Regression testing is essential to ensure software quality. Regression Test-case selection is another process wherein, the testers would like to ensure that test-cases which are obsolete due to the changes in the system should not be considered for further testing. This is the Regression Test-case Selection problem. Although existing research has addressed many related problems, most of the existing regression test-case selection techniques cater to procedural systems. Being academic, they lack the scalability and detail to cater to multi-tier applications. Such techniques can be employed for procedural systems, usually mathematical applications. Enterprise applications have become complex and distributed leading to component-based architectures. Thus, inter-process communication has become a very important activity of any such system. Messaging is the most widely employed intermodule interaction mechanism. Today's systems, being heavily internet dependent, are Web-Services based which utilize XML for messaging. We propose an RTS technique which is specifically targeted at enterprise applications.

Since the cost of software testing has continuously accounted for large proportion of the software development total cost, automatic test data generation becomes a hot topic in recent software testing research. These researches attempt to reduce the cost of software testing by generating test data automatically, but they are discussed only for the single version programs not for the programs which are needed re-testing after changing. On the other hand, the regression testing researches discuss about how to re-test programs after changing, but they don¡¦t talk about how to generate test data automatically. Therefore, we propose an automatic test data evolution strategy in this paper. We use the method of regression testing to find out the part of programs which need re-testing, then automatic evolutes the test data by hybrid genetic algorithm. According to the experiment result, our strategy has the same or better testing ability but needs less cost than the other strategies.

Software testing is the most commonly used methodology for validating quality of software systems. Conceptually, testing is simple, but in practice, given the huge (practically infinite) space of inputs to test against, it requires solving a number of challenging problems, including evaluating and reusing tests efficiently and effectively as software evolves. While software testing research has seen much progress in recent years, many crucial bugs still evade state-of-the-art approaches and cause significant monetary losses and sometimes are responsible for loss of life. My thesis is that a unified, bi-dimensional, change-driven methodology can form the basis of novel techniques and tools that can make testing significantly more effective and efficient, and allow us to find more bugs at a reduced cost. We propose a novel unification of the following two dimensions of change: (1) real manual changes made by programmers, e.g., as commonly used to support more effective and efficient regression testing techniques; and (2) mechanically introduced changes to code or specifications, e.g., as originally conceived in mutation testing for evaluating quality of test suites. We believe such unification can lay the foundation of a scalable and highly effective methodology for testing and maintaining real software systems. The primary contribution of my thesis is two-fold. One, it introduces new techniques to address central problems in both regression testing (e.g., test prioritization) and mutation testing (e.g., selective mutation testing). Two, it introduces a new methodology that uses the foundations of regression testing to speed up mutation testing, and also uses the foundations of mutation testing to help with the fault localization problem raised in regression testing. The central ideas are embodied in a suite of prototype tools. Rigorous experimental evaluation is used to validate the efficacy of the proposed techniques using a variety of real-world Java programs. / text

Application Engineering is a field where software organizations develop software products from a predefined Software Product Line. The time and cost allotted to come up with a new product variant is limited. Lack of systematic support in testing leads to redundancy. Redundancy in this context can be found in test-cases that do not contribute towards fault-detection and testing leads to an increased testing effort. This thesis work proposes a framework to reduce the testing effort, aimed at avoiding testing redundancy. Feature Model diagrams have been constructed from the assumed specification requirements. These Feature Model diagrams have been used to derive test models such as Object Model diagram and State Chart diagram. Unit testing and System testing have been performed on test models to obtain test cases that have been stored in the repository. Regression testing has been applied to these test cases to classify them into Reusable, Re-testable and Obsolete.

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.

It is very important that the software being delivered to the user is reliable and fault free. This makes software testing one of the most important phases in the software development life cycle. The problem being faced by everyone is the time it takes to test the software, which is normally huge. An important part of the software testing process is running and evaluating test scenarios. The objective of this part is to evaluate how well the application under test conforms to its specifications. One of the ways to achieve this is to generate the test cases and make use of the test oracle (a human expert) to determine whether a given test case exposes a fault. This procedure consumes a lot of time. Using an automated oracle can contribute towards the reduction in software testing time which helps in the reduction of the cost of the testing process. The use of Artificial Neural Networks (ANN) and Info-Fuzzy Networks (IFN) for test case selection and evaluation has already been explored. In this thesis these two approaches are compared on their use as an automated oracle. An ROC Analysis is done to compare the two approaches. The execution times of both the approaches are also compared. For comparison, three applications have been used. The basic methodology behind the use of IFN or ANN is to train the network on randomly generated test cases executed with a stable version of the software. This trained network is then used as an oracle for evaluating the correctness of the output produced by new and possibly faulty versions of the stable software. The outputs from the oracle i.e. IFN or ANN and faulty versions of the software system are compared with that of the original version to evaluate the outputs generated by new version of the software.

<p>Regression testing plays an important role during the software development life-cycle,especially during maintenance, it provides confidence that the modified parts of softwarebehave as intended and the unchanged parts have no affect by the modification. Regressiontest selection is used to select test cases from the test suites which have been used to test theprevious version of the software. In this thesis, we extend the traditional definition of a testcase with a log file, containing information of which events that occurred when the test casewas last executed. Based on the contents of this log file, we propose a method of regressiontest selection for multi-tasking real-time systems, able to determine which parts of softwarethat have not been affected by the modification. Therefore, the test cases designed for theunchanged parts do not need to be re-tested.</p>

Context: When a software is modified, regression testing is performed to ensure the behaviour of software is not affected because of those modifications. Due to frequent modifications, the regression testing became challenging. Although there are many regression testing techniques are developed in the research, they are not incorporating in the industry. This is because of the differences in regression testing goals and measures in research and industry. The current context of this study is to identify the regression testing goals and measures in the research and industry perspectives and to find the differences and similarities in both perspectives. Objectives: The primary objective of this study is to identify the similarities and differences in regression testing goals and measure from research and industry perspectives. Additionally, in this study, a general adapted goals list is presented. Methods: A mixed method approach is used for this study. A literature review has been used to identify the regression testing goals and measures in research. A survey is used to identify the regression testing goals and measures in the industry. Semi-structured interviews and online questionnaire are used as data collection methods in the survey. Thematic analysis and descriptive statistics are used as data analysis methods for the qualitative and quantitative data. Results: A literature review is conducted using 33 research articles. In the survey, the data is collected from 11 semi-structured interviews which are validated with 45 responses from an online questionnaire. A total of 6 regression testing goals are identified from the literature review and 8 goals are identified in the survey respectively. The measures used to evaluate these goals are identified and tabulated. Conclusions: From the results, we observed the similarities and differences in the regression testing goals and measures in industry and research. There are few similarities in goals but the major difference is the priority order of these goals. There are various measures used in research but very fewer measures are incorporating in the industry. The respondents from the survey implied that there is a need for generic adaptive goals. Further, a general list of goals is presented. Keywords: Regression, Regression testing, Goals, Objectives, Measures, Metrics.

Motivation: Test case prioritization can prioritize test cases, optimize the test execution, save time and cost. There are many different methods for test case prioritization, test case prioritization method based on test case execution history is one kind of them. Based on the test case execution history, it’s easier to increase the rate of fault detection, hence we want to do a study about test case prioritization methods based on the test case execution history. Meanwhile, executing the feasible methods to compare the effectiveness of them. For the motivation of the thesis may be regarded as an example for experiencing approach for comparing test case prioritizations based on test case execution history, or as a study case for identifying the suitable methods to use and help improve the effectiveness of the testing process. Objectives: The aim of this thesis is to look for a suitable test case prioritization method that can support risk based testing, in which test case execution history is employed as the key criterion of evaluation. For this research, there are three main objectives. First, explore and summarize methods of test case prioritization based on test case history. Next, identify what are differences among the test case prioritization methods. Finally, execute the methods which we selected, and compare the effectiveness of methods. Methods: To achieve the first and the second study objectives, a systematic literature review has been conducted using Kitchenham guidelines. To achieve the third study objective, an experiment was conducted following Wohlin guidelines. Results: In our thesis: 1) We conducted a systematic literature review and selected 15 relevant literatures. We extracted data of the literatures and then we synthesized the data. We found that the methods have different kinds of inputs, test levels, maturity levels, validation and "automated testing or manual testing". 2) We selected two feasible methods from those 15 literatures, Method 1 is Adaptive test-case prioritization and Method 2 is Similarity-based test quality metric. We executed the methods within 17 test suites. Comparing the result of two methods and non-prioritization, the mean Average Percentage of Defects Found (APFD) of Adaptive test-case prioritization execution result (86.9%) is significantly higher than non-prioritization (51.5%) and Similarity-based test quality metric (47.5%), it means that the Adaptive test-case prioritization has higher effectiveness. Conclusion: In our thesis, existing test case prioritization methods based on test case execution history are extracted and listed out through systematic literature review. The summary of them and the description of differences can be available in the thesis. The 15 relevant literatures and the synthesized data may be as a guideline for relevant software researchers or testers. We did the statistical test for the experimental result, we can see two different test case prioritization methods have different effectiveness.

As Web services grow in maturity and use, so do the methods which are being used to test and maintain them. Regression Testing is a major component of most major testing systems but has only begun to be applied to Web services. The majority of the tools and techniques applying regression test to Web services are focused on test-case generation, thus ignoring the potential savings of regression test selection. Regression test selection optimizes the regression testing process by selecting a subset of all tests, while still maintaining some level of confidence about the system performing no worse than the unmodified system. A safe regression test selection technique implies that after selection, the level of confidence is as high as it would be if no tests were removed. Since safe regression test selection techniques generally involve code-based (white-box) testing, they cannot be directly applied to Web services due to their loosely-coupled, standards-based, and distributed nature. A framework which automates both the regression test selection and regression testing processes for Web services in a decentralized, end-to-end manner is proposed. As part of this approach, special consideration is given to the concurrency issues which may occur in an autonomous and decentralized system. The resulting synchronization method will be presented along with a set of algorithms which manage the regression testing and regression test selection processes throughout the system. A set of empirical results demonstrate the feasibility and benefit of the approach.