Given the availability of large-scale source-code repositories, there have been a large number of applications for clone detection. Unfortunately, despite a decade of active research, there is a marked lack in clone detectors that scale to large software repositories. In particular for detecting near-miss clones where significant editing activities may take place in the cloned code.
In order to produce effective fault-localization, debugging, failure-clustering, and test-suite maintenance techniques, researchers would benefit from a deeper understanding of how faults (i.e., bugs) behave and interact with each other. Some faults, even if executed, may or may not propagate to the output, and even still may or may not influence the output in a way to cause failure. Furthermore, in the presence of multiple faults, faults may interact in a way to obscure each other or in a way to produce behavior not seen in their isolation.
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.
One method of facilitating developers to understand the complex inner nature of software that we have employed is the use of information visualization. Software is often so complex that even the developers who initially created it cannot understand all of the possible runtime behaviors that it can exhibit --- specifically, all of the bugs that it may contain. In order to present large code bases with innumerable characteristics and relationships of its components (e.g., instructions, variables, values, and timings) we have developed a number of novel visualizations of software.
Research shows that sharing one’s location can help people stay connected, coordinate daily activities, and provide a sense of comfort and safety . Recently, smartphones and location-based services (LBS) have become widely available in developed countries , but only a small percentage of smartphone users have ever tried sharing location with other people . Our work aims to understand real-world factors shaping behaviors and attitudes towards social location-sharing, especially in regards to why people avoid or abandon the technology, or limit their usage.
The dynamic nature of markets wherein business relationships are established and dissolved continuously demands systems that can cope with constant change, and do so with security paramount. These relationships are reified as services that are offered by organizations and used within a spectrum of domains and use contexts. Current service technologies fail to meet the requirements, however; interfaces are rigid, non-secure, and “one-size-fits-all solutions” which hardly meet the demands of any of its users.
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?
The development of a software system is now ever more frequently a part of a larger development effort, including multiple software systems that co-exist in the same environment: a software ecosystem. Though most studies of the evolution of software have focused on a single software system, there is much that we can learn from the analysis of a set of interrelated systems. Topic modeling techniques show promise for mining the data stored in software repositories to understand the evolution of a system.