Taming the Static Analysis Beast

While industrial-strength static analysis over large, real-world codebases has become commonplace, so too have difficult-to-analyze language constructs, large libraries, and popular frameworks. These features make constructing and evaluating a novel, sound analysis painful, error-prone, and tedious. We motivate the need for research to address these issues by highlighting some of the many challenges faced by static analysis developers in today\u27s software ecosystem. We then propose our short- and long-term research agenda to make static analysis over modern software less burdensome

AdSplit: Separating smartphone advertising from applications

A wide variety of smartphone applications today rely on third-party advertising services, which provide libraries that are linked into the hosting application. This situation is undesirable for both the application author and the advertiser. Advertising libraries require additional permissions, resulting in additional permission requests to users. Likewise, a malicious application could simulate the behavior of the advertising library, forging the user's interaction and effectively stealing money from the advertiser. This paper describes AdSplit, where we extended Android to allow an application and its advertising to run as separate processes, under separate user-ids, eliminating the need for applications to request permissions on behalf of their advertising libraries. We also leverage mechanisms from Quire to allow the remote server to validate the authenticity of client-side behavior. In this paper, we quantify the degree of permission bloat caused by advertising, with a study of thousands of downloaded apps. AdSplit automatically recompiles apps to extract their ad services, and we measure minimal runtime overhead. We also observe that most ad libraries just embed an HTML widget within and describe how AdSplit can be designed with this in mind to avoid any need for ads to have native code

Adaptive Context-sensitive Analysis for JavaScript

Context sensitivity is a technique to improve program analysis precision by distinguishing between function calls. A specific context-sensitive analysis is usually designed to accommodate the programming paradigm of a particular programming language. JavaScript features both the object-oriented and functional programming paradigms. Our empirical study suggests that there is no single context-sensitive analysis that always produces precise results for JavaScript applications. This observation motivated us to design an adaptive analysis, selecting a context-sensitive analysis from multiple choices for each function. Our two-staged adaptive context-sensitive analysis first extracts function characteristics from an inexpensive points-to analysis and then chooses a specialized context-sensitive analysis per function based on the heuristics. The experimental results show that our adaptive analysis achieved more precise results than any single context-sensitive analysis for several JavaScript programs in the benchmarks

A Generative Middleware for Heterogeneous and Distributed Services

International audienceModern software-based services increasingly rely on a highly heterogeneous and dynamic interconnection of platforms and devices offering a wide diversity of capabilities ranging from cloud server with virtually unlimited resources down to micro-controllers with only a few KB of RAM. This paper motivates the fact that no single software framework or software engineering approach is suited to span across this range, and proposes an approach which leverages the latest advances in model-driven engineering, generative techniques and models@runtime in order to tame this tremendous heterogeneity. This paper presents a set of languages dedicated to the integration, deployment and continuous operation of existing libraries and components already available and implemented in various languages. The proposed approach is validated on an industrial case study in the eHealth domain, implemented by an industrial partner that provide an qualitative evaluation of the approach. This case study involves a large number of sensors, devices and gateways based on Rasperry Pi, Intel Edison and Arduino