Swapneel Sheth

HALO (highly addictive, socially optimized) software engineering

In recent years, computer games have become increasingly social and collaborative in nature. Massively multiplayer online games, in which a large number of players collaborate with each other to achieve common goals in the game, have become extremely pervasive. By working together towards a common goal, players become more engrossed in the game. In everyday work environments, this sort of engagement would be beneficial, and is often sought out. We propose an approach to software engineering called HALO that builds upon the properties found in popular games, by turning work into a game environment. Our proposed approach can be viewed as a model for a family of prospective games that would support the software development process. Utilizing operant conditioning and flow theory, we create an immersive software development environment conducive to increased productivity. We describe the mechanics of HALO and how it could fit into typical software engineering processes.

Secret ninja testing with HALO software engineering

Software testing traditionally receives little attention in early computer science courses. However, we believe that if exposed to testing early, students will develop positive habits for future work. As we have found that students typically are not keen on testing, we propose an engaging and socially-oriented approach to teaching software testing in introductory and intermediate computer science courses. Our proposal leverages the power of gaming utilizing our previously described system HALO. Unlike many previous approaches, we aim to present software testing in disguise - so that students do not recognize (at first) that they are being exposed to software testing. We describe how HALO could be integrated into course assignments as well as the benefits that HALO creates

genSpace: Exploring social networking metaphors for knowledge sharing and scientific collaborative work

Many collaborative applications, especially in scientific research, focus only on the sharing of tools or the sharing of data. We seek to introduce an approach to scientific collaboration that is based on the sharing of knowledge. We do this by automatically building organizational memory and enabling knowledge sharing by observing what users do with a particular tool or set of tools in the domain, through the addition of activity and usage monitoring facilities to standalone applications. Once this knowledge has been gathered, we apply social networking models to provide collaborative features to users, such as suggestions on tools to use, and automatically-generated sequences of actions based on past usage amongst the members of a social network or the entire community. In this work, we investigate social networking models as an approach to scientific knowledge sharing, and present an implementation called genSpace, which is built as an extension to the geWorkbench platform for computational biologists. Last, we discuss the approach from the viewpoint of social software engineering.

Us and them: a study of privacy requirements across north america, asia, and europe

Data privacy when using online systems like Facebook and Amazon has become an increasingly popular topic in the last few years. However, only a little is known about how users and developers perceive privacy and which concrete measures would mitigate their privacy concerns. To investigate privacy requirements, we conducted an online survey with closed and open questions and collected 408 valid responses. Our results show that users often reduce privacy to security, with data sharing and data breaches being their biggest concerns. Users are more concerned about the content of their documents and their personal data such as location than about their interaction data. Unlike users, developers clearly prefer technical measures like data anonymization and think that privacy laws and policies are less effective. We also observed interesting differences between people from different geographies. For example, people from Europe are more concerned about data breaches than people from North America. People from Asia/Pacific and Europe believe that content and metadata are more critical for privacy than people from North America. Our results contribute to developing a user-driven privacy framework that is based on empirical evidence in addition to the legal, technical, and commercial perspectives.

A competitive-collaborative approach for introducing software engineering in a CS2 class

Introductory Computer Science (CS) classes are typically competitive in nature. The cutthroat nature of these classes comes from students attempting to get as high a grade as possible, which may or may not correlate with actual learning. Further, there is very little collaboration allowed in most introductory CS classes. Most assignments are completed individually since many educators feel that students learn the most, especially in introductory classes, by working alone. In addition to completing “normal” individual assignments, which have many benefits, we wanted to expose students to collaboration early (via, for example, team projects). In this paper, we describe how we leveraged competition and collaboration in a CS2 class to help students learn aspects of computer science better - in this case, good software design and software testing - and summarize student feedback.

weHelp: A Reference Architecture for Social Recommender Systems

Recommender systems have become increasingly popular. Most of the research on recommender systems has focused on recommendation algorithms. There has been relatively little research, however, in the area of generalized system architectures for recommendation systems. In this paper, we introduce weHelp: a reference architecture for social recommender systems - systems where recommendations are derived automatically from the aggregate of logged activities conducted by the systems users. Our architecture is designed to be application and domain agnostic. We feel that a good reference architecture will make designing a recommendation system easier; in particular, weHelp aims to provide a practical design template to help developers design their own well-modularized systems.

Towards Diversity in Recommendations Using Social Networks

While there has been a lot of research towards improving the accuracy of recommender systems, the resulting systems have tended to become increasingly narrow in suggestion variety. An emerging trend in recommendation systems is to actively seek out diversity in recommendations, where the aim is to provide unexpected, varied, and serendipitous recommendations to the user. Our main contribution in this paper is a new approach to diversity in recommendations called“Social Diversity,”a technique that uses social network information to diversify recommendation results. Social Diversity utilizes social networks in recommender systems to leverage the diverse underlying preferences of different user communities to introduce diversity into recommendations. This form of diversification ensures that users in different social networks (who may not collaborate in real life, since they are in a different network) share information, helping to prevent siloization of knowledge and recommendations. We describe our approach and show its feasibility in providing diverse recommendations for the MovieLens dataset.

A large-scale, longitudinal study of user profiles in world of warcraft

We present a survey of usage of the popular Massively Multiplayer Online Role Playing Game, World of Warcraft. Players within this game often self-organize into communities with similar interests and/or styles of play. By mining publicly available data, we collected a dataset consisting of the complete player history for approximately six million characters, with partial data for another six million characters. The paper provides a thorough description of the distributed approach used to collect this massive community data set, and then focuses on an analysis of player achievement data in particular, exposing trends in play from this highly successful game. From this data, we present several findings regarding player profiles. We correlate achievements with motivations based upon a previously-defined motivation model, and then classify players based on the categories of achievements that they pursued. Experiments show players who fall within each of these buckets can play differently, and that as players progress through game content, their play style evolves as well.

Towards Using Cached Data Mining for Large Scale Recommender Systems

Recommender systems are becoming increasingly popular. As these systems become commonplace and the number of users increases, it will become important for these systems to be able to cope with a large and diverse set of users whose recommendation needs may be very different from each other. In particular, large scale recommender systems will need to ensure that users’ requests for recommendations can be answered with low response times and high throughput. In this paper, we explore how to use caches and cached data mining to improve the performance of recommender systems by improving throughput and reducing response time for providing recommendations. We describe the structure of our cache, which can be viewed as a prefetch cache that prefetches all types of supported recommendations, and how it is used in our recommender system.We also describe the results of our empirical study to measure the efficacy of our cache.

Money for Nothing and Privacy for Free

Privacy in the context of ubiquitous social computing systems has become a major concern for the society at large. As the number of online social computing systems that collect user data grows, this privacy threat is further exacerbated. There has been some work (both, recent and older) on addressing these privacy concerns. These approaches typically require extra computational resources, which might be beneficial where privacy is concerned, but when dealing with Green Computing and sustainability, this is not a great option. Spending more computation time results in spending more energy and more resources that make the software system less sustainable. Ideally, what we would like are techniques for designing software systems that address these privacy concerns but which are also sustainable systems where privacy could be achieved “for free,” i.e., without having to spend extra computational effort. In this paper, we describe how privacy can be achieved for free an accidental and beneficial side effect of doing some existing computation and what types of privacy threats it can mitigate. More precisely, we describe a “Privacy for Free” design pattern and show its feasibility, sustainability, and utility in building complex social computing systems. Keywords-Design Pattern; Correlation Privacy; Web 2.0; Concept Drift; Differential Privacy;