The JavaScript programming language is widely used for web programming and, increasingly, for general purpose computing. As such, improving the correctness, security and performance of JavaScript applications has been the driving force for research in type systems, static analysis and compiler techniques for this language. Many of these techniques aim to reign in some of the most dynamic features of the language, yet little seems to be known about how programmers actually utilize the language or these features. In this paper we perform an empirical study of the dynamic behavior of a corpus of widely-used JavaScript programs, and analyze how and why the dynamic features are used. We report on the degree of dynamism that is exhibited by these JavaScript programs and compare that with assumptions commonly made in the literature and accepted industry benchmark suites.

JavaScript is a highly dynamic language for web-based applications. Innovative implementation techniques for improving its speed and responsiveness have been developed in recent years. Industry benchmarks such as WebKit SunSpider are often cited as a measure of the efficacy of these techniques. However, recent studies have shown that these benchmarks fail to accurately represent the dynamic nature of modern JavaScript applications, and so may be poor predictors of real-world performance. Worse, they may guide the development of optimizations which are unhelpful for real applications. Our goal is to develop a tool and techniques to automate the creation of realistic and representative benchmarks from existing web applications. We propose a record-and-replay approach to capture JavaScript sessions which has sufficient fidelity to accurately recreate key characteristics of the original application, and at the same time is sufficiently flexible that a recording produced on one platform can be replayed on a different one. We describe JSBench, a flexible tool for workload capture and benchmark generation, and demonstrate its use in creating eight benchmarks based on popular sites. Using a variety of runtime metrics collected with instrumented versions of Firefox, Internet Explorer, and Safari, we show that workloads created by JSBench match the behavior of the original web applications.