association feature,the adaptive enhancement algorithm(AdaBoost) is used to train the source code author's gender re-cognition classifier,ensuring high recognition rate while improving model robustness.At the same time,it is compared with natural language gender recognition algorithms to highlight the applicability of gender recognition features of source code authors.This study collectes a total of 115 004 and 22 700 Java and C++source code files with gender labels from Github,providing the academic community with the first research dataset with gender labels for source code authors.The proposed method shows good performance on the collected C++ and Java data sets,reaching 98% and 94% accuracy respectively.The conclusion of this study explores the mapping from source code author style to other social attributes,which is helpful to guide the further research from source code author style to other social attributes.

Key words: Software security, Software forensics, Source code author attribution, Source code author gender identification, Feature representation