JavaScript is a language that is widely-used for both webbased and standalone applications such as those in the Windows 8 operating system. Analysis of JavaScript has long been known to be challenging due to the language's dynamic nature. On top of that, most JavaScript applications rely on large and complex libraries and frameworks, often written in a combination of JavaScript and native code such as C and C++. Stubs have been commonly employed as a partial specification mechanism to address the library problem; alas, they are tedious and error-prone.However, the manner in which library code is used within applications often sheds light on what library APIs return or pass into callbacks declared within the application. In this paper, we propose a technique which combines pointer analysis with a novel use analysis to handle many challenges posed by large JavaScript libraries. Our techniques have been implemented and empirically validated on a set of 25 Windows 8 JavaScript applications, averaging 1,587 lines of code, together with about 30,000 lines of library code, demonstrating a combination of scalability and precision.

Developers of JavaScript web applications have little tool support for catching errors early in development. In comparison, an abundance of tools exist for statically typed languages, including sophisticated integrated development environments and specialized static analyses. Transferring such technologies to the domain of JavaScript web applications is challenging. In this paper, we discuss the challenges, which include the dynamic aspects of JavaScript and the complex interactions between JavaScript, HTML, and the browser. From this, we present the first static analysis that is capable of reasoning about the flow of control and data in modern JavaScript applications that interact with the HTML DOM and browser API.One application of such a static analysis is to detect typerelated and dataflow-related programming errors. We report on experiments with a range of modern web applications, including Chrome Experiments and IE Test Drive applications, to measure the precision and performance of the technique. The experiments indicate that the analysis is able to show absence of errors related to missing object properties and to identify dead and unreachable code. By measuring the precision of the types inferred for object properties, the analysis is precise enough to show that most expressions have unique types. By also producing precise call graphs, the analysis additionally shows that most invocations in the programs are monomorphic. We furthermore study the usefulness of the analysis to detect spelling errors in the code. Despite the encouraging results, not all problems are solved and some of the experiments indicate a potential for improvement, which allows us to identify central remaining challenges and outline directions for future work.

We present FLIX, a declarative programming language for specifying and solving least fixed point problems, particularly static program analyses. FLIX is inspired by Datalog and extends it with lattices and monotone functions. Using FLIX, implementors of static analyses can express a broader range of analyses than is currently possible in pure Datalog, while retaining its familiar rule-based syntax.We define a model-theoretic semantics of FLIX as a natural extension of the Datalog semantics. This semantics captures the declarative meaning of FLIX programs without imposing any specific evaluation strategy. An efficient strategy is semi-naïve evaluation which we adapt for FLIX. We have implemented a compiler and runtime for FLIX, and used it to express several well-known static analyses, including the IFDS and IDE algorithms. The declarative nature of FLIX clearly exposes the similarity between these two algorithms.

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Abstract. In JavaScript, and scripting languages in general, dynamic field access is a commonly used feature. Unfortunately, current static analysis tools either completely ignore dynamic field access or use overly conservative approximations that lead to poor precision and scalability.We present new string domains to reason about dynamic field access in a static analysis tool. A key feature of the domains is that the equal, concatenate and join operations take O(1) time.Experimental evaluation on four common JavaScript libraries, including jQuery and Prototype, shows that traditional string domains are insufficient. For instance, the commonly used constant string domain can only ensure that at most 21% dynamic field accesses are without false positives. In contrast, our string domain H ensures no false positives for up to 90% of all dynamic field accesses.We demonstrate that a dataflow analysis equipped with the H domain gains significant precision resulting in an analysis speedup of more than 1.5x for 7 out of 10 benchmark programs.