Abstract

Community Question and Answer (CQA) platforms are expected to provide relevant content that is not readily available through search engines. With an increase in the number of users and growth of internet, CQA platforms have transitioned from generic to domain specific systems. Expert rating, machine learning and statistical methods are being used for assessing the quality of answers. However, the research on importance of consistency as a quality parameter in the form of text cohesion in CQAs is limited. We extracted 109,113 CQAs from StackExchange related to Information Security of the last 8 years to evaluate text cohesion in answers. An empirical study conducted with 246 participants (Information Security Experts, Software Engineers and Computational Linguists) on the extracted answers stated that lack of text cohesion impacts the rating of answers in CQA. Software Engineers are seekers and viewers of answers, they responded to a survey that lack of text cohesion leads to difficulty in reading and remembering. Information Security Experts providing answers to CQA stated that they need text cohesion for understandability.

Abstract

Software Engineering Researchers in India from both academia and industry are widely contributing to various research problems. In this report, we briefly summarize the key insights from 1st Software Engineering Research in India Update Meeting (SERI 2019), which provides a platform for all the researchers to present and discuss their research work. The essence of this research meeting is to examine the current\ research areas within the software engineering research community and discover the scope of collaboration. Specifically, the first SERI update meeting had a series of invited research talks presented by researchers from academia and industry representing various software engineering labs across India. These talks unveiled notable views through presentations and group discussions, and cogitation of topics that can lead to fruitful collaborations between software engineering researchers and industry practitioners. The main objective of this update meeting is the exchange of research areas that promotes successful short-term and long-term collaborations.

Abstract

With more than a trillion web pages, there is a plethora of content available for consumption. Search Engine queries invariably lead to overwhelming information, parts of it relevant and some others irrelevant. Often the information provided can be conflicting, ambiguous, and inconsistent contributing to the loss of credibility of the content. In the past, researchers have proposed approaches for credibility assessment and enumerated factors influencing the credibility of web pages. In this work, we detailed a W EBCred framework for automated genre-aware credibility assessment of web pages. We developed a tool based on the proposed framework to extract web page features instances and identify genre a web page belongs to while assessing it’s Genre Credibility Score (GCS). We validated our approach on ‘Information Security’ dataset of 8,550 URLs with 171 features across 7 genres. The supervised learning algorithm, Gradient Boosted Decision Tree classified genres with 88.75% testing accuracy over 10 fold cross-validation, an improvement over the current benchmark. We also examined our approach on ‘Health’ domain web pages and had comparable results. The calculated GCS correlated 69% with crowdsourced Web Of Trust (W OT) score and 13% with algorithm based Alexa ranking across 5 Information security groups. This variance in correlation states that our GCS approach aligns with human way (W OT) as compared to algorithmic way (Alexa) of web assessment in both the experiments.

Abstract

Identifying dependency between various artifacts in a large scale software system is a non-trivial task. As the software evolves, multiple artifacts like files, docs, classes, database scripts, etc., are likely to undergo change concurrently. Such artifacts tend to have a dependency between them, otherwise referred to as logical coupling. Researchers have used Support and Confidence as an association rule based measurement to predict the levels of logical coupling among the software artifacts. However, employing a single change on a software artifact can span across various closely related changes when many code contributors are working on the same change. Thus it is important to preprocess and group these semantically related change-sets before identifying logical coupling. In this paper, we propose a method to identify logical coupling and group semantically related change sets. We evaluate our method on real-world git repositories and document our observations

Abstract

VR development practices have a diverse set of practices compared to traditional software development. Tasks like scene design, acoustic design, vergence manipulation, image depth, etc. are specific to VR apps and hence require evaluation processes that may be different from the traditional means. Usability Evaluation is one such process which is being executed in an unconventional way by Industrial Practitioners today. In this paper, the researchers detail a Systematic Literature Review of the Usability Evaluation Methods practised by Industrial researchers while building VR Products. The researchers found that VR Product teams follow unique methods to improve usability in their products. Further, the researchers consolidate these methods and provide insights into choosing the best to build a real-world VR Product based on the defined product constraints.

Abstract

With the rise of Virtual Reality (VR) footprint in many organizations, it was unclear if traditional software engineering practices are still exercised during VR product development. As part of our research, we conducted a year-long multi-level exploratory study to understand the various software development practices within VR product development teams. An empirical study on VR practitioners from 6 different countries was done to examine their development strategies, methods, and models adopted along with the various challenges faced during the course of VR product release. We found that VR practitioners adopted hybrid Software engineering approaches in VR product development. In this paper, we present our insights from the empirical study and stress on the need for a diverse software development model for VR products.

Abstract

Quality Models play a critical role in assuring quality and have evolved over 40+ years. They provide support for defining quality attributes, building and measuring the quality of the resulting product. Each quality model adopts a critical view on quality in terms of a set of model elements and relationships between them. This study aims to provide an overview of the state-of-the-art research on quality models with a focus on encompassing model elements and their support to architecting quality. The study was conducted using systematic mapping as the research methodology. A total of 238 primary papers were classified based on the type of research, standards usage, and publication trends. We identified that 17% (40) of papers belong to quality models. These 40 models were analyzed for the underlying meta-model elements and their support for a quality architecture using   rence architecture framework. The architecture phase mapping analysis shows that quality planning phase is 100% supported, quality assessment is 75% supported, quality documentation is included in 40% models and quality realization aspect is barely considered in 13% models. Quality realization happens through software processes and patterns, and it is necessary to evolve quality models and software process architectures that correlate quality definitions and quality realization mechanisms. Future research is expected in this direction

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

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

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.