The Internet of Things (IoT) is an emerging technology powered by smart devices that increasingly pervades our environments and practices. The process of incorporating technology into one's practices involves use and adaptation of the technology and is usually referred to as appropriation. In research about IoT, appropriation of IoT technology is a rather new topic. It is necessary to understand how appropriation takes place in order to be able to provide strong support for it in the design of technology.
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.
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.
Multi-criteria test-suite minimization aims to remove redundant test cases from a test suite based on some criteria such as code coverage, while trying to optimally maintain the capability of the reduced suite based on other criteria such as fault-detection effectiveness. Existing techniques addressing this problem with integer linear programming claim to produce optimal solutions. However, the multi-criteria test-suite minimization problem is inherently nonlinear, due to the fact that test cases are often dependent on each other in terms of test-case criteria.
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?
I am aiding core developers of the Linux kernel to use mutation analysis to improve kernel systems testing methods, and to verify critical algorithms. I am also investigating the use of bounded model checking (CBMC) on Linux kernel. As an outcome of this project so far we have identified 3 bugs in the Linux kernel. I am also applying mutation analysis on sqlite3 to improve its testing.
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.
Computer games may well be the quintessential domain for software engineering R&D. Why? Modern multi-player online games (MMOG) must address core issues in just about every major area of Computer Science and SE research and education.
ISR
