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Authorship identification is the process of identifying and classifyingauthors through given codes. Authorship identification can be used in a widerange of software domains, e.g., code authorship disputes, plagiarismdetection, exposure of attackers' identity. Besides the inherent challengesfrom legacy software development, framework programming and crowdsourcing modein Android raise the difficulties of authorship identification significantly.More specifically, widespread third party libraries and inherited components(e.g., classes, methods, and variables) dilute the primary code within theentire Android app and blur the boundaries of code written by differentauthors. However, prior research has not well addressed these challenges.  To this end, we design a two-phased approach to attribute the primary code ofan Android app to the specific developer. In the first phase, we put forwardthree types of strategies to identify the relationships between Java packagesin an app, which consist of context, semantic and structural relationships. Apackage aggregation algorithm is developed to cluster all packages that are ofhigh probability written by the same authors. In the second phase, we developthree types of features to capture authors' coding habits and code stylometry.Based on that, we generate fingerprints for an author from its developedAndroid apps and employ several machine learning algorithms for authorshipclassification. We evaluate our approach in three datasets that contain 15,666apps from 257 distinct developers and achieve a 92.5% accuracy rate on average.Additionally, we test it on 2,900 obfuscated apps and our approach can classifyapps with an accuracy rate of 80.4%.

A3Ident: A Two-phased Approach to Identify the Leading Authors of Android Apps