Improving Usability of Software Refactoring Tools

Post-deployment maintenance and evolution can account for up to 75% of the cost of developing a software system. Software refactoring can reduce the costs associated with evolution by improving system quality. Although refactoring can yield benefits, the process includes potentially complex, error-prone, tedious and time-consuming tasks. It is these tasks that automated refactoring tools seek to address. However, although the refactoring process is well-defined, current refactoring tools do not support the full process. To develop better automated refactoring support, we have completed a usability study of software refactoring tools. In the study, we analysed the task of software refactoring using the ISO 9241-11 usability standard and Fitts' List of task allocation. Expanding on this analysis, we reviewed 11 collections of usability guidelines and combined these into a single list of 38 guidelines. From this list, we developed 81 usability requirements for refactoring tools. Using these requirements, the software refactoring tools Eclipse 3.2, Condenser 1.05, RefactorIT 2.5.1, and Eclipse 3.2 with the Simian UI 2.2.12 plugin were studied. Based on the analysis, we have selected a subset of the requirements that can be incorporated into a prototype refactoring tool intended to address the full refactoring process.

Improved Subject Identification in Surveillance Video using Super Resolution

The time consuming and labour intensive task of identifying individuals in surveillance video is often challenged by poor resolution and the sheer volume of stored video. Faces or identifying marks such as tattoos are often too coarse for direct matching by machine or human vision. Object tracking and super-resolution can then be combined to facilitate the automated detection and enhancement of areas of interest. The object tracking process enables the automatic detection of people of interest, greatly reducing the amount of data for super-resolution. Smaller regions such as faces can also be tracked. A number of instances of such regions can then be utilized to obtain a super-resolved version for matching. Performance improvement from super-resolution is demonstrated using a face verification task. It is shown that there is a consistent improvement of approximately 7% in verification accuracy, using both Eigenface and Elastic Bunch Graph Matching approaches for automatic face verification, starting from faces with an eye to eye distance of 14 pixels. Visual improvement in image fidelity from super-resolved images over low-resolution and interpolated images is demonstrated on a small database. Current research and future directions in this area are also summarized.