Understanding the dynamics of JavaScript

Lebresne, Sylvain; Richards, Gregor; Östlund, Johan; Wrigstad, Tobias; Vítek, Jan

doi:10.1145/1570506.1570510

Cited by 8 publications

(4 citation statements)

References 6 publications

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“…Studies by Vitek and coworkers [18] as well as by Jensen and coworkers [15] have shown that recency fits the typical initialization pattern in JavaScript programs where the programmer allocates a number of objects and then initializes them. Quite often, no further properties are added after the initialization and the type of the properties rarely changes.…”

Section: Introducing Recencymentioning

confidence: 81%

Recency Types for Analyzing Scripting Languages

Heidegger

Thiemann

2010

ECOOP 2010 – Object-Oriented Programming

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Abstract. With the current surge of scripting technologies, large programs are being built with dynamically typed languages. As these programs grow in size, semantics-based tools gain importance for detecting programming errors as well as for program understanding. As a basis for such tools, we propose a descriptive type system for an imperative call-by-value lambda calculus with objects. The calculus models essential features of JavaScript, a widely used dynamically-typed language: first-class functions, objects as property maps, and prototypes. Our type system infers precise singleton object types for recently allocated objects. These object types are handled flow-sensitively and change during the objects' initialization phase. The notion of recency provides an automatic criterion to subsume these precise object types to summary object types, which are handled flow-insensitively. The criterion applies on a per-object basis. Thus, the type system identifies a generalized initialization phase for each object during which the change of its value is precisely reflected in the change of its type. Unlike with linear types, summary types may refer to singleton types and vice versa. We prove the soundness of the type system and present a constraintbased inference algorithm. An implementation is available on the web.

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Section: Introducing Recencymentioning

confidence: 81%

Recency Types for Analyzing Scripting Languages

Heidegger

Thiemann

2010

ECOOP 2010 – Object-Oriented Programming

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“…We analyze a variety of applications, including several from Alexa's top 500 websites [18], compute-intensive JavaScript programs, such as a fluid dynamics simulator, and the V8 benchmarks [19]. In contrast to prior JavaScript behavior studies [15], [16], [17], we take a lower-level, data-dependence-driven approach, with an eye toward potential parallelism.…”

Section: Contributions and Summary Of Findingsmentioning

confidence: 99%

“…Lebresne et al examined JavaScript objects, how frequently their properties and data are modified, and the length of their prototype chains in the SunSpider benchmarks and a small set of real web pages [17]. This higher-level analysis focused on objects and their modification in order to ultimately provide a type system for JavaScript.…”

Section: Case Study: Googlewavementioning

confidence: 99%

“…More recently, researchers have sought to characterize the general behavior of JavaScript from the language perspective in order to write better interpreters and Just-In-Time (JIT) compilers [15], [16], [17]. Several of these studies compared the behavior between JavaScript on the web to standard JavaScript benchmarks such as SunSpider and V8.…”

Section: Introductionmentioning

confidence: 99%

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A limit study of JavaScript parallelism

Fortuna

Anderson

Ceze

et al. 2010

IEEE International Symposium on Workload Characterization (IISWC'10)

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Abstract-JavaScript is ubiquitous on the web. At the same time, the language's dynamic behavior makes optimizations challenging, leading to poor performance. In this paper we conduct a limit study on the potential parallelism of JavaScript applications, including popular web pages and standard JavaScript benchmarks. We examine dependency types and looping behavior to better understand the potential for JavaScript parallelization. Our results show that the potential speedup is very encouragingaveraging 8.9x and as high as 45.5x. Parallelizing functions themselves, rather than just loop bodies proves to be more fruitful in increasing JavaScript execution speed. The results also indicate in our JavaScript engine, most of the dependencies manifest via virtual registers rather than hash table lookups.

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Of scripts and programs

Vítek

2009

Proceedings of the 5th Symposium on Dynamic Languages

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Understanding the dynamics of JavaScript

Cited by 8 publications

References 6 publications

Recency Types for Analyzing Scripting Languages

Recency Types for Analyzing Scripting Languages

A limit study of JavaScript parallelism

Of scripts and programs

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