Recommender systems usually base their predictions on user-item interaction, a technique known as collaborative filtering. Vendors that utilize collaborative filtering generally exclusively use their own user-item interactions, but the accuracy of the recommendations may improve if several vendors share their data. Since user-item interaction data is typically privacy sensitive, sharing this data poses a privacy challenge for the collaborating vendors. In this work, we study the use of matrix factorization with multiple vendors under a differential privacy guarantee. Since differential privacy incurs a trade-off between privacy and utility, one obstacle is that the utility loss of the privacy-preserving measure may be greater than the utility gain of collaboration. We show that the empirical evaluation of this property in existing work is questionable, and that these works do not solve the problem. We also demonstrate that in a common experiment setup, the upper bound on the utility gain that can be achieved by collaboration is limited, which places a hard limit on the acceptable utility loss due to privacy preservation. This limit is small enough that even the utility loss in the current state of the art in differentially private matrix factorization in general exceeds it. We conclude with a number of open challenges for future work.

Software testing is widely recognised as a crucial activity for the development of modern software systems, since it points out defects and errors that were made during the development. Testing also improves the reliability and consistency of an application.

However, testing behaviour on mobile devices is not easy because of a number of reasons: (i) manual testing of behaviour requires a lot of valuable time of developers, (ii) the problem of covering all possible operating systems, (iii) covering all possible mobile devices, (iv) and frequently releasing new versions of apps is the industry standard. In addition bugs in mobile apps can cause the reputation of the app on the store to decrease and thereby the the number of downloads. Hence the following question is central to our research: can we automate user interface testing for mobile apps and multiple platforms without human interaction?

To overcome the limitation of multi-platform testing, as well as to help non-developers in the creation of tests, we introduce a novel tool that is able to test behaviour on mobile devices by defining expected scenarios. These scenarios consist of simple human readable instructions that are easy to write and understand by non-developers. These scenarios can then be tested against multiple operating systems and devices by simply connecting a device and pressing a button.