Over the last few years, the complexity of web applications has increased to provide more dynamic web applications to users. The drawback of this complexity is the growing number of errors in the front-end applications. In this paper, we present BikiniProxy, a novel technique to provide self-healing for the web. BikiniProxy is designed as an HTTP proxy that uses five self-healing strategies to rewrite the buggy HTML and Javascript code. We evaluate BikiniProxy with a new benchmark of 555 reproducible Javascript errors, DeadClick. We create DeadClick by randomly crawling the Internet and collect all web pages that contain Javascript errors. Then, we observe how BikiniProxy heals those errors by collecting and comparing the traces of the original and healed pages. To sum up, BikiniProxy is a novel fully-automated self-healing approach that is specific to the web, evaluated on 555 real Javascript errors, and based on original self-healing rewriting strategies for HTML and Javascript.
Several studies on client-side Javascript been have been made by @cite_7 @cite_0 @cite_23 . In 2011, they have shown that most websites contain Javascript errors even in the top 100 of Alexa @cite_7 . They have also investigated @cite_0 @cite_23 the nature of the Javascript errors as follows. They manually analyze Javascript bug reports from various web applications and Javascript libraries. They find that the majority of reported Javascript bugs are related to the Document Object Model (DOM). None of this work has explored self-healing strategies for the web.
@cite_0 @cite_23 @cite_7
2472584751 2033890725 2078389019 2548949553
Client-side JavaScript is widely used in web applications to improve user-interactivity and minimize client-server communications. Unfortunately, JavaScript is known to be error-prone. While prior studies have demonstrated the prevalence of JavaScript faults, no attempts have been made to determine their causes and consequences. The goal of our study is to understand the root causes and impact of JavaScript faults and how the results can impact JavaScript programmers, testers and tool developers. We perform an empirical study of 502 bug reports from 19 bug repositories. The bug reports are thoroughly examined to classify and extract information about each bug' cause (the error ) and consequence (the failure and impact ). Our results show that the majority (68 percent) of JavaScript faults are DOM-related , meaning they are caused by faulty interactions of the JavaScript code with the Document Object Model (DOM) . Further, 80 percent of the highest impact JavaScript faults are DOM-related. Finally, most JavaScript faults originate from programmer mistakes committed in the JavaScript code itself, as opposed to other web application components. These results indicate that JavaScript programmers and testers need tools that can help them reason about the DOM. Additionally, developers can use the error patterns we found to design more powerful static analysis tools for JavaScript. Context: Client-side JavaScript is widely used in web applications to improve user-interactivity and minimize client-server communications. Unfortunately, web applications are prone to JavaScript faults. While prior studies have demonstrated the prevalence of these faults, no attempts have been made to determine their root causes and consequences. Objective: The goal of our study is to understand the root causes and impact of JavaScript faults and how the results can impact JavaScript programmers, testers and tool developers. Method: We perform an empirical study of 317 bug reports from 12 bug repositories. The bug reports are thoroughly examined to classify and extract information about the fault's cause (the error) and consequence (the failure and impact). Result: The majority (65 ) of JavaScript faults are DOM-related, meaning they are caused by faulty interactions of the JavaScript code with the Document Object Model (DOM). Further, 80 of the highest impact JavaScript faults are DOM-related. Finally, most JavaScript faults originate from programmer mistakes committed in the JavaScript code itself, as opposed to other web application components such as the server-side or HTML code. Conclusion: Given the prevalence of DOM-related faults, JavaScript programmers need development tools that can help them reason about the DOM. Also, testers should prioritize detection of DOM-related faults as most high impact faults belong to this category. Finally, developers can use the error patterns we found to design more powerful static analysis tools for JavaScript. JavaScript is used in web applications for achieving rich user interfaces and implementing core functionality. Unfortunately, JavaScript code is known to be prone to faults. In an earlier study, we found that over 65 of such faults are caused by the interaction of JavaScript code with the DOM at runtime (DOM-related faults). In this paper, we first perform an analysis of 190 bug reports to understand fixes commonly applied by programmers to these DOM-related faults; we observe that parameter replacements and DOM element validations are common fix categories. Based on these findings, we propose an automated technique and tool, called Vejovis, for suggesting repairs for DOM-based JavaScript faults. To evaluate Vejovis, we conduct a case study in which we subject Vejovis to 22 real-world bugs across 11 applications. We find that Vejovis accurately suggests repairs for 20 out of the 22 bugs, and in 13 of the 20 cases, the correct fix was the top ranked one. JavaScript has become the most popular language used by developers for client and server side programming. The language, however, still lacks proper support in the form of warnings about potential bugs in the code. Most bug finding tools in use today cover bug patterns that are discovered by reading best practices or through developer intuition and anecdotal observation. As such, it is still unclear which bugs happen frequently in practice and which are important for developers to be fixed. We propose a novel semi-automatic technique, called BugAID, for discovering the most prevalent and detectable bug patterns. BugAID is based on unsupervised machine learning using language-construct-based changes distilled from AST differencing of bug fixes in the code. We present a large-scale study of common bug patterns by mining 105K commits from 134 server-side JavaScript projects. We discover 219 bug fixing change types and discuss 13 pervasive bug patterns that occur across multiple projects and can likely be prevented with better tool support. Our findings are useful for improving tools and techniques to prevent common bugs in JavaScript, guiding tool integration for IDEs, and making developers aware of common mistakes involved with programming in JavaScript.