Early in the development of Hoare logic, Owicki and Gries introduced auxiliary variables as a way of encoding information about the history of a program's execution that is useful for verifying its correctness. Over a decade later, Abadi and Lamport observed that it is sometimes also necessary to know in advance what a program will do in the future. To address this need, they proposed prophecy variables, originally as a proof technique for refinement mappings between state machines. However, despite the fact that prophecy variables are a clearly useful reasoning mechanism, there is (surprisingly) almost no work that attempts to integrate them into Hoare logic. In this paper, we present the first account of prophecy variables in a Hoare-style program logic that is flexible enough to verify logical atomicity (a relative of linearizability) for classic examples from the concurrency literature like RDCSS and the Herlihy-Wing queue. Our account is formalized in the Iris framework for separation logic in Coq. It makes essential use of ownership to encode the exclusive right to resolve a prophecy, which in turn lets us enforce soundness of prophecies with a very simple set of proof rules.

Dependent Object Types (DOT) is intended to be a core calculus for modelling Scala. Its distinguishing feature is abstract type members, elds in objects that hold types rather than values. Proving soundness of DOT has been surprisingly challenging, and existing proofs are complicated, and reason about multiple concepts at the same time (e.g. types, values, evaluation). To serve as a core calculus for Scala, DOT should be easy to experiment with and extend, and therefore its soundness proof needs to be easy to modify. is paper presents a simple and modular proof strategy for reasoning in DOT. e strategy separates reasoning about types from other concerns. It is centred around a theorem that connects the full DOT type system to a restricted variant in which the challenges and paradoxes caused by abstract type members are eliminated. Almost all reasoning in the proof is done in the intuitive world of this restricted type system. Once we have the necessary results about types, we observe that the other aspects of DOT are mostly standard and can be incorporated into a soundness proof using familiar techniques known from other calculi.Our paper comes with a machine-veri ed version of the proof in Coq.

Relying on ubiquitous Internet connectivity, applications on mobile devices frequently perform web requests during their execution. They fetch data for users to interact with, invoke remote functionalities, or send user-generated content or metadata. These requests collectively reveal common practices of mobile application development, like what external services are used and how, and they point to possible negative effects like security and privacy violations, or impacts on battery life. In this paper, we assess different ways to analyze what web requests Android applications make. We start by presenting dynamic data collected from running 20 randomly selected Android applications and observing their network activity. Next, we present a static analysis tool, Stringoid, that analyzes string concatenations in Android applications to estimate constructed URL strings. Using Stringoid, we extract URLs from 30, 000 Android applications, and compare the performance with a simpler constant extraction analysis. Finally, we present a discussion of the advantages and limitations of dynamic and static analyses when extracting URLs, as we compare the data extracted by Stringoid from the same 20 applications with the dynamically collected data.

Abstract. ConsAs Scala gains popularity, there is growing interest in programming tools for it. Such tools often require call graphs. However, call graph construction algorithms in the literature do not handle Scala features, such as traits and abstract type members. Applying existing call graph construction algorithms to the JVM bytecodes generated by the Scala compiler produces very imprecise results due to type information being lost during compilation. We adapt existing call graph construction algorithms, Name-Based Resolution (RA) and Rapid Type Analysis (RTA), for Scala, and present a formalization based on Featherweight Scala. We evaluate our algorithms on a collection of Scala programs. Our results show that careful handling of complex Scala constructs greatly helps precision and that our most precise analysis generates call graphs with 1.1-3.7 times fewer nodes and 1.5-18.7 times fewer edges than a bytecode-based RTA analysis.