In this paper, we propose methods for testing the usability of graphical user interface (GUI) applications based on log files of user interactions. Log analysis by existing methods is not efficient because evaluators analyze a single log file or log files of the same user and then manually compare results. The methods proposed here solve this problem; the methods enable evaluators to analyze the log files of multiple users together by detecting interaction patterns that commonly appear in the log files. To achieve the methods, we first clarify usability attributes that can be evaluated by a log-based usability testing method and user interaction patterns that have to be detected for the evaluation. Based on an investigation on the information that can be obtained from the log files, we extract the attributes of clarity, safety, simplicity, and continuity. For the evaluations of clarity and safety, the interaction patterns that have to be detected include those from user errors. We then propose our methods for detecting interaction patterns from the log files of multiple users. Patterns that commonly appear in the log files are detected by utilizing a repeating pattern detection algorithm. By regarding an operation sequence recorded in a log file as a string and concatenating strings, common patterns are able to be detected as repeating patterns in the concatenated string. We next describe the implementation of the methods in a computer tool for log-based usability testing. The tool, GUITESTER, records user-application interactions into log files, generates usability analysis data from the log files by applying the proposed methods, and visualizes the generated usability analysis data. To show the effectiveness of GUITESTER in finding usability problems, we report an example of a usability test. In this test, evaluators could find 14 problems in a tested GUI application. We finally discuss the ability of the proposed methods in terms of its log analysis efficiency, by comparing the analysis/sequence time (AT/ST) ratio of GUITESTER with those of other methods and tools. The ratio of GUITESTER is found to be smaller. This indicates the methods make log analysis more efficient.

Our aim is to develop an intuitive sound designing interface for non-expert users. We propose editing sound by sound, which means using vocal mimicking as a "master" to transform the pitch and amplitude envelope. Our technique allows any user to easily and intuitively design sound because it requires no knowledge of acoustic features.

Our aim is to develop an intuitive and effective sound retrieval method for non-expert users. Such a retrieval method should be developed to accommodate a human's perceptual features. We therefore first conducted an experiment to clarify how people represent sound. A participant listens to one sound stimulus and then conveys the sound to a partner. The results indicated that people used mostly verbal description categorized in three groups: the sound itself, the sound's situation, and the sound's impression. Based on these results, we propose three types of keywords: onomatopoeia, sound source, and adjective, which are typical keywords of the above three groups of sound description, for sound retrieval. This retrieval method was implemented for a sound database. Our method can increase the varieties of sounds able to be retrieved and allow users to intuitively search sounds because users can retrieve sounds by using keywords that are most natural to them.