On automatically detecting similar Android apps
Detecting similar applications is a challenging problem, since it implies that similar high level features and their low level implementations can be detected and matched automatically. We propose an approach for automatically detecting Closely reLated applications in ANdroid (CLANdroid) by relying on advanced Information Retrieval techniques and five semantic anchors: identifiers, Android APIs, intents, permissions, and sensors. To evaluate CLANdroid we created a benchmark consisting of 14,450 apps along with information on similar apps provided by Google Play. We also compared effectiveness of different semantic anchors for detecting similar apps as perceived by 27 users. The results show that using Android specific semantic anchors are useful for detecting similar Android apps across different categories. We also measured the impact of third party libraries and obfuscated code when identifying similar Android apps, and our results suggest that there is significant difference in the accuracy when third party libraries are excluded.
2016 IEEE 24th International Conference on Program Comprehension (ICPC)