JavaScript Developers Research Articles

Design and Conception of Will Notarization System Based on Ethereum

It is crucial to use blockchain to notarization system. Although a notarized will has the highest legal effect, the notarization process is cumbersome, the notary agency has heavy responsibilities and high costs, which has become a drawback of notarized wills, and which makes many people give up notarized wills. This research will design a will notarization system based on blockchain technology to solve the problem of difficult will notarization. Through demand analysis and scenario analysis, this paper builds the overall architecture of the will notarization system based on blockchain technology and the Ethereum platform. The architecture consists of three parts: the client used by the user, the server provided by the notary office, and the blockchain module. Based on the Truffle framework provided by Ethereum, it will involve multiple links such as HTML front-end development, JavaScript development, solidity smart contract development, Ethereum interface call, network construction, etc., to design the entire system deployment and operation process.

What are the characteristics of highly-selected packages? A case study on the npm ecosystem

With the popularity of software ecosystems, the number of open source components (known as packages) has grown rapidly. Identifying high-quality and well-maintained packages from a large pool of packages to depend on is a basic and important problem, as it is beneficial for various applications, such as package recommendation and package search. However, no systematic and comprehensive work focuses on addressing this problem except in online discussions or informal literature and interviews. To fill this gap, in this paper, we conducted a mixed qualitative and quantitative analysis to understand how developers identify and select relevant open source packages. In particular, we started by surveying 118 JavaScript developers from the npm ecosystem to qualitatively understand the factors that make a package to be highly-selected within the npm ecosystem. The survey results showed that JavaScript developers believe that highly-selected packages are well-documented, receive a high number of stars on GitHub, have a large number of downloads, and do not suffer from vulnerabilities. Then, we conducted an experiment to quantitatively validate the developers’ perception of the factors that make a highly-selected package. In this analysis, we collected and mined historical data from 2,527 packages divided into highly-selected and not highly-selected packages. For each package in the dataset, we collected quantitative data to present the factors studied in the developers’ survey. Next, we used regression analysis to quantitatively investigate which of the studied factors are the most important. Our regression analysis complements our survey results about highly-selected packages. In particular, the results showed that highly-selected packages tend to be correlated by the number of downloads, stars, and how large the package’s readme file is.

JavaScript API Deprecation Landscape: A Survey and Mining Study

JavaScript provides no native deprecation mechanisms. To assess how developers deprecate JavaScript application programming interfaces (APIs), we report the results of a survey and a mining study on JavaScript developers and projects. The results suggest several solutions but no standard solution to deprecate JavaScript APIs.

Helping or not helping? Why and how trivial packages impact the npm ecosystem

Developers often share their code snippets by packaging them and making them available to others through software packages. How much a package does and how big it is can be seen as positive or negative. Recent studies showed that many packages that exist in the npm ecosystem are trivial and may introduce high dependency overhead. Hence, one question that arises is why developers choose to publish these trivial packages. Therefore, in this paper, we perform a developer-centered study to empirically examine why developers choose to publish such trivial packages. Specifically, we ask 1) why developers publish trivial packages, 2) what they believe to be the possible negative impacts of these packages, and 3) how such negative issues can be mitigated. The survey response of 59 JavaScript developers who publish trivial npm packages showed that the main advantages for publishing these trivial packages are to provide reusable components, testing & documentation, and separation of concerns. Even the developers who publish these trivial packages admitted to having issues when they publish such packages, which include the maintenance of multiple packages, dependency hell, finding the right package, and the increase of duplicated packages in the ecosystems. Furthermore, we found that the majority of the developers suggested grouping these trivial packages to cope with the problems associated with publishing them. Then, to quantitatively investigate the impact of these trivial packages on the npm ecosystem and its users, we examine grouping these trivial packages. We found that if trivial packages that are always used together are grouped, the ecosystem can reduce the number of dependencies by approximately 13%. Our findings shed light on the impact of publishing trivial packages and show that ecosystems and developer communities need to rethink their publishing policies since it can negatively impact the developers and the entire ecosystem.

Artificial Intelligence System for College Students’ Physical Fitness and Health Management Based on Physical Measurement Big Data

Most of the current health management products are used in medical institutions and generally do not pay enough attention to the student population. Based on this, this paper designs a student‐oriented and functional autonomous health management system. This paper proposes a personal health management system based on a multidimensional data model based on the main social characteristics of the population with chronic diseases and the actual needs of personal health management for chronic diseases. The value of various health data for health management is deeply analyzed and mined, and a multidimensional model data warehouse is constructed according to relevant national health data standards to create a standard data platform for intelligent health warning and disease risk assessment. This paper researches and designs a closed‐loop personal health management method based on the Plan‐Do‐Check‐Action (PDCA) cycle management model, with detailed functional design in four aspects: health data collection and recording, health assessment, health planning, and tracking and execution. This paper researches health data collection, processing, and storage technologies and adopts HDFS data storage technology, html, css, Java Script, java, and other software development technologies, combined with j Query, UEditor, Date Range Picker, and other plug‐ins, as well as SMS email generation interface, wireless Bluetooth transmission interface, etc. This system web and mobile application platforms are designed and developed. Relational database is used as the system database, and a snowflake‐type multidimensional data model is designed. Finally, the functions and performance of this system were tested, and the development and trial run of the basic version have been completed.

Research on the university intelligent learning analysis system based on AI

The integration of Artificial Intelligence technology and school education had become a future trend, and became an important driving force for the development of education. With the advent of the era of big data, although the relationship between students’ learning status data was closer to nonlinear relationship, combined with the application analysis of artificial intelligence technology, it could be found that students’ living habits were closely related to their academic performance. In this paper, through the investigation and analysis of the living habits and learning conditions of more than 2000 students in the past 10 grades in Information College of Institute of Disaster Prevention, we used the hierarchical clustering algorithm to classify the nearly 180000 records collected, and used the big data visualization technology of Echarts + iView + GIS and the JavaScript development method to dynamically display the students’ life track and learning information based on the map, then apply Three Dimensional ArcGIS for JS API technology showed the network infrastructure of the campus. Finally, a training model was established based on the historical learning achievements, life trajectory, graduates’ salary, school infrastructure and other information combined with the artificial intelligence Back Propagation neural network algorithm. Through the analysis of the training resulted, it was found that the students’ academic performance was related to the reasonable laboratory study time, dormitory stay time, physical exercise time and social entertainment time. Finally, the system could intelligently predict students’ academic performance and give reasonable suggestions according to the established prediction model. The realization of this project could provide technical support for university educators.

A declarative enhancement of JavaScript programs by leveraging the Java metadata infrastructure

Web browsers have evolved into indispensable software platforms for modern applications. Although JavaScript has become a de-facto lingua franca for developing web applications, the JavaScript software development tools and methodologies lag behind those of languages such as Java and C#. In particular, to enhance JavaScript programs with non-functional concerns (e.g., persistence, security, transactions, and logging) JavaScript developers need to modify the source code by hand. Developers then often end up having to maintain the resulting modified version separately for reasons that include poor design, the complexities of program evolution, and fundamental language limitations. The necessity to modify code by hand could be avoided if JavaScript had metadata facilities for developers to specify non-functional concerns declaratively. To address this problem, we present a novel declarative approach, T ransparent A utomated E nhancement for J ava S cript (TAE-JS) that enhances JavaScript programs with the ability to use declarative metadata. The metadata is expressed by means of Java annotations. We have implemented our technique in an open-source plug-in for the Eclipse IDE. This plug-in allows a developer to enhance the original version of a JavaScript program by specifying concerns declaratively using Java annotations. It then analyzes an original version of the program to automatically generate enhanced program versions by adding the declared concerns. We evaluated TAE-JS with four practical examples that enhance JavaScript programs by declaratively adding concerns such as persistence, security, transactions, and logging. In a user study with ten student developers and three professional engineers at PayPal, the participants viewed favorably TAE-JS's flexible declarative enhancement capabilities, seeing them as a valuable mechanism for implementing non-functional concerns in JavaScript programs. Our evaluation results are promising in demonstrating the potential benefits of our approach to complement existing JavaScript development tools.

Open source column: Deep learning in the browser

Having already discussed MatConvNet and Keras, let us continue with an open source framework for deep learning, which takes a new and interesting approach. TensorFlow.js is not only providing deep learning for JavaScript developers, but it's also making applications of deep learning available in the WebGL enabled web browsers, or more specifically, Chrome, Chromium-based browsers, Safari and Firefox. Recently node.js support has been added, so TensorFlow.js can be used to directly control TensorFlow without the browser. TensorFlow.js is easy to install. As soon as a browser is installed one is ready to go. Browser based, cross platform applications, e.g. running with Electron, can also make use of TensorFlow.js without an additional install. The performance, however, depends on the browser the client is running, and memory and GPU on the client device. More specifically, one cannot expect to analyze 4K videos on a mobile phone in real time. While it's easy to install, and it's easy to develop based on TensorFlow.js, there are drawbacks: (i) developers have less control over where the machine learning actually takes place (e.g. on CPU or GPU), that it is running in the same sandbox as all web pages in the browser do, and (ii) that in the current release it still has rough edges and is not considered stable enough to use in production.

Slimming javascript applications: An approach for removing unused functions from javascript libraries

ContextA common practice in JavaScript development is to ship and deploy an application as a large file, called bundle, which is the result of combining the application code along with the code of all the libraries the application depends on. Despite the benefits of having a single bundle per application, this approach leads to applications being shipped with significant portions of code that are actually not used, which unnecessarily inflates the JavaScript bundles and could slow down website loading because of the extra unused code. Although some static analysis techniques exist for removing unused code, our investigations suggest that there is still room for improvements. ObjectiveThe goal of this paper is to address the problem of reducing the size of bundle files in JavaScript applications. MethodIn this context, we define the notion of Unused Foreign Function (UFF) to denote a JavaScript function contained in dependent libraries that is not needed at runtime. Furthermore, we propose an approach based on dynamic analysis that assists developers to identify and remove UFFs from JavaScript bundles. ResultsWe report on a case-study performed over 22 JavaScript applications, showing evidence that our approach can produce size reductions of 26% on average (with reductions going up to 66% in some applications). ConclusionIt is concluded that removing unused foreign functions from JavaScript bundles helps reduce their size, and thus, it can boost the results of existing static analysis techniques.

Identification and Refactoring of Exception Handling Code Smells in JavaScript

Avoid making bad smells is very important in writing exception handling code for dealing with unexpected runtime errors. The task however is challenging and demands proficient programming skills and experience. This is particularly true in developing JavaScript applications because JavaScript is very rich in features as being dynamic, interpreted and object-oriented with first-class functions. What further complicates the situation is the use of event-driven and non-blocking I/O model in Node.js, which is a runtime environment written in server-side JavaScript. Extended from our previous work on exception handling code smells in Java, this study aims at identifying exception handling code smells that can occur in a JavaScript application at either the client side or the server side. The impact to software quality that each smell has is demonstrated with examples. Refactorings corresponding to the identified smells are proposed; their effects to the application, including the robustness level achieved and other benefits gained, are illustrated. The work is intended to serve as a guide in helping JavaScript developers avoid or discover exception handling code smells.

Fast and precise type checking for JavaScript

In this paper we present the design and implementation of Flow, a fast and precise type checker for JavaScript that is used by thousands of developers on millions of lines of code at Facebook every day. Flow uses sophisticated type inference to understand common JavaScript idioms precisely. This helps it find non-trivial bugs in code and provide code intelligence to editors without requiring significant rewriting or annotations from the developer. We formalize an important fragment of Flow's analysis and prove its soundness. Furthermore, Flow uses aggressive parallelization and incrementalization to deliver near-instantaneous response times. This helps it avoid introducing any latency in the usual edit-refresh cycle of rapid JavaScript development. We describe the algorithms and systems infrastructure that we built to scale Flow's analysis.

Growing a language: An empirical study on how (and why) developers use some recently-introduced and/or recently-evolving JavaScript features

We describe an empirical study to understand how different language features in JavaScript are used by developers, with the goal of using this information to assist future extensions of JavaScript. We inspected more than one million unique scripts (over 80 MLOC) from various sources: JavaScript programs in the wild collected by a spider, (supposedly) better JavaScript programs collected from the top 100 URLs from the Alexa list, JavaScript programs with new language features used in Firefox Add-ons, widely used JavaScript libraries, and Node.js applications. Our corpus is larger and more diversified than those in prior studies. We also performed two explanatory studies to understand the reasons behind some of the language feature choices. One study was conducted on 107 JavaScript developers; the other was conducted on 45 developers of Node.js applications. Our study shows that there is a widespread confusion about newly introduced JavaScript features, a continuing misuse of existing problematic features, and a surprising lack of adoption of object-oriented features. It also hints at why developers choose to use language features this way. This information is valuable to the language designers and the stakeholders, e.g., IDE and tool builders, all of whom are responsible for growing a language.

Lightweight Node.js & EJsS-based Web Server for Remote Control Laboratories

This paper presents a new approach to develop a web server for remote Systems Engineering and Automatic Control laboratories based on Node.js and Easy JavaScript Simulations (EJsS). On one hand, Node.js, a JavaScript development and runtime platform to distribute non-blocking data-intensive event-driven real-time applications across different devices, is used 1) as the server that hosts the laboratory web-pages and 2) to support the real-time communication between the controller back-end (application that closes the control loop over the plant) and front-end (graphical user interface that lets students parametrize and observe the behavior of the controllers/systems under study from any web-browser). On the other one, EJsS, a powerful intuitive graphical tool to develop graphical/interactive interfaces and simulations, is used to generate the JavaScript and HTML5 controller front-end. The new strategy, deployable over different operating system (Linux, Mac OS and Windows) and against different controller back-ends (e.g. an application developed in C, a TwinCAT Programable Logic Controller, and a LabVIEW Virtual Instrument), is a lightweight versatile solution to set up a friendly remote control laboratory whose experiments can be accessed by the students from different types of devices (PCs, laptops, tablets and smartphones).

Empirical study of the dynamic behavior of JavaScript objects

Despite the popularity of JavaScript for client-side web applications, there is a lack of effective software tools supporting JavaScript development and testing. The dynamic characteristics of JavaScript pose software engineering challenges such as program understanding and security. One important feature of JavaScript is that its objects support flexible mechanisms such as property changes at runtime and prototype-based inheritance, making it difficult to reason about object behavior. We have performed an empirical study on real JavaScript applications to understand the dynamic behavior of JavaScript objects. We present metrics to measure behavior of JavaScript objects during execution (e.g., operations associated with an object, object size, and property type changes). We also investigated the behavioral patterns of observed objects to understand the coding or user interaction practices in JavaScript software. Copyright © 2015 John Wiley & Sons, Ltd.

Are web applications ready for parallelism?

In recent years, web applications have become pervasive. Their backbone is JavaScript, the only programming language supported by all major web browsers. Most browsers run on desktop or mobile devices with parallel hardware. However, JavaScript is by design sequential, and current web applications make little use of hardware parallelism. Are web applications ready to exploit parallel hardware? We answer the question in two steps: First, we survey 174 web developers about the potential and challenges of using parallelism. Then, we study the performance and computation shape of a set of web applications that are representative for the emerging web. Our findings indicate that emerging web applications do have latent data parallelism, and JavaScript developers' programming style is not a significant impediment to exploiting this parallelism.

Predicting Program Properties from "Big Code"

We present a new approach for predicting program properties from massive codebases (aka "Big Code"). Our approach first learns a probabilistic model from existing data and then uses this model to predict properties of new, unseen programs. The key idea of our work is to transform the input program into a representation which allows us to phrase the problem of inferring program properties as structured prediction in machine learning. This formulation enables us to leverage powerful probabilistic graphical models such as conditional random fields (CRFs) in order to perform joint prediction of program properties. As an example of our approach, we built a scalable prediction engine called JSNice for solving two kinds of problems in the context of JavaScript: predicting (syntactic) names of identifiers and predicting (semantic) type annotations of variables. Experimentally, JSNice predicts correct names for 63% of name identifiers and its type annotation predictions are correct in 81% of the cases. In the first week since its release, JSNice was used by more than 30,000 developers and in only few months has become a popular tool in the JavaScript developer community. By formulating the problem of inferring program properties as structured prediction and showing how to perform both learning and inference in this context, our work opens up new possibilities for attacking a wide range of difficult problems in the context of "Big Code" including invariant generation, decompilation, synthesis and others.

Remote Web-based Control Laboratory for Mobile Devices based on EJsS, Raspberry Pi and Node.js

This paper presents a new approach to develop remote practices for Systems Engineering and Automatic Control laboratories based on Easy JavaScript Simulations (EJsS), Raspberry Pi and Node.js. EJsS is used to create a JavaScript and HTML5 laboratory front-end that lets teachers and students parametrize and observe the behavior of the controllers/systems under study from the web-browsers of their tablets and smartphones. The Raspberry Pi, a low-cost single-board computer, is in charge of running 1) the C program that closes the control loop over the selected plant and 2) the JavaScript laboratory web server that hosts the laboratory front-end and communicates it with the controller. Finally, the JavaScript development and runtime platform Node.js is used 1) to develop and run the Javascript laboratory web sever within the Raspberry Pi and 2) to support the communications between the laboratory front-end and server. The new strategy, tested over a Proportional/Integral/Differential (PID) controller for a vertical mono-rotor plant, provides low-cost real-time support to the controller and friendly remote access from mobile devices to the practices for the students.

Eval begone!

Eval endows JavaScript developers with great power. It allows developers and end-users, by turning text into executable code, to seamlessly extend and customize the behavior of deployed applications as they are running. With great power comes great responsibility, though not in our experience. In previous work we demonstrated through a large corpus study that programmers wield that power in rather irresponsible and arbitrary ways. We showed that most calls to eval fall into a small number of very predictable patterns. We argued that those patterns could easily be recognized by an automated algorithm and that they could almost always be replaced with safer JavaScript idioms. In this paper we set out to validate our claim by designing and implementing a tool, which we call Evalorizer, that can assist programmers in getting rid of their unneeded evals. We use the tool to remove eval from a real-world website and validated our approach over logs taken from the top 100 websites with a success rate over 97% under an open world assumption.

A Novel Multiple-Option Web-based Questionnaire System with IPA and IPA Discrimination Index

In this paper, a novel web-based multiple-option questionnaire by using Asynchronous JavaScript and XML (AJAX) web development technology is designed to allow respondents to choose one or two of five options, resulting in the effect of nine-point scale. In comparison with the conventional paperbased questionnaires, the advantages of this web-based questionnaire are described as follows: 1. The enhancement of IPA reliability; 2. The researchers can timely obtain the basic statistic analysis results; 3. To save paper; 4. To reduce the number of items in a questionnaire with higher range scale; 5. To provide friendly web-based user interface; 6. To enhance the questionnaire’s face validity, in which Importance-Performance Analysis (IPA) model discrimination index can reflect more accurate quantity comparing to the IPA model proposed by Hollenhorst. Further the index can be implemented to compare and distinguish the effects of different types of research events. This research sampled the questionnaires carried out in 2009 Taichung City Teacher Seminar referenced to review and identify the effects of teachers training program and the usage of the budget. When adopting the IPA model with our novel IPA discrimination index, compared our proposed web-based multiple-option questionnaire with traditional single-option questionnaires, it demonstrated that our scheme has better performance obviously. Furthermore, to use our novel IPA discrimination index can indentify and clearly find the ways to improve the quality and figure out a proper way for budget allocation.

EntrezAJAX: direct web browser access to the Entrez Programming Utilities.

Web applications for biology and medicine often need to integrate data from Entrez services provided by the National Center for Biotechnology Information. However, direct access to Entrez from a web browser is not possible due to 'same-origin' security restrictions. The use of "Asynchronous JavaScript and XML" (AJAX) to create rich, interactive web applications is now commonplace. The ability to access Entrez via AJAX would be advantageous in the creation of integrated biomedical web resources. We describe EntrezAJAX, which provides access to Entrez eUtils and is able to circumvent same-origin browser restrictions. EntrezAJAX is easily implemented by JavaScript developers and provides identical functionality as Entrez eUtils as well as enhanced functionality to ease development. We provide easy-to-understand developer examples written in JavaScript to illustrate potential uses of this service. For the purposes of speed, reliability and scalability, EntrezAJAX has been deployed on Google App Engine, a freely available cloud service. The EntrezAJAX webpage is located at http://entrezajax.appspot.com/