Abstract

With improved digital literacy and cheaper data connections, crowdsourcing has become more prevalent. Crowdsourcing tasks such as image tagging, language translation, etc. have contributors with varied skill level. Question and answers, software development/testing and product design are more serious tasks and require expert contributions. With increasing security threats, crowdsourcing in 'security' domain is seen as an upcoming trend. In the survey, opportunities and challenges in crowdsourcing security are analyzed based on a survey conducted with 229 participants. The results provide insights on motivation, quality mechanisms and improvement areas for increased participation. 'Question and Answers' that includes information sharing and 'software development and testing' that includes Bug Bounty are identified as top crowdsourcing tasks by security specialists although they are concerned about security and privacy of information. Among quality attributes analyzed, contributor 'credibility' is identified as a key area of improvement for increased participation in crowdsourcing.

Abstract

Nations have set-up Critical Information Infrastruc-ture (CII) protection centers to provide safety of organizations which are deemed to be critical for the nation. In this context,this paper proposes a framework to identify Critical Information Infrastructures (CIIs). This framework has been adopted to identify the banks which are critical for Indian economy. Previously,Reserve Bank of India, the central bank of the country, has announced three banks as systemically important banks. The proposed approach, in this paper, considers a publicly available data set publishing the number (volume) and the amount (value)of financial transactions in the Indian domestic market involving all the banks. The proposed approach finds the individual ranks of each participating bank based on these two parameters.Finally, rank aggregation method combines these two rank lists(based on the value and the volume) to generate a final rank of the banks. Several rank aggregation methods have been compared in this context.

Abstract

There has been a significant rise in FinTech mobile app offerings in India for the past 3-4 years. These mobile apps have become disruptive means of managing daily financial transactions. Given the variety of offerings, the usability of these FinTech applications across different age groups is still unclear. In this paper, we explore the usability of FinTech mobile apps across different age groups through a quantitative survey and a more focused study on factors impacting the geriatric population on adopting FinTech mobile apps in India. In addition, we propose a framework called UMETRIX to evaluate the usability of mobile apps to determine the adoption rate among geriatric population.

Abstract

Domain specific search engines (DSSE) are gaining popularity because of better search relevance and domain specificity. The growth of IT and internet led to increase of cyber attacks, however, lack of DSSE for Security is making users refer multiple sites for security information. We demonstrate SIREN, a search engine for ’Information and Cyber Security’ with subdomain coverage, classification and site credibility for ranking search results. As part of our demonstration, we also automated identification of seed URLs (34,007) and related child URLs (400,726) of the security domain using Artificial Bee Colony algorithm. We also evaluated functional and non-functional parameters of available open source software stack that can be used for building other DSSEs.

Abstract

The increasing usage of smart-phones has resulted in mobile applications replacing or supplementing traditional web-based applications. Given the limitations of the form factor in smartphones, usability can be considered as one of the important attributes that determine the success of a mobile application. The measures available for assessing the usability of mobile applications tend to focus more on human aspects and less on the functional aspects of usability. As part of this paper, we propose a usability evaluation framework to identify functional usability issues specific to mobile applications. This framework use usability guidelines and code analysis to improve the usability of a mobile application. As a proof of concept, we have built an end-to-end system using the framework to validate and verify usability issues in Android mobile applications. We also generate code recommendations to implement failed usability guidelines.

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.