Abstract

With more than a billion web sites, volume and variety of content available for consumption is huge. However, credibility, an important quality characteristic of web pages is questionable in many cases and tends to be non-uniform. Credibility can increase or reduce the importance of web page leading to potential gain or loss of user base. Credibility without factoring genre of content (for example, Help, Article, Discussion, etc.) can lead to incorrect assessment. Depending on the genre, the importance of features such as web page date time modified, grammar, image to text ratio, in and out links, and other web page features differ. We propose a genre credibility assessment based on web page surface features and their importance in a genre. Further, we built a W EBCred framework to assess GCS (Genre based Credibility Score) with flexibility to add/modify genres, its features and their importance. We validated our approach on 10,429 ’Information Security’ related web pages; the assessed score correlated 35% with crowdsourced Web Of Trust (WOT) score and 39% with Alexa ranking.

Abstract

Increased internet bandwidth at low cost is leading to the creation of large volumes of unstructured data. This data explosion opens up opportunities for the creation of a variety of data-driven intelligent systems, such as the Semantic Web. Ontologies form one of the most crucial layers of semantic web, and the extraction and enrichment of ontologies given this data explosion becomes an inevitable research problem. In this paper, we survey the literature on semi-automatic and automatic ontology extraction and enrichment and classify them into four broad categories based on the approach. Then, we proceed to narrow down four algorithms from each of these categories, implement and analytically compare them based on parameters like context relevance, efficiency and precision. Lastly, we propose a Long Short Term Memory Networks (LSTM) based deep learning approach to try and overcome the gaps identified in these approaches.

Abstract

Bug report filing is a major part of software maintenance. Due to extensive number of bugs filed everyday in large software projects and the asynchronous nature of bug report filing ecosystem, duplicate bug reports are filed. Capturing and tagging duplicate bug reports is crucial in order to avoid assignment of the same bug to different developers. Efforts have been made in the past to detect duplicate bug reports by using topic modelling [2], discriminative methods [5], meta-attributes [6], etc. Recently, Yang et al.[8] proposed an approach to combine word embeddings, TF-IDF and meta-attributes to compute bug similarity between two bug reports.

Abstract

Bug reporting is a major part of software maintenance and due to its inherently asynchronous nature, duplicate bug reporting has become fairly common. Detecting duplicate bug reports is an important task in order to avoid the assignment of a same bug to different developers. Earlier approaches have improved duplicate bug report detection by using the notions of word embeddings, topic models and other machine learning approaches. In this poster, we attempt to combine Latent Dirichlet Allocation (LDA) and word embeddings to leverage the strengths of both approaches for this task. As a first step towards this idea, we present initial analysis and an approach which is able to outperform both word embeddings and LDA for this task. We validate our hypothesis on a real world dataset of Firefox project and show that there is potential in combining both LDA and word embeddings for duplicate bug report detection.

Abstract

A key part of software maintenance is bug reporting and rectifica-tion. Bug reporting is a major issue and due to its asynchronous nature, duplicate bug reporting is common. Detecting duplicate bug reports is an important task in software maintenance in order to avoid the assignment of the same bug to different developers.In this paper, we explore the notion of using word embeddings for retrieving duplicate bug report in large software repositories. We discuss an approach to model each bug report as a dense vector and retrieve its top-k most similar reports for duplicate bug report de-tection. Through experiments on two real world datasets, we show that word embeddings perform better than baselines and related approaches and have the potential to improve duplicate bug report retrieval.

Abstract

Domain Specific Search Engines are expected to provide relevant search results. Availability of enormous number of URLs across subdomains improves relevance of domain specific search engines. The current methods for seed URLs can be systematic ensuring representation of subdomains. We propose a fine grained approach for automatic extraction of seed URLs at subdomain level using Wikipedia and Twitter as repositories. A SeedRel metric and a Diversity Index for seed URL relevance are proposed to measure subdomain coverage. We implemented our approach for 'Security - Information and Cyber' domain and identified 34,007 Seed URLs and 400,726 URLs across subdomains. The measured Diversity index value of 2.10 conforms that all subdomains are represented, hence, a relevant 'Security Search Engine' can be built. Our approach also extracted more URLs (seed and child) as compared to existing approaches for URL extraction.

Abstract

Data structures is an important area of knowledge in computer science that is often difficult to understand for high school students. There has been a lot of research in computer science education around the subject of programming. Hence, it becomes imperative to explore the domain of data structures as well. With this work, we have looked into an active pedagogical approach for facilitating learning of data structures in school. This approach has been implemented by means of an interactive game. The game aims to teach arrays and linked lists through activities based on the principle of compare and contrast. The innovation in the game is the use of musical notes to teach operations in arrays and linked lists. The game was evaluated with 27 students across 8th, 9th and 10th grades using a questionnaire. The results of the evaluation showed that the group of students who learnt about arrays and linked lists through the game scored an average of 81.5% of the total marks while the other group scored an average of 72% of the total marks.

Abstract

Over the past few years, there is a significant shift from web to mobile devices. In addition, the success of HTML5 has negatively impacted the usage and support for Adobe Flash. This has resulted in unmaintained Flash assets and code bases. In this paper, we discuss the feasibility of a semi-automated technique to transform Flash based animations to HTML5 and JavaScript against re-writing the same animations from scratch. Writing animations from scratch in JavaScript is a time taking effort due to adherence to aesthetic details of the animation, domain knowledge and enumeration of all states. Our approach addresses these challenges by providing techniques to transform the Small Web Format (SWF) files to JavaScript. We validate our approach by conducting an experimental study to measure the efforts for a set of animations with respect to transformation from scratch, and transformation using our proposed

Abstract

The ability to modify the existing published web pages is what we are calling Renarration of the web. Such a mechanism is useful for improving accessibility and personalization of the content currently on the web. There are many techniques in place for enabling both Web Accessibility and Web Personalization. In this paper we propose a novel approach: an Aspects inspired design of renarration. Aspects have traditionally been applied to programming. Here we reinterpret concepts like Join Points, Point-cuts and Advices and apply them to web documents. To validate our approach, we designed a framework called Rennaration Studio that is built using microservices based architecture pattern and implemented using Python's flask platform. We demonstrate the feasibility of our proposal by renarrating different Aspects (text, language, phonetics) of two specific web documents.

Abstract

Researchers have supported the idea of gamification to enhance students' interest in activities like code reviews, change management, knowledge management, issue tracking, etc. which might otherwise be repetitive and monotonous. We performed an experimental study consisting of nearly 180+ participants to measure the impact of gamification on code review process using 5 different code review tools, including one gamified code review instance from our extensible architectural framework. We assess the impact of gamification based on the code smells and bugs identified in a gamified and non-gamified environment as per code inspection report. Further, measurement and comparison of the quantity and usefulness of code review comments was done using machine learning techniques.