Luay Tahat

Model based regression test reduction using dependence analysis

Model based testing is a system testing technique used to test software systems modeled by formal description languages, e.g., an extended finite state machine (EFSM). System models are frequently changed because of specification changes. Selective test generation techniques are used to test the modified parts of the model. However, the size of regression test suites still may be very large. In this paper, we present a model-based regression testing approach that uses EFSM model dependence analysis to reduce regression test suites. The approach automatically identifies the difference between the original model and the modified model as a set of elementary model modifications. For each elementary modification, regression test reduction strategies are used to reduce the regression test suite based on EFSM dependence analysis. Our initial experience shows that the approach may significantly reduce the size of regression test suites.

Slicing of state-based models

System modeling is a widely used technique to model state-based systems. Several state-based languages are used to model such systems, e.g., EFSM (extended finite state machine), SDL (specification description language) and state charts. Although state-based modeling is very useful, system models are frequently large and complex and are hard to understand and modify. Slicing is a well-known reduction technique. Most of the research on slicing is code-based. There has been limited research on specification-based slicing and model-based slicing. In this paper, we present an approach to slicing state-based models, in particular EFSM models. Our approach automatically identifies the parts of the model that affect an element of interest using EFSM dependence analysis. Slice reduction techniques are then used to reduce the size of the EFSM slice. Our experience with the presented slicing approach showed that significant reduction of state-based models could be achieved.

Requirement-based automated black-box test generation

Testing large software systems is very laborious and expensive. Model-based test generation techniques are used to automatically generate tests for large software systems. However, these techniques require manually created system models that are used for test generation. In addition, generated test cases are not associated with individual requirements. In this paper, we present a novel approach of requirement-based test generation. The approach accepts a software specification as a set of individual requirements expressed in textual and SDL formats (a common practice in the industry). From these requirements, system model is automatically created with requirement information mapped to the model. The system model is used to automatically generate test cases related to individual requirements. Several test generation strategies are presented. The approach is extended to requirement-based regression test generation related to changes on the requirement level. Our initial experience shows that this approach may provide significant benefits in terms of reduction in number of test cases and increase in quality of a test suite.

Test prioritization using system models

During regression testing, a modified system is retested using the existing test suite. Because the size of the test suite may be very large, testers are interested in detecting faults in the system as early as possible during the retesting process. Test prioritization tries to order test cases for execution so the chances of early detection of faults during retesting are increased. The existing prioritization methods are based on the code of the system. System modeling is a widely used technique to model state-based systems. In this paper, we present methods of test prioritization based on state-based models after changes to the model and the system. The model is executed for the test suite and information about model execution is used to prioritize tests. Execution of the model is inexpensive as compared to execution of the system; therefore the overhead associated with test prioritization is relatively small. In addition, we present an analytical framework for evaluation of test prioritization methods. This framework may reduce the cost of evaluation as compared to the existing evaluation framework that is based on experimentation (observation). We have performed an experimental study in which we compared different test prioritization methods. The results of the experimental study suggest that system models may improve the effectiveness of test prioritization with respect to early fault detection.

Model-based test prioritization heuristic methods and their evaluation

During regression testing, a modified system needs to be retested using the existing test suite. Since test suites may be very large, developers are interested in detecting faults in the system as early as possible. Test prioritization orders test cases for execution to increase potentially the chances of early fault detection during retesting. Most of the existing test prioritization methods are based on the code of the system, but model-based test prioritization has been recently proposed. System modeling is a widely used technique to model state-based systems. System models may not only be used to generate test cases but also to prioritize tests. In model-based prioritization, information collected during execution of a model is used to prioritize tests for execution. In this paper we present several model-based test prioritization heuristics. The major motivation to develop these heuristics was simplicity and effectiveness in early fault detection. We have conducted a small experimental study in which we experimentally compared model-based test prioritization heuristics. The results have shown that some simple heuristic methods can be as effective in early fault detection as more complex ones.

Dependence analysis in reduction of requirement based test suites

Requirement-based automated test case generation is a model-based technique for generating test suites related to individual requirements. The technique supports test generation from EFSM (Extended Finite State Machine) system models. Several requirement-based selective test generation techniques were proposed. These techniques may significantly reduce a number of test cases with respect to a requirement under test as opposed to a complete system testing. However, the number of test cases may still be very large especially for large systems. In this paper, we present an approach of reduction of requirement based test suites using EFSM dependence analysis. Different types of dependencies are identified between elements of the EFSM system model. These dependencies capture potential interactions between elements of the model and are used to determine parts of the model that affect a requirement under test. This information is used to reduce the test suite by identifying repetitive tests, i.e., tests that exhibit the same pattern of interactions with respect to the requirement under test. Our initial experience shows that this approach may significantly reduce the size of selective test suites.

Regression test suite prioritization using system models

During regression testing, a modified system is often retested using an existing test suite. Since the size of the test suite may be very large, testers are interested in detecting faults in the modified system as early as possible during this retesting process. Test prioritization attempts to order tests for execution so that the chances of early detection of faults during retesting are increased. The existing prioritization methods are based on the source code of the system under test. In this paper, we present and evaluate two model‐based selective methods and a dependence‐based method of test prioritization utilizing the state‐based model of the system under test. These methods assume that the modifications are made both on the system under test and its model. The existing test suite is executed on the system model and information about this execution is used to prioritize tests. Execution of the model is inexpensive as compared with execution of the system under test; therefore, the overhead associated with test prioritization is relatively small. In addition, we present an analytical framework for evaluation of test prioritization methods. This framework may reduce the cost of evaluation as compared with the framework that is based on observation. We have performed an empirical study in which we compared different test prioritization methods. The results of the empirical study suggest that system models may improve the effectiveness of test prioritization with respect to early fault detection. Copyright

Understanding modifications in state-based models

System modeling is a widely used technique to model state-based systems. System models are frequently large and complex and are hard to understand. In addition, they are frequently modified because of specification changes. Understanding the effect of these changes on the model and the system may be very difficult for large models. In this paper, we present an approach that may support understanding the effect of model modifications. The goal is to identify these parts of the model that may exhibit different behavior because of the modification. In this approach, the difference between the original model and the modified model is identified and then affected parts of the model are computed based on model dependence analysis. Our initial experience shows that the approach may be helpful in understanding the effect of modifications on the system.

State-based models in regression test suite prioritization

Testing software products is very expensive and time consuming, especially for large software systems with extensive regression testing. During regression testing, a modified system is often re-tested using an existing test suite. Since test suites can be very large, testers are interested in detecting faults in the modified system as early as possible. Test prioritization tries to order test cases for execution in a way that increases the chances of the early detection of faults. Most of the existing test prioritization methods are based on the code of the system under test, but model-based test prioritization has been lately proposed. Most of the existing model-based test prioritization methods can be used only when models are modified during system maintenance. In this paper, we present model-based prioritization for a class of modifications for which models are not modified (only the source code is modified). After identifying the elements of the model related to the modified source code, information collected during the execution of the model is used to prioritize tests for execution. Here, we present and compare existing and new model-based test prioritization methods focused on this class of modifications. The major motivation for presenting these methods is to provide system developers with simple and yet effective test prioritization techniques for early fault detection. Statistical analysis of the empirical study, which compares the effectiveness of the presented methods in terms of early fault detection, show that compared to random ordering of test cases, model-based test prioritization significantly improve the effectiveness of test prioritization with respect to early fault detection.

The ethical attitudes of information technology professionals: a comparative study between the USA and the Middle East

This paper aims at investigating comparatively the ethical orientation of information technology (IT) professionals in the Middle East and the United States. It tests for attitudes toward and awareness of ethically-related issues, namely intellectual property, privacy and other general ethical IT aspects. In addition, through a comparison between the two regions, this paper intends to examine whether differences in IT professional demographics and characteristics, including gender and academic level, have any impact on attitudes to business ethics. A ttest is used to establish significant differences between the targeted samples, while an ANOVA F-test is conducted to determine significant differences among the sample countries on a group basis. The results show a general awareness of ethical issues concerning information technology, and no significant differences are found between the two samples. However, different ethical attitudes are reported among respondents in terms of their reactions to the targeted IT ethical aspects. On an individual sample basis, the results about gender support the claim that male and female respondents are different, while mixed results are revealed for the influence of academic level on attitudes towards IT ethics. For intellectual property, the results are significant regarding ethical attitude differences between Middle-Eastern professionals and their counterparts in the US, while no significance differences are reported in terms of privacy.