Furthermore, our results show that our approach is very efficient and effective in detecting different types of clones to check the similarity level in Android applications. We evaluate DroidCC clone detection approach on real time data-set and count the Recall and Precision, which is quite significant. Meanwhile it can detect full and partial level similarity between applications. It also detects the similar code fragments, that were injected into many applications, which might be an indication of spreading malware. DroidCC detects type-1, type-2 and type-3 clones in Android apps at the source code level. A prototype has been developed and implemented on the dataset of almost 30,000 top rated Android apps. In this paper, we propose and design DroidCC, a novel clone detection approach in Android applications, that helps to detect different types of clones from APK's source code.
For the course each app must be developed for Android and iOS, normally. To avoid these problems, it is essential to find, identify, evaluate and recover those code clones as early as possible. ent programming languages to target mobile platforms natively (i.e., iOS and Android). It has been noticed that the code clones in Android apps make it difficult to maintain the security flaws in source code.
Android became more popular and widely used operating system.