End-to-end (E2E) testing is a software validation approach that simulates realistic user scenarios throughout the entire workflow of an application. In the context of web applications, E2E testing involves two activities: Graphic User Interface (GUI) testing, which simulates user interactions with the web app’s GUI through web browsers, and performance testing, which evaluates system workload handling. Despite its recognized importance in delivering high-quality web applications, the availability of large-scale datasets featuring real-world E2E web tests remains limited, hindering research in the field.To address this gap, we present E2EGit, a comprehensive dataset of non-trivial open-source web projects collected on GitHub that adopt E2E testing. By analyzing over 5,000 web repositories across popular programming languages (Java, JavaScript, TypeScript and Python), we identified 472 repositories implementing 43,670 automated Web GUI tests with popular browser automation frameworks (Selenium, Playwright, Cypress, Puppeteer), and 84 repositories that featured 271 automated performance tests implemented leveraging the most popular open-source tools (JMeter, LoCust). Among these, 13 repositories implemented both types of testing for a total of 786 Web GUI tests and 61 performance tests. The dataset is available on Zenodo (DOI: 10.5281/zenodo.14234731).

It is recognized in the literature that finding representative data to conduct regression testing research is non-trivial. In our experience within this field, existing datasets are often affected by issues that limit their applicability. Indeed, these datasets often lack fine-grained coverage information, reference software repositories that are not available anymore, or do not allow researchers to readily build and run the software projects, e.g., to obtain additional information. As a step towards better replicability and data-availability in regression testing research, we introduce ReCover, a dataset of 114 pairs of subsequent versions from 28 open source Java projects from GitHub. In particular, ReCover is intended as a consolidation and enrichment of recent dedicated regression testing datasets proposed in the literature, to overcome some of the above described issues, and to make them ready to use with a broader number of regression testing techniques. To this end, we developed a custom mining tool, that we make available as well, to automatically process two recent, massive regression testing datasets, retaining pairs of software versions for which we were able to (1) retrieve the full source code; (2) build the software in a general-purpose Java/Maven environment (which we provide as a Docker container for ease of replication); and (3) compute fine-grained test coverage metrics. ReCover can be readily employed in regression testing studies, as it bundles in a single package full, buildable source code and detailed coverage reports for all the projects. We envision that its use could foster regression testing research, improving replicability and long-term data availability.

Current vehicles are equipped with a number of environmental sensors to improve safety and quality of life for passengers. Many researchers have shown that these sensors can also be exploited for opportunistic crowd-sensing. Useful new services can be developed on top of these data, like urban surveillance of Smart Cities. The spatio-temporal sensing coverage achievable with Vehicular Crowd-Sensing (VCS), however, is an open issue, since vehicles are not uniformly distributed over the road network, undermining the quality of potential services based on VCS data.In this paper, we present an evolution of the standard A ∗ routing algorithm, meant to increase VCS coverage by selecting a route in a random way among all those satisfying a parametric constraint on the total cost of the path. The proposed solution is based on an edge-computing paradigm, not requiring a central coordination but rather leveraging the computational resources available on-board, significantly reducing the back-end infrastructure costs. The proposed solution has been empirically evaluated on two public datasets of 450,000 real taxi trajectories from two cities, San Francisco and Porto, characterized by a very different road network topology. Results show sensible improvements in terms of achievable spatio-temporal sensing coverage of probe vehicles.