Static analysis of JavaScript libraries in a scalable and precise way using loop sensitivity

Park, Changhee; Lee, Hongki; Ryu, Sukyoung

doi:10.1002/spe.2552

Cited by 6 publications

(13 citation statements)

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“…Our experimental evaluation with 12 versions of jQuery, 2 the most popular JavaScript library, and 5 JavaScript benchmarks from a previous work [13], demonstrates that the technique can easily find 144 out of 150 major causes (96 %) in 17 applications by only automatic detection. It also shows that a new analysis using our technique outperforms a state-of-the-art JavaScript analyzer [24] in analyzing 13 out of 17 applications with higher analysis precision.…”

Section: If Notmentioning

confidence: 86%

“…We performed the experiments on a Linux x64 machine with 4.0 GHz Intel Core i7 CPU and 32 GB memory, and report an average of 30 runs for each experiment. We compare Selective with SAFE LSA [24] with the most precise configurations by applying loop-sensitivity to all loops and heap cloning to both heuristically selected allocation sites and identified ones by Selective.…”

Section: Evaluation Methodsmentioning

confidence: 99%

“…Another validity threat is the representativeness of our evaluation targets. While the evaluation targets are also used in various previous studies [1,23,24,31,33], they do not cover the wide spectrum of JavaScript applications. Statically analyzing them may impose other challenges, and our future work includes applying the technique to more various program domains.…”

Section: :20mentioning

confidence: 99%

“…Existing approaches [1,20,23,24,31,32] mainly have focused on adaptively combining existing techniques or designing new techniques in the 2-b phase relying on manual investigation for the 2-a phase. For manual investigation, however, static analyzer developers must keep track of analysis flows to identify causes of precision loss, which is expensive and requires expertise in the complex internal structure of an analyzer.…”

Section: If Notmentioning

confidence: 99%

“…Researchers have developed various static analysis frameworks [11,12,17] for JavaScript, and suggested techniques to improve analysis precision such as loop specialization [1,24,31], adaptive context-sensitivity [32], and advanced analysis domains [20,23]. Analysis precision may be improved by repeating the following process: 1. Check if an analysis is precise enough to prove properties of interest.…”

Section: Introductionmentioning

confidence: 99%

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Automatically Tracing Imprecision Causes in JavaScript Static Analysis

2019

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Researchers have developed various techniques for static analysis of JavaScript to improve analysis precision. To develop such techniques, they first identify causes of the precision losses for unproven properties. While most of the existing work has diagnosed main causes of imprecision in static analysis by manual investigation, manually tracing the imprecision causes is challenging because it requires detailed knowledge of analyzer internals. Recently, several studies proposed to localize the analysis imprecision causes automatically, but these localization techniques work for only specific analysis techniques.In this paper, we present an automatic technique that can trace analysis imprecision causes of JavaScript applications starting from user-selected variables. Given a set of program variables, our technique stops an analysis when any of the variables gets imprecise analysis values. It then traces the imprecise analysis values using intermediate analysis results back to program points where the imprecision first started. Our technique shows the trace information with a new representation called tracing graphs, whose nodes and edges together represent traces from imprecise points to precise points. In order to detect major causes of analysis imprecision automatically, we present four node/edge patterns in tracing graphs for common imprecision causes. We formalized the technique of generating tracing graphs and identifying patterns, and implemented them on SAFE, a state-of-the-art JavaScript static analyzer with various analysis configurations, such as contextsensitivity, loop-sensitivity, and heap cloning. Our evaluation demonstrates that the technique can easily find 96 % of the major causes of the imprecision problems in 17 applications by only automatic detection in tracing graphs using the patterns, and selectively adopting various advanced techniques can eliminate the found causes of imprecision. ACM CCS 2012Software and its engineering → Software verification; Automated static analysis;

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Section: If Notmentioning

confidence: 86%

Section: Evaluation Methodsmentioning

confidence: 99%

Section: :20mentioning

confidence: 99%

Section: If Notmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automatically Tracing Imprecision Causes in JavaScript Static Analysis

2019

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Learning ability of top university students in China: Shanghai Jiao Tong University as a case study

Wang

2022

SN Soc Sci

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The network illustrates the interrelationships among diverse entities and has attracted considerable attention. A wide range of applications makes the network a popular modeling tool, including but not limited to social networks [1], business administration [2], and city construction [3]. In these fields, quantifying network value is essential. By examining network value, companies gain insights into consumer behavior, allowing them to refine marketing strategies accordingly [4]. Similarly, investigating network value can assist urban planners in better understanding the utilization and demands for public facilities, ultimately optimizing city construction [5]. To better understand network value, researchers have established some influential laws that describe network value in terms of the number of neighbors, edges, and subgraphs.Sarnoff 's Law. In the 1940s, David Sarnoff proposed that the value of a broadcasting network is directly proportional to the number of nodes (audience), which is also known as Sarnoff's law [6]. For example, in the case of TV programs, the network value increases linearly with the number of the audience, because the growing number of audiences allows advertisers to access more potential customers. This results in more advertisers, increased revenues, and higher broadcast media demand. Formally, for a network with n nodes, the communication value is Θ(n). This law was initially applied to the film industry, later extended to television, and usually represents one-way communication. Broadcasting can only transmit messages unidirectionally to users, but cannot spread information within users.Metcalfe's Law. With the conferral of the Turing Award on Robert Metcalfe in 2022, Metcalfe's law[7] has regained attention. Metcalfe's law was proposed on the background of the increasing number of Ethernet users and growing attention to the interconnection value of networks. Metcalfe argues that if the value of each node (terminal) is equal, the value of a network is proportional to the number of edges. One of the classic illustrations of Metcalfe's law lies in communication networks, where a network with n users can provide interconnection value proportional to approximately n 2 , i.e. Θ(n 2 ), as each user can communicate with the other n − 1 users in the network. Despite the many challenges associated with the development of the network, such as network scale, connection quality, and network design, Metcalfe's law remains applicable in some scenarios, especially in the field of cloud computing [8]. The increasing number of cloud computing users leads to the availability of more resources, which in turn attracts additional users, creating a virtuous cycle of growth. As a theory for describing the interconnection value of networks, Metcalfe's law is held in high regard and possesses significant implications for communication networks, the Internet, and social networks.Reed's Law. In 2000, David Reed proposed Reed's law [9] for group-forming networks (GFNs). He argued that network value depends not ...

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JSOptimizer: An Extensible Framework for JavaScript Program Optimization

Liu

2019

2019 IEEE/ACM 41st International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)

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Static analysis of JavaScript libraries in a scalable and precise way using loop sensitivity

Cited by 6 publications

References 38 publications

Automatically Tracing Imprecision Causes in JavaScript Static Analysis

Automatically Tracing Imprecision Causes in JavaScript Static Analysis

Learning ability of top university students in China: Shanghai Jiao Tong University as a case study

JSOptimizer: An Extensible Framework for JavaScript Program Optimization

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