1,720,974 research outputs found
Migration of Legacy Data Intensive Applications to the Web: The Sinfor Project
No full textMontréal, Canad
A Two-Phase Bug Localization Approach Based on Multi-layer Perceptrons and Distributional Features
No full textBug localization is a challenging and time-consuming task of the process of bug fixing and, more in general, of software maintenance. Several approaches have been proposed in the literature which support developers in this task by identifying source code files in which the bug is likely to be located. However, the research on this topic never stopped, looking for new methods providing better accuracy and/or better efficiency. In this paper, we propose a two-phase bug localization approach which leverages multi-layer neural networks and distributional features. First phase locations are obtained thanks to a neural network trained on word embeddings representations of fixed bug reports. The second phase refines bug locations taking into account the number of times source code files co-occur in fixed bug locations. To evaluate the approach, we conducted a large-scale experiment on five open source projects, namely Mozilla, Eclipse, Dolphin, httpd, and gcc. Results show that, thanks to pre-trained word embeddings, we were able to implement a scalable approach with a training running time of few hours on large datasets. Performances are comparable to other existing deep learning approaches
An Approach and an Eclipse Based Environment to Enhance the Navigation Structure of Web Sites
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Migrating to the Web legacy application: The Sinfor project
No full textVarious approaches can be used to migrate legacy applications to the Web. In particular, migrating data-intensive legacy applications (e.g. traditional application for business management) needs methodological approach to face the challenges implied by the process. The Ubiquitous Web Applications (UWA) framework is one of the most innovative and complete frameworks for conceptual user centered modelling of a Web application. In this paper we describe the application of UWA to a real experience of reengineering a real legacy application for customer's order management
DomainSenticNet: An Ontology and a Methodology Enabling Domain-Aware Sentic Computing
Full text linkIn recent years, SenticNet and OntoSenticNet have represented important developments in the novel interdisciplinary field of research known as sentic computing, enabling the development of a variety of Sentic applications. In this paper, we propose an extension of the OntoSenticNet ontology, named DomainSenticNet, and contribute an unsupervised methodology to support the development of domain-aware Sentic applications. We developed an unsupervised methodology that, for each concept in OntoSenticNet, mines semantically related concepts from WordNet and Probase knowledge bases and computes domain distributional information from the entire collection of Kickstarter domain-specific crowdfunding campaigns. Subsequently, we applied DomainSenticNet to a prototype tool for Kickstarter campaign authoring and success prediction, demonstrating an improvement in the interpretability of sentiment intensities. DomainSenticNet is an extension of the OntoSenticNet ontology that integrates each of the 100,000 concepts included in OntoSenticNet with a set of semantically related concepts and domain distributional information. The defined unsupervised methodology is highly replicable and can be easily adapted to build similar domain-aware resources from different domain corpora and external knowledge bases. Used in combination with OntoSenticNet, DomainSenticNet may favor the development of novel hybrid aspect-based sentiment analysis systems and support further research on sentic computing in domain-aware applications
Enhancing online discussion forums with topic-driven content search and assisted posting
No full textEmotional Intensity-based Success Prediction Model for Crowdfunded Campaigns
No full textWe present a novel framework to predict the success of Kickstarter campaigns based on the emotional intensity induced by domain specific aspects. The framework enables to automatically mine (from campaign descriptions and product reviews) clusters of aspects characterizing a domain of interest. A Need Index-based model is built in order to predict whether a campaign will result in success (i.e., reach its funding goal). The easy to interpret Need Index representation enables to understand and monitor the most relevant domain aspects and their related emotional intensities. We tested our framework on Kickstarter campaigns in the dominant domain of mobile games with a prediction accuracy of 94.4%. The methodology opens new ground for further interdisciplinary research on causal inference to support predictions related to customer needs, particularly in the areas of behavioural economics, marketing, brand management and market research
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