Kazuyuki Shima

Detecting low usability web pages using quantitative data of users' behavior

The purpose of this research is to detect low usability web pages from the behavior of users, such as browsing time, mouse movement and eye movement. We experimented to investigate the relation between the quantitative data viewing behavior of users and web usability evaluation by subjects. We analyzed the data to detect low usability web pages using discriminant analysis. Low usability web pages, 94.4% (17pages / 18pages = detectable pages / low usability pages) were detectable from the moving speed of gazing points and the amount of wheel rolling of a mouse. Moreover, this detection reduced the number of web pages which should be evaluated by half (46% = 89 pages / 192 pages = detected pages / all pages).

WebTracer: A new web usability evaluation environment using gazing point information

WebTracer is a new usability evaluation environment that supports recording, replaying, and analysis of a gazing point and operation while a user is browsing a website. WebTracer can record a users gazing point and operation compactly. Results of an experimental evaluation showed that the size of the operation history recorded by WebTracer was from 1/10 to 1/20 of the size of data recorded by an MPEG-2 or MPEG-4 format. With its compact form, the results of usability testing with the gazing point can be efficiently shared. It is expected to easily share empirical data between researchers. Also, evaluators can easily send the testing results as a feedback to the developers over the Internet. Moreover, the results show a possibility that gazing points related to usability. For example, when the menu of a Web page is divided into two panes, gazing points move more quickly. It seems that WebTracer efficiently improves usability evaluation, with gazing point data helping to identify problems on Web pages.

A study on the failure intensity of different software faults

We describe an experiment investigating the distribution of failure intensity in software reliability growth models. We found that the assumption of conventional models that the failure intensity follows a gamma distribution is not always true. Our new software reliability model does not make this assumption; rather, the failure intensity is calculated from failure data. We show that our new model predicts more accurately the number of detected faults for our study project than the conventional models.

A new method for increasing the reliability of multiversion software systems using software breeding

The paper proposes a new method for increasing the reliability of multiversion software systems. The software using software breeding is more reliable than one using N version programming. But software breeding is not suitable for real time application because program versions are executed several times for detecting faulty modules. In the proposed method, the detection of faulty modules is performed in the background when program versions fail and the software continues the execution in the foreground. When the detection of faulty modules is finished, the combination of module versions in program versions are changed. Ten simulations, each of which executed program versions 10/sup 6/ times, were performed to analyse the effectiveness of the new method. This resulted in the reduction of the number of failures to range from 33% to 76% with an average of 56%.

Drone were me: Flying drone as avatar by aligned stereo view and head pose

In this paper, we present a natural interface between a drone, i.e., a miniature aerial vehicle (MAV), and a human, in which a stereo-vision-MAV is flied via a HMD (Head Mounted Display) with inertial sensors. A stereo camera is mounted on a MAV and the stereo images are transmitted to a remote PC. Then, a user controls the motion of the MAV by his/her head pose, which is measured from the inertial sensor of the HMD, while looking at the stereo images via the HMD as if the MAV is an avatar of the user. Thanks to a stereo-vision camera, it makes it possible that not only a user can sense the distance information between the MAV and obstacles from the HMD, but also a MAV can avoid collisions using the measured 3D information even if human error occurs during the operation.