Regression Test Selection by Exclusion

This thesis addresses the research in the area of regression testing. Software systems change and evolve over time. Each time a system is changed regression tests have to be run to validate these changes. An important issue in regression testing is how to minimise reuse the existing test cases of original program for modied program. One of the techniques to tackle this issue is called regression test selection technique. The aim of this research is to signicantly reduce the number of test cases that need to be run after changes have been made. Specically, this thesis focuses on developing a model for regression test selection using the decomposition slicing technique. Decomposition slicing provides a technique that is capable of identifying the unchanged parts of the system. The model of regression test selection based on decomposition slicing and exclusion of test cases was developed in this thesis. The model is called Regression Test Selection by Exclusion (ReTSE) and has four main phases. They are Program Analysis, Comparison, Exclusion and Optimisation phases. The validity of the ReTSE model is explored through the application of a number of case studies. The case studies tackle all types of modication such as change, delete and add statements. The case studies have covered a single and combination types of modication at a time. The application of the proposed model has shown that signicant reductions in the number of test cases can be achieved. The evaluation of the model based on an existing framework and comparison with another model also has shown promising results. The case studies have limited themselves to relatively small programs and the next step is to apply the model to larger systems with more complex changes to ascertain if it scales up. While some parts of the model have been automated tools will be required for the rest when carrying out the larger case studies

An Adoption Model of Mobile Knowledge Sharing Based on the Theory of Planned Behavior

Resting on the use of mobile device which is increasingly popular worldwide, mobile learning and sharing knowledge between among students and academicians in fact extends the reach of education and sharing knowledge to all social-economic levels independent of location and time, indicating a new opportunity for education industry development and sharing knowledge. Nonetheless, there is still a lack of a comprehensive understanding regarding the factors affecting the adoption of mobile phone technology for learning and sharing knowledge. In this light, an adoption model of mobile phone technology knowledge sharing was built in this paper based on the Theory of Planned Behavior, in which perceived enjoyment, facilitating conditions, interpersonal influences, perceived usefulness, external influence, mobility, self- efficacy, and perceived ease of use of mobile sharing knowledge are integrated in order to increase the predictive capability of model. This model hopefully provides a framework for future research, and will serve as a basis for our future survey and analysis of data

A proposed java forward slicing approach

Many organization, programmers, and researchers need to debug, test and make maintenance for a segment of their source code to improve their system. Program slicing is one of the best techniques to do so. There are many slicing techniques available to solve such problems such as static slicing, dynamic slicing, and amorphous slicing. In our paper, we decided to develop a tool that supports many slicing techniques. Our proposed tool provides new flexible ways to process simple segments of Java code, and it generates needed slicing according to the user needs, our tool will provide the user with direct and indirect dependencies for each variable in the code segments. This tool can work under various operating systems and does not need particular environments. Thus, our tool is helpful in many aspects such as debugging, testing, education, and many other elements

Tool for collaborative temporal-based software version management

Software version management is the processes of identifying and keeping track of different versions of a software.Complexity level of this process would become complicated should software was distributed in many places.This paper present a new dimension in software version management which based on temporal elements.Temporal elements such as valid time and transaction time are the main attributes considered, to be inserted into the software version management database.By having these two attributes, it would help the people involved in software process to organize data and perform activity monitoring with more efficient. For a practical application of the model, therefore an automate tool has been developed that could be applied under collaborative software process called TEMVer

Regression test selection model: a comparison between ReTSE and pythia

As software systems change and evolve over time regression tests have to be run to validate these changes. Regression testing is an expensive but essential activity in software maintenance. The purpose of this paper is to compare a new regression test selection model called ReTSE with Pythia. The ReTSE model uses decomposition slicing in order to identify the relevant regression tests. Decomposition slicing provides a technique that is capable of identifying the unchanged parts of a system. Pythia is a regression test selection technique based on textual differencing. Both techniques are compare using a Power program taken from Vokolos and Frankl’s paper. The analysis of this comparison has shown promising results in reducing the number of tests to be run after changes are introduced

Regression test selection model: a comparison between ReTSE and pythia

As software systems change and evolve over time regression tests have to be run to validate these changes. Regression testing is an expensive but essential activity in software maintenance. The purpose of this paper is to compare a new regression test selection model called ReTSE with Pythia. The ReTSE model uses decomposition slicing in order to identify the relevant regression tests. Decomposition slicing provides a technique that is capable of identifying the unchanged parts of a system. Pythia is a regression test selection technique based on textual differencing. Both techniques are compare using a Power program taken from Vokolos and Frankl’s paper. The analysis of this comparison has shown promising results in reducing the number of tests to be run after changes are introduced