Test suites often need to adapt to the software that it is intended to test. The core software changes and grows, and as such, its test suite also needs to change and grow. However, the test suites can often grow so large as to be unmaintainable. We have developed techniques to assist in the maintenance of these test suites, specifically in allowing for test-suite reduction (while preserving coverage adequacy) and test-suite prioritization.
We developed techniques for clustering of failures. Failure-clustering techniques attempt to categorize failing test cases according to the bugs that caused them. Test cases are clustered by utilizing their execution profiles (gathered from instrumented versions of the code) as a means to encode the behavior of those executions. Such techniques can offer suggestions for duplicate submissions of bug reports.
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.
Research shows that sharing one’s location can help people stay connected, coordinate daily activities, and provide a sense of comfort and safety [1]. Recently, smartphones and location-based services (LBS) have become widely available in developed countries [7], but only a small percentage of smartphone users have ever tried sharing location with other people [8]. 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.
To enable much of our research to enable program understanding, software quality, and maintenance, we utilize and develop analyses of program code. These analyses model the flows of information through the logic of programs and systems. With these analysis models enable automated techniques to assist development and maintenance tasks.
Over the last several years we have been studying how digital media affects people’s lives. Rather than bring people into a laboratory, I view the real world as a living laboratory--I go where people live, study, and work, to study them as they go about their normal activities. Digital media use affects people’s mood, stress, and behavior quite significantly. In particular, people experience disruptions when working with digital media due to multi-tasking and interruptions.
Scientists are always working to determine which articles are interesting to them, timely, and relevant to their research. If working in an unfamiliar research area, searching for papers becomes even more difficult. By allowing users to vote on the prominence of links, social news sites like Slashdot, Digg, and reddit.com have addressed the issue of surfacing new and interesting content from across the internet. Moreover, they provide opportunities to provide context and comment on the content.
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.
ISR
