Reverse Engineering and Testing of Rich Internet Applications

The World Wide Web experiences a continuous and constant evolution, where new initiatives, standards, approaches and technologies are continuously proposed for developing more effective and higher quality Web applications. To satisfy the growing request of the market for Web applications, new technologies, frameworks, tools and environments that allow to develop Web and mobile applications with the least effort and in very short time have been introduced in the last years. These new technologies have made possible the dawn of a new generation of Web applications, named Rich Internet Applications (RIAs), that offer greater usability and interactivity than traditional ones. This evolution has been accompanied by some drawbacks that are mostly due to the lack of applying well-known software engineering practices and approaches. As a consequence, new research questions and challenges have emerged in the field of web and mobile applications maintenance and testing. The research activity described in this thesis has addressed some of these topics with the specific aim of proposing new and effective solutions to the problems of modelling, reverse engineering, comprehending, re-documenting and testing existing RIAs. Due to the growing relevance of mobile applications in the renewed Web scenarios, the problem of testing mobile applications developed for the Android operating system has been addressed too, in an attempt of exploring and proposing new techniques of testing automation for these type of applications

Validation of Adult OMNI Perceived Exertion Scales for Elliptical Ergometry

PURPOSE: The purpose of this project was to examine concurrent and construct validity of two newly developed Adult OMNI Elliptical Ergometry ratings of perceived exertion (RPE) Scales. METHODS: Fifty-nine sedentary to recreationally active, college-aged volunteers (males, n = 30; age = 21.3 + 3.3 yrs and females, n = 29; 22.3 + 3.5 yrs) participated in this study. A single observation, cross-sectional perceptual estimation trial was employed with subjects exercising to volitional fatigue on an elliptical ergometer. Oxygen consumption (VO2), heart rate (HR) and RPE-Overall Body (O), Legs (L) and Chest/Breathing (C) were recorded each stage from the Borg 15 Category Scale and two different OMNI RPE scale formats. One scale maintained the original format of the OMNI Picture System of Perceived Exertion. The second scale modified verbal, numerical and pictorial descriptors at the low end of the response range. Concurrent validity was established by correlating RPE-O, L and C from each scale with VO2 and HR obtained from each test stage during the estimation trial. Construct validity was established by correlating RPE-O, L and C from the Adult OMNI Elliptical Ergometry Scales with RPE-O, L and C from the Borg Scale. RESULTS: Correlation analyses indicated the relation between RPE-O, L and C from each OMNI RPE Scale distributed as a positive linear function of both VO2 (males, r = .941 - .951 and females, r = .930 - .946) and HR (males, r = .950 - .960 and females, r = .963 - .966). A strong, positive relation was also exhibited between differentiated and undifferentiated RPE from the Adult OMNI Elliptical Ergometry Scales and the Borg 15 Category Scale (males, r = .961 - .972 and females, r = .973 - .977). CONCLUSION: Concurrent and construct validity were established for both formats of the Adult OMNI Elliptical Ergometry Scale during partial weight bearing exercise. Either scale can be used to estimate RPE during elliptical ergometer exercise in health-fitness settings. However, because of the potential use of RPE in caloric expenditure indices and prediction models, the modified scale depicting the "rest" pictorial may be more practical

Mixed k-means clustering in computer adaptive learning

The ASSISTments project from Worcester Polytechnic Institute provides a free web-based intelligent tutoring system including two levels of differentiation, that are manually programmed by teachers and researchers. Problems assigned through ASSISTments can be programmed in trees, where the sequence of problems adapts to the student\u27s performance on each question. Within each problem, if a student enters an incorrect response the ASSISTments system provides scaffolded feedback to target the student\u27s misconception. This thesis begins to develop an educational data mining algorithm to automate this differentiation. First, an adaption of Alsahaf\u27s mixed k-means clustering algorithm is proposed to handle a mix of categorical and numeric data. Second, the algorithm is implemented in MATLAB and its performance is compared to Alsahaf\u27s results on benchmark data sets. Finally, the MATLAB implementation is applied to ASSISTments data sets from 2009 and 2012 to develop a predictive model