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

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

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

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

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

Virtual Labs allow performance of experiments without real world instrumentation needs. Most of these virtual experiments are simulation based and developed using proprietary or Open Source simulation, Adobe Flash, and other client side software. The procedural steps involved in conducting these experiments are hardwired and require software modification for enhancements or alignment to the course structure. We propose a model based approach that eases development of virtual experiments without dependency on software programmers for any changes. We demonstrate our model driven based approach on Chemical Sciences labs of Virtual Labs, a Government of India initiative. With our model driven based approach, the effort for new experiment development or FOSS conversion of existing experiments that were using outdated technologies is less than a person day as compared to more than a person …

Abstract

Most financial institutions, NGOs, and self help groups (SHG) are now using web technology to create outreach. However, for non-mainstream users, like those in rural India, the web application’s non vernacular language, unfamiliar terminology, complex financial models, and user’s own poor digital exposure, together creates a mistrust in the web applications, which tends to result in poor adoption. This inhibits financial literacy initiatives and slows down Financial Inclusion, which is a Millennium Goal. We treat this as a web accessibility problem, and look for prior work on transforming published web content for making it suitable for easy consumption by non-mainstream users. To this end we present a systematic literature review, which forages through 1068 records related to page transformation, to RQ1) uncover existing motivators for transforming an existing web page, RQ2) surface the approaches taken, and …

Abstract

Student internships are necessary for every student to learn and apply the learning to deliver tangible and relevant outcomes. In-person internship opportunities are less in number and have major challenges in scaling such as lesser number of available projects in relevant technologies, inadequate mentorship during the internship, varying college calendars and others. This paper presents SRIP (Student Remote Internship Program) approach which focuses on the domain of programming in open source technologies and projects for the internships that are relevant to 2nd and 3rd year engineering college students. It aims to overcome the stated challenges and simultaneously make a contribution to the open source community. Virtual Labs is the open source repository that we leveraged to implement SRIP pilot study for interns to contribute and develop programming skills.

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 are

Abstract

Bug report filing is a major part of software maintenance. Due to the asynchronous nature of the bug filing process, duplicate bug reports are filed. Detecting duplicate bug reports is an important aspect of software maintenance since the same bug should not be assigned to different developers. In this poster, we present Deep Word Embedding Network for computing similarity between two bug reports for the task of duplicate bug report detection. We propose to learn a two step model to calculate similarity between two bug reports by means of word embeddings and a deep neural network. We run experiments on two large datasets of Mozilla Project and Open Office Project and compare the proposed approach with baselines and related approaches. Through this initial work, we show that a combination of word embeddings and deep neural networks can be used to improve duplicate bug report detection.