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javascript Dead Code Identification, Elimination, and Empirical Assessment

IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 2023年第7期49卷 3692-3714页

作者： Malavolta, Ivano Nirghin, Kishan Scoccia, Gian Luca Romano, Simone Lombardi, Salvatore Scanniello, Giuseppe Lago, Patricia Vrije Univ Amsterdam NL-1081 HV Amsterdam Netherlands Univ LAquila I-37100 LAquila Italy Univ Salerno I-84084 Fisciano Italy Univ Basilicata Potenza I-85100 Italy Vrije Univ Amsterdam Amsterdam Netherlands

Web apps are built by using a combination of HTML, CSS, and javascript. While building modern web apps, it is common practice to make use of third-party libraries and frameworks, as to improve developers' productivity and code quality. Alongside these benefits, the adoption of such libraries results in the introduction of javascript dead code, i.e., code implementing unused functionalities. The costs for downloading and parsing dead code can negatively contribute to the loading time and resource usage of web apps. The goal of our study is two-fold. First, we present Lacuna, an approach for automatically detecting and eliminating javascript dead code from web apps. The proposed approach supports both static and dynamic analyses, it is extensible and can be applied to any javascript code base, without imposing constraints on the coding style or on the use of specific javascript constructs. Second, by leveraging Lacuna we conduct an experiment to empirically evaluate the run-time overhead of javascript dead code in terms of energy consumption, performance, network usage, and resource usage in the context of mobile web apps. We applied Lacuna four times on 30 mobile web apps independently developed by third-party developers, each time eliminating dead code according to a different optimization level provided by Lacuna. Afterward, each different version of the web app is executed on an Android device, while collecting measures to assess the potential run-time overhead caused by dead code. Experimental results, among others, highlight that the removal of javascript dead code has a positive impact on the loading time of mobile web apps, while significantly reducing the number of bytes transferred over the network.

关键词： Terms-Dead code javascript

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Self-Driving Car Navigation With Single-Beam LiDAR and Neural Networks Using javascript

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IEEE ACCESS 2024年 12卷 190203-190219页

作者： Nguyen, Trung Thi Hoa Trang Dao, Thanh Toan Ngo, Thanh Binh Phi, Vu Anh Univ Transport & Commun Fac Elect & Elect Engn Hanoi 10000 Vietnam Hanoi Coll High Technol Fac Elect & Elect Hanoi 100000 Vietnam Michigan State Univ Coll Engn Comp Sci Dept E Lansing MI 48823 USA

This paper presents the development of a system for simulating and visualizing autonomous vehicle navigation using a single-beam LiDAR sensor integrated with an artificial neural network, implemented entirely in javascript. The single-beam LiDAR sensor in the simulation was configured to closely resemble real-world counterparts, such as the YDLIDAR TG30. The proposed neural network architecture, consisting of four output neurons, processes data from ten distinct angular measurements of the LiDAR sensor to predict necessary steering and speed adjustments for autonomous navigation. Furthermore, we introduce an enhanced collision detection algorithm that improves upon traditional methods by providing more precise identification of line segment intersections. Implementing the entire system in javascript demonstrates the viability of utilizing web technologies for complex machine learning tasks, representing a significant advancement in autonomous vehicle research. Experimental results validate the effectiveness of this approach across various scenarios, emphasizing its potential for broader applications in cost-sensitive autonomous systems. This work may also serve as a valuable resource for researchers new to machine learning, contributing to greater accessibility in the development of autonomous vehicles.

关键词： Laser radar Robot sensing systems Navigation Laser beams Collision avoidance Automobiles Autonomous robots Roads Real-time systems Feedforward neural networks Self-driving car single-beam LiDAR deep feedforward neural network javascript autonomous navigation obstacle detection collision detection

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Next-generation antivirus for javascript malware detection based on dynamic features

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KNOWLEDGE AND INFORMATION SYSTEMS 2024年第2期66卷 1337-1370页

作者： de Lima, Sidney M. L. Souza, Danilo M. Pinheiro, Ricardo P. Silva, Sthefano H. M. T. Lopes, Petronio G. de Lima, Rafael D. T. de Oliveira, Jemerson R. Monteiro, Thyago de A. Fernandes, Sergio M. M. Albuquerque, Edison de Q. da Silva, Washington W. A. dos Santos, Wellington P. Univ Fed Pernambuco Elect & Syst Dept Recife Brazil Univ Pernambuco Comp Dept Recife Brazil Univ Fed Pernambuco Biomed Engn Dept Recife Brazil

There are many kinds of Exploit Kits, each one being built with several vulnerabilities, but almost all of them are written in javascript. So, we created an antivirus, endowed with machine learning, expert in detecting javascript malware based on Runtime Behaviors. In our methodology, javascript is executed, in a controlled environment. The goal was to investigate suspicious file behavior. Our antivirus, as a whole, dynamically monitors and ponders 7690 suspicious behaviors that the javascript file can do in Windows 7. As experiments, the authorial antivirus is compared to antiviruses based on deep as based on shallow networks. Our antivirus achieves an average accuracy of 99.75% in the distinction between benign and malware, accompanied by a training time of 8.92 s. Establishing the relationship between accuracy and training time is essential in information security. Eight (8) new malware are released every second. An antivirus with excessive training time can become obsolete even when released. As our proposed model can overcome the limitations of state-of-the-art, our antivirus combines high accuracy and fast training. In addition, the authorial antivirus is able to detect javascript malware, endowed with digital antiforense, such as obfuscates, polymorphic and fileless attacks.

关键词： Malware javascript Sandbox Dynamic features Machine learning Antivirus Computer forensics

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Tail Call Optimization Tailored for Native Stack Utilization in javascript Runtimes

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IEEE ACCESS 2024年 12卷 111801-111817页

作者： Park, Hyukwoo Kim, Seonghyun Samsung Seoul Res & Dev Campus Samsung Res Seoul 06765 South Korea

javascript has significantly evolved, broadening its capabilities. However, the uptake of tail call optimization (TCO) remains limited, largely due to concerns about debugging difficulties and the potential increase in overall complexity. This paper highlights the compelling importance of tail calls within web applications, advocating for TCO as a means to boost performance and enhance memory efficiency. We present an innovative TCO technique that leverages the native stack within javascript engines, capitalizing on the native stack's inherent benefits over heap memory. This technique is carefully crafted to comply with diverse TCO standards, prioritizing simplicity and providing debugging features. We tackle the inherent challenges of TCO and successfully deploy our method in a lightweight javascript engine. Our approach is rigorously evaluated, proving its effectiveness and practicality. Notably, our implementation facilitates considerable memory savings for web applications, comparable to the maximum Resident Set Size (RSS), and achieves an average performance improvement of approximately 19.8% for algorithms that are heavily recursive. This research not only demonstrates the versatility and efficiency of our TCO strategy but also makes a significant contribution to the wider adoption and comprehension of TCO, thereby improving javascript engines' performance and memory management efficiency.

关键词： Tail Engines Runtime Optimization Standards Codes Memory management Code optimization compiler interpreter javascript javascript engine tail call optimization

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Making javascript Render Decisions to Optimize Security-Oriented Crawler Process

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IEEE ACCESS 2024年 12卷 161688-161696页

作者： Aktas, Onur Can, Ahmet Burak S4E TR-06800 Ankara Turkiye Hacettepe Univ Dept Comp Engn TR-06800 Ankara Turkiye

The widespread use of web applications requires important changes in cybersecurity to protect online services and data. In the process of identifying security vulnerabilities in web applications, a systematic approach is employed to detect and mitigate cybersecurity risks. This approach utilizes web crawlers to identify attack vectors. Traditional web crawling methods are resource-intensive and often need to be more efficient in handling dynamic javascript-rich content. Addressing this crucial gap, our study introduces an innovative approach to predict the necessity of javascript rendering, thereby enhancing the effectiveness and efficiency of security-oriented web crawlers. This approach seeks to reduce computational requirements and quicken the security evaluation process through the use of machine learning algorithms. By utilizing a dataset containing the source code from the main pages of 17,160 websites, our experimental results demonstrate a 20% reduction in execution time compared to full javascript rendering, indicating an improvement in resource usage without any significant reduction in coverage. Our methodology significantly improves the efficiency of security-focused web crawlers and helps security scanners to detect security risks of web applications with fewer resources.

关键词： Crawlers Computer security Web pages Rendering (computer graphics) Libraries Application security Uniform resource locators Surveys Indexes Java Machine learning Crawler cyber security javascript machine learning rendering web application security

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COLMARvista: an open source 2D and pseudo-3D NMR spectral processing, visualization, and analysis software in javascript

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JOURNAL OF BIOMOLECULAR NMR 2025年 1-9页

作者： Li, Dawei Brueschweiler, Rafael Ohio State Univ Dept Chem & Biochem Columbus OH 43210 USA Ohio State Univ Dept Biol Chem & Pharmacol Columbus OH 43210 USA

COLMARvista is presented as a new, highly versatile software for the easy and intuitive processing and visual inspection of 2D and pseudo-3D NMR data both for uniformly and non-uniformly sampled datasets. COLMARvista allows fully autonomous processing of spectra, including zero-filling, apodization, water suppression, Fourier transformation, and phase correction. Its full integration with DEEP Picker and Voigt Fitter programs allows the automated deconvolution and reconstruction of the experimental spectra for highly quantitative analysis, from compound concentration determination to the extraction of cross-peak specific relaxation parameters, even for signals affected by significant overlap with other peaks. COLMARvista is based on javascript and, hence, it is computer-architecture and operating-system independent including its advanced graphics. It runs on all recent web browsers and does not require a potentially elaborate operating-system dependent installation. COLMARvista may serve as a paradigm also for other software projects to prevent the stockpiling of once powerful legacy software that became frozen in time, thereby ensuring continuing progress of the NMR field and its software for future generations of NMR spectroscopists.

关键词： NMR software NMR spectral visualization javascript Open-source software

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Dynamic code compression for javascript engine

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SOFTWARE-PRACTICE & EXPERIENCE 2023年第5期53卷 1196-1217页

作者： Park, Hyukwoo Kim, Seonghyun Bae, Boram Samsung Res Samsung Elect Seoul South Korea

Web applications created using web languages such as HTML, CSS, and javascript are widely used regardless of execution environments, owing to their portability and popularity. Since javascript language is conventionally used for complex computations in large-scale web apps, many optimization techniques have been proposed to accelerate the javascript performance. However, most of these optimizations speed up javascript engines at the expense of consuming more memory. Hence, memory consumption becomes an additional concern in the javascript field. Based on the research on memory status, we unearthed that a substantial portion of the heap memory is allocated for javascript source code, particularly in lightweight javascript engines, which may range from 13.2% to a maximal 52.5% of the entire heap. To resolve this memory issue, this article suggests a new memory optimization method, called dynamic code compression that dynamically compresses the source code and keeps its compressed form instead of the source string. A novel heuristic is proposed to timely compress the source code. We also re-design the internal structure of the javascript engine to efficiently compress the source code without incurring any conflict. Using our code compression method, we could reduce the entire heap memory by up to 43.3% and consistently downsize the overall heap size. From an evaluation of standard benchmarks, our approach showed just 2.7% degradation in performance with negligible compression overhead, proving the feasibility of the code compression technique.

关键词： code compression javascript memory optimization web engine

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Efficient Static Vulnerability Analysis for javascript with Multiversion Dependency Graphs

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PROCEEDINGS OF THE ACM ON PROGRAMMING LANGUAGES-PACMPL 2024年第PLDI期8卷 417-441页

作者： Ferreira, Mafalda Monteiro, Miguel Brito, Tiago Coimbra, Miguel E. Santos, Nuno Jia, Limin Fragoso Santos, Jose Univ Lisbon Inst Super Tecn INESC ID Lisbon Portugal Carnegie Mellon Univ Pittsburgh PA USA

While static analysis tools that rely on Code Property Graphs (CPGs) to detect security vulnerabilities have proven effective, deciding how much information to include in the graphs remains a challenge. Including less information can lead to a more scalable analysis but at the cost of reduced effectiveness in identifying vulnerability patterns, potentially resulting in classification errors. Conversely, more information in the graph allows for a more effective analysis but may affect scalability. For example, scalability issues have been recently highlighted in ODGen, the state-of-the-art CPG-based tool for detecting *** vulnerabilities. This paper examines a new point in the design space of CPGs for javascript vulnerability detection. We introduce the Multiversion Dependency Graph (MDG), a novel graph-based data structure that captures the state evolution of objects and their properties during program execution. Compared to the graphs used by ODGen, MDGs are significantly simpler without losing key information needed for vulnerability detection. We implemented ***, a new MDG-based static vulnerability scanner specialized in analyzing npm packages and detecting taint-style and prototype pollution vulnerabilities. Our evaluation shows that *** outperforms ODGen by significantly reducing both the false negatives and the analysis time. Additionally, we have identified 49 previously undiscovered vulnerabilities in npm packages.

关键词： Static Analysis javascript Vulnerability Detection

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Linear Matching of javascript Regular Expressions

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PROCEEDINGS OF THE ACM ON PROGRAMMING LANGUAGES-PACMPL 2024年第PLDI期8卷 1336-1360页

作者： Barriere, Aurele Pit-Claudel, Clement Ecole Polytech Fed Lausanne Lausanne Switzerland

Modern regex languages have strayed far from well-understood traditional regular expressions: they include features that fundamentally transform the matching problem. In exchange for these features, modern regex engines at times suffer from exponential complexity blowups, a frequent source of denial-of-service vulnerabilities in javascript applications. Worse, regex semantics differ across languages, and the impact of these divergences on algorithmic design and worst-case matching complexity has seldom been investigated. This paper provides a novel perspective on javascript's regex semantics by identifying a larger-than-previously-understood subset of the language that can be matched with linear time guarantees. In the process, we discover several cases where state-of-the-art algorithms were either wrong (semantically incorrect), inefficient (suffering from superlinear complexity) or excessively restrictive (assuming certain features could not be matched linearly). We introduce novel algorithms to restore correctness and linear complexity. We further advance the state-of-the-art in linear regex matching by presenting the first nonbacktracking algorithms for matching lookarounds in linear time: one supporting captureless lookbehinds in any regex language, and another leveraging a javascript property to support unrestricted lookaheads and lookbehinds. Finally, we describe new time and space complexity tradeoffs for regex engines. All of our algorithms are practical: we validated them in a prototype implementation, and some have also been merged in the V8 javascript implementation used in Chrome and ***.

关键词： Regex Automata javascript

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Learning How to Listen: Automatically Finding Bug Patterns in Event-Driven javascript APIs

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IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 2023年第1期49卷 166-184页

作者： Arteca, Ellen Schafer, Max Tip, Frank Northeastern Univ Khoury Coll Comp Sci Boston MA 02115 USA GitHub Oxford OX1 4EH England

Event-driven programming is widely practiced in the javascript community, both on the client side to handle UI events and AJAX requests, and on the server side to accommodate long-running operations such as file or network I/O. Many popular event-based APIs allow event names to be specified as free-form strings without any validation, potentially leading to lost events for which no listener has been registered and dead listeners for events that are never emitted. In previous work, Madsen et al. presented a precise static analysis for detecting such problems, but their analysis does not scale because it may require a number of contexts that is exponential in the size of the program. Concentrating on the problem of detecting dead listeners, we present an approach to learn how to use event-based APIs by first mining a large corpus of javascript code using a simple static analysis to identify code snippets that register an event listener, and then applying statistical modeling to identify anomalous patterns, which often indicate incorrect API usage. In a large-scale evaluation on 127,531 open-source javascript code bases, our technique was able to detect 75 anomalous listener-registration patterns, while maintaining a precision of 90.9% and recall of 7.5% over a validation set, demonstrating that a learning-based approach to detecting event-handling bug patterns is feasible. In an additional experiment, we investigated instances of these patterns in 25 open-source projects, and reported 30 issues to the project maintainers, of which 7 have been confirmed as bugs.

关键词： Static analysis Libraries javascript event-driven programming bug finding API modeling

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