Lean implementations of software testing tools using XML representations of source codes

By utilizing XML representations of source programs under test, a new approach is proposed to concisely implement some prototypes for TACCLE, a software testing methodology. The conversions between a source program and its XML representation can be easily realized using existing conversion tools. In this way, the conversion tools can automatically analyze and parse the source program, so that testing tool developers only need to concentrate on the manipulation of the XML document. If appropriate XML DOM APIs are chosen, the implementations of such testing tools will be pretty lean. A detailed case study for GMPS tool, a prototype for the TACCLE methodology, is presented to illustrate the new approach. © 2008 IEEE.published_or_final_versionThis research is supported by the Jinan University Youth Foundation under Grant #51208035, Union Grant of Guangdong Province and National Natural Science Foundation of China under Grant #U0775001, and the Guangdong Province Science Foundation under Grant #7010116

Une seule manière d'utiliser les exceptions ? Une étude empirique de 21 applications Java

International audienceLe langage Java offre un puissant système de gestion des exceptions et de traitement des erreurs. L'étude de différentes applications Java peut permettre de distinguer différentes utilisations de ce système. Ce papier étudie les utilisations des exceptions pour constater les différences ou similitudes entre 21 applications. Les résultats permettent de constater de fortes différences entre les applications et dans l'ensemble une gestion très variée des exceptions

Une seule manière d'utiliser les exceptions ? Une étude empirique de 21 applications Java

International audienceLe langage Java offre un puissant système de gestion des exceptions et de traitement des erreurs. L'étude de différentes applications Java peut permettre de distinguer différentes utilisations de ce système. Ce papier étudie les utilisations des exceptions pour constater les différences ou similitudes entre 21 applications. Les résultats permettent de constater de fortes différences entre les applications et dans l'ensemble une gestion très variée des exceptions

Developing & Marketing a JavaScript Support Extension for the srcML Infrastructure

This work describes the development of a JavaScript grammar file for the srcML infrastructure\u27s future parser generator, the many files used to test it, and the marketing plan used to market the JavaScript Support extension to industry software developers and potential collaborators

Development of a National-Scale Big Data Analytics Pipeline to Study the Potential Impacts of Flooding on Critical Infrastructures and Communities

With the rapid development of the Internet of Things (IoT) and Big data infrastructure, crowdsourcing techniques have emerged to facilitate data processing and problem solving particularly for flood emergences purposes. A Flood Analytics Information System (FAIS) has been developed as a Python Web application to gather Big data from multiple servers and analyze flooding impacts during historical and real-time events. The application is smartly designed to integrate crowd intelligence, machine learning (ML), and natural language processing of tweets to provide flood warning with the aim to improve situational awareness for flood risk management and decision making. FAIS allows the user to submit search request from the United States Geological Survey (USGS) as well as Twitter through a series of queries, which is used to modify request URL sent to data sources. This national scale prototype combines flood peak rates and river level information with geotagged tweets to identify a dynamic set of at-risk locations to flooding. The list of prioritized areas can be updated every 15 minutes as the crowdsourced data and environmental information and condition change. In addition, FAIS uses Google Vision API (application programming interface) and image processing algorithms to detect objects (flood, road, vehicle, river, etc.) in time-lapse digital images and build valuable metadata into image catalog. The application performs Flood Frequency Analysis (FFA) and computes design flow values corresponding to specific return periods that can help engineers in designing safe structures and in protection against economic losses due to maintenance of civil infrastructure. FAIS is successfully tested in real-time during Hurricane Dorian flooding event across the Carolinas where the storm made extensive damage and disruption to critical infrastructure and the environment. The prototype is also verified during historical events such as Hurricanes Matthew and Florence flooding for the Lower PeeDee Basin in the Carolinas