Web end-to-end (e2e) testing evaluates the workflow of a web application. It simulates real-world user scenarios to ensure the application flows behave as expected. However, web e2e tests are notorious for being flaky, i.e., the tests can produce inconsistent results despite no changes to the code. One common type of flakiness is caused by nondeterministic execution orders between the test code and the client-side code under test. In particular, UI-based flakiness emerges as a notably prevalent and challenging issue to fix because the test code has limited knowledge about the client-side code execution. In this paper, we propose WEFix, a technique that can automatically generate fix code for UI-based flakiness in web e2e testing. The core of our approach is to leverage browser UI changes to predict the client-side code execution and generate proper wait oracles. We evaluate the effectiveness and efficiency of WEFix against 122 web e2e flaky tests from seven popular real-world projects. Our results show that WEFix dramatically reduces the overhead (from 3.7$\times$ to 1.25$\times$) while achieving a high correctness (98%).

Today, more than 3 million websites rely on online advertising revenue. Despite the monetary incentives, ads often frustrate users by disrupting their experience, interrupting content, and slowing browsing. To improve ad experiences, leading media associations define Better Ads Standards for ads that are below user expectations. However, little is known about how well websites comply with these standards and whether existing approaches are sufficient for developers to quickly resolve such issues. In this paper, we propose Adhere, a technique that can detect intrusive ads that do not comply with Better Ads Standards and suggest repair proposals. Adhere works by first parsing the initial web page to a DOM tree to search for potential static ads, and then using mutation observers to monitor and detect intrusive (dynamic/static) ads on the fly. To handle ads' volatile nature, Adhere includes two detection algorithms for desktop and mobile ads to identify different ad violations during three phases of page load events. It recursively applies the detection algorithms to resolve nested layers of DOM elements inserted by ad delegations. We evaluate Adhere on Alexa Top 1 Million Websites. The results show that Adhere is effective in detecting violating ads and suggesting repair proposals. Comparing to the current available alternative, Adhere detected intrusive ads on 4,656 more mobile websites and 3,911 more desktop websites, and improved recall by 16.6% and accuracy by 4.2%.

Identifying what front-end library runs on a web page is challenging. Although many mature detectors exist on the market, they suffer from false positives and the inability to detect libraries bundled by packers such as Webpack. Most importantly, the detection features they use are collected from developers' knowledge leading to an inefficient manual workflow and a large number of libraries that the existing detectors cannot detect. This paper introduces PTDETECTOR, which provides the first automated method for generating features and detecting libraries on web pages. We propose a novel data structure, the pTree, which we use as a detection feature. The pTree is well-suited for automation and addresses the limitations of existing detectors. We implement PTDETECTOR as a browser extension and test it on 200 top-traffic websites. Our experiments show that PTDETECTOR can identify packer-bundled libraries, and its detection results outperform existing tools.

WebAssembly is the newest programming language for the Web. It defines a portable bytecode format for use as a compilation target for programs developed in high-level languages such as C, C++, and Rust. As a result, WebAssembly binaries are generally created by WebAssembly compilers rather than being written manually. To port native code to the Web, WebAssembly compilers need to address the differences between the source and target languages and dissimilarities in their execution environments. A deep understanding of the bugs in WebAssembly compilers can help compiler developers determine where to focus development and testing efforts. In this paper, we conduct two empirical studies to understand the characteristics of the bugs found in WebAssembly compilers. First, we perform a qualitative analysis of bugs in Emscripten, the most widely-used WebAssembly compiler. We investigate 146 bug reports in Emscripten related to the unique challenges WebAssembly compilers encounter compared with traditional compilers. Second, we provide a quantitative analysis of 1,054 bugs in three open-source WebAssembly compilers, AssemblyScript, Emscripten, and Rustc/Wasm-Bindgen. We analyze these bugs along three dimensions: lifecycle, impact, and sizes of bug-inducing inputs and bug fixes. These studies deepen our understanding of WebAssembly compiler bugs. We hope that the findings of our study will shed light on opportunities to design practical tools for testing and debugging WebAssembly compilers.