Examples of spatio-temporal data that can be represented as sets of points (called dot patterns) are pervasive in many applications, for example when tracking herds of migrating animals, ships in busy shipping channels and crowds of people in everyday life. The use of this type of data extends beyond the standard remit of Geographic Information Science (GISc), as classification and optimisation problems can often be visualised in the same manner. A common task within these fields is the assignment of a region (called a footprint) that is representative of the underlying pattern. The ways in which this footprint can be generated has been the subject of much research with many algorithms having been produced. Much of this research has focused on the dot patterns and footprints as static entities, however for many of the applications the data is prone to change. This thesis proposes that the footprint need not necessarily be updated each time the dot pattern changes; that the footprint can remain an appropriate representation of the pattern if the amount of change is slight. To ascertain the appropriate times at which to update the footprint, and when to leave it as it is, this thesis introduces the concept of change identifiers as simple measures of change between two dot patterns. Underlying the change identifiers is an in-depth examination of the data inherent in the dot pattern and the creation of descriptors that represent this data. The experimentation performed by this thesis shows that change identifiers are able to distinguish between different types of change across dot patterns from different sources. In doing so the change identifiers reduce the number of updates of the footprint while maintaining a measurably good representation of the dot pattern.

As the availability and popularity of the Internet continues to grow, the trend ofproviding global access to business resources and services online is an efficient andprofitable way for organizations to acquire a new share of the market. Due to the flexibilityand scalability of modern web technologies, web-based applications processand store personal or critical information in enormous amounts. Hence, the overallapplication’s functionality and secure data processing are the main key factors ofeach web application. For ensuring those key factors, the web page code must be regularlymonitored to retain the overall quality of the code. This project is devoted tochange identification and classification in modern web-based applications, based onthe comparison of two versions of web page code, acquired in different time periods.The foundation of the development is described as a detection algorithm in one of theacademic papers. The algorithm was supplemented by a more extensive classificationof changes that was originally proposed by the author. The result of the researchis a semi-automatic tool, developed in Python. The tool compares two versions ofthe web page code to find changes and classify those changes. The result of the tool’sexecution is a report file that contains statistics of the overall algorithm’s executionand type-clustered information about the detected changes between two versions ofthe web page code. The analysis of results showed that the implemented diff-toolprovides reliable results and allocates all types of possible changes in the web pagecodes, which are acknowledged by statistical analysis. The comparative analysis ofthe results of the developed diff-tool with the results of other similar technical solutionsrevealed serious shortcomings of other solutions, due to their data processingimplementation, classification of the changes and resulting report file.

Context: Today, most of the software products are built with COTS components. When a new version of these components is available, it is difficult to perform testing as the vendors of the component do not usually provide source code. Various regression testing techniques have been developed, but most of the techniques are based on the source code for change identification. So, the testers are facing different challenges in performing effective testing. Objectives: The goal of this research is to find out the different approaches that are used to identify changes in modified COTS component, analyze the main characteristics of those approaches and investigate how these characteristics can be used in selection and development of CBRT approach. Methods: To fulfill the aims of the research, we have conducted systematic literature review of different CBRT approaches from the year 1993-2010. From systematic literature we found out 32 papers relevant to our study. Data related to our research are extracted from those papers and conclusion is made. The relevant articles were searched in six scientific databases such as IEEE Explore, ACM Digital Library, SpringerLink, Science Direct, Scopus, and Engineering Village. Furthermore, online survey was conducted based on the characteristics of CBRT approaches. This survey was conducted to validate the SLR result. Results: From the systematic Literature Review we have found out 8 different characteristics of CBRT approaches such as applicability, automation, complexity, behavior model used, coverage criteria, strength and weakness, theory used and input. We observe that these are the most important characteristics in CBRT approaches and these approaches should be considered in selecting or developing new CBRT approach. The results from the survey also validate our findings. From survey some more factors were identified. Conclusion: The research develops the state-of-art of CBRT approaches towards future research. The result of this thesis will be helpful for the researchers as well as practitioners who are working on CBRT. The result of the thesis can be considered as a basis for further study. Based on the result of this thesis further study can be done on making a framework based on these characteristics and support component based regression testing. / Nabin Raj Dulal, 139, Jagriti Tole Marg, Balaju-16, Kathmandu , Nepal ph: +97714351087