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Apps on mobile phones manipulate all sorts of data, including sensitive data,leading to privacy-related concerns. Recent regulations like the European GDPRprovide rules for the processing of personal and sensitive data, like that nosuch data may be leaked without the consent of the user.  Researchers have proposed sophisticated approaches to track sensitive datawithin mobile apps, all of which rely on specific lists of sensitive source andsink API methods. The data flow analysis results greatly depend on these lists'quality. Previous approaches either used incomplete hand-written lists thatquickly became outdated or relied on machine learning. The latter, however,leads to numerous false positives, as we show.  This paper introduces CoDoC, a tool that aims to revive the machine-learningapproach to precisely identify privacy-related source and sink API methods. Incontrast to previous approaches, CoDoC uses deep learning techniques andcombines the source code with the documentation of API methods. Firstly, wepropose novel definitions that clarify the concepts of sensitive source andsink methods. Secondly, based on these definitions, we build a new ground truthof Android methods representing sensitive source, sink, and neither (i.e., nosource or sink) methods that will be used to train our classifier.  We evaluate CoDoC and show that, on our validation dataset, it achieves aprecision, recall, and F1 score of 91% in 10-fold cross-validation,outperforming the state-of-the-art SuSi when used on the same dataset. However,similarly to existing tools, we show that in the wild, i.e., with unseen data,CoDoC performs poorly and generates many false positive results. Our findings,together with time-tested results of previous approaches, suggest thatmachine-learning models for abstract concepts such as privacy fail in practicedespite good lab results.

Negative Results of Fusing Code and Documentation for Learning to  Accurately Identify Sensitive Source and Sink Methods An Application to the  Android Framework for Data Leak Detection