Microtask crowdsourcing systems such as FoldIt and ESP partition work into short, self-contained microtasks, reducing barriers to contribute, increasing parallelism, and reducing the time to complete work. Could this model be applied to software development? To explore this question, we are designing a development process and cloud-based IDE for crowd development.
This research focuses on techniques for identifying and reducing the costs, streamlining the process, and improving the readiness of future workforce for the acquisition of complex software systems. Emphasis is directed at identifying, tracking, and analyzing software component costs and cost reduction opportunities within acquisition life cycle of open architecture (OA) systems, where such systems combine best-of-breed software components and software products lines (SPLs) that are subject to different intellectual property (IP) license requirements.
We developed a token-based approach for large scale code clone detection which is based on a filtering heuristic that reduces the number of token comparisons when the two code blocks are compared. We also developed a MapReduce based parallel algorithm that uses the filtering heuristic and scales to thousands of projects. The filtering heuristic is generic and can also be used in conjunction with other token-based approaches. In that context, we demonstrated how it can increase the retrieval speed and decrease the memory usage of the index-based approaches.
When a live site is down or time is of the essence, software teams mobilize to fix bugs as fast as possible. How might such important bugs be fixed more quickly? One answer is through crowdsourcing, where ad-hoc participants are each given small, self-contained microtasks that are then aggregated into an overall solution. To explore this idea, we are currently designing new techniques and tools for crowd debugging.
This project describes and documents observational results that arise from the playtesting-based evaluation of twenty-six computer games focused on science learning or scientific research. We refer to this little studied genre of computer games as science learning games (SLGs). Our goal was to begin to identify a new set of criteria, play mechanics, and play experiences that give rise to play-based learning experiences in the realm of different scientific topics.
Bitcoin is a digital currency and payment platform that has been the source of much media attention. The currency is not backed by a government like most conventional currencies but is part of a democratic and dencentralized movement. Bitcoin transactions are pseudo-anonymous in a similar way to cash money. Why do people use this currency? How do their political values align with their usage of bitcoin? Furthermore, how does the community regulate itself in the absence of a formal hierarchical structure? Lastly, how do anonymous users form communities?
In the era of big data and personalization, websites and (mobile) applications collect an increasingly large amount of personal information about their users. The large majority of users decide to disclose some but not all information that is requested from them. They trade off the anticipated benefits with the privacy risks of disclosure, a decision process that has been dubbed privacy calculus. Such decisions are inherently difficult though, because they may have uncertain repercussions later on that are difficult to weigh against the (possibly immediate) gratification of disclosure. How can we help users to balance the benefits and risks of information disclosure in a user-friendly manner, so that they can make good privacy decisions?
ISR has long been an internationally recognized leader in research into all aspects of open source software development. In this role, researchers at ISR along with colleagues throughout the U.S. helped to develop a new agenda that can help guide future research into open source software development.