Regression Testing and Test selection in Research

ABSTRACT Regression testing is a costly but crucial problem in software development. Both the research community and the industry have paid much attention to this problem. However, are the issues they concerned the same? The paper try to do the survey of current research on regression testing and current practice in industry and also try to find out whether there are gaps between them. The observations show that although some issues are concerned both by the research community and the industry gay, there do exist gaps. Keywords Regression Testing, Software Engineering, Software Maintenance The goal of this project is to address the gaps between current research and current application of regression in industry by studying the current research on regressing testing and the literatures on existing testing tools which are widely used in the industry. Once the gaps are addressed, we will be able to ask the question---why this happens and new research direction and new guideline for the practice will be proposed. The rest of the paper is organized as following: Section 2 is a briefly review of the current regression testing research literatures. In Section 3, the current popular commercial tools will first be introduced, and then some case studies are presented as they are some practices to apply the regression testing technology. Some interesting observations have been found according to Section 2 and Section 3, and they are presented in Section 4. Conclusions are given In Section 5

A supportive environment for the management of software testing

This dissertation describes research undertaken on the management of software testing. A support environment for the management of software testing, entitled SEMST, is presented. The research approach involves the investigation of software configuration management and its application to the testing process; the study of software testing techniques and methods; the exploration of the significance of software testing management; a survey of related work; the development and analysis of the requirements for SEMST; its implementation and an assessment. The current version of SEMST is a prototype built on the top of Unix and RCS on a Sun workstation. It is able to maintain all versions of specifications, test cases and programs, as well as to manage the relationships between these components

Formal functional testing of graphical user interfaces.

SIGLEAvailable from British Library Document Supply Centre- DSC:DX177960 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

A Bayesian Framework for Software Regression Testing

Software maintenance reportedly accounts for much of the total cost associated with developing software. These costs occur because modifying software is a highly error-prone task. Changing software to correct faults or add new functionality can cause existing functionality to regress, introducing new faults. To avoid such defects, one can re-test software after modifications, a task commonly known as regression testing. Regression testing typically involves the re-execution of test cases developed for previous versions. Re-running all existing test cases, however, is often costly and sometimes even infeasible due to time and resource constraints. Re-running test cases that do not exercise changed or change-impacted parts of the program carries extra cost and gives no benefit. The research community has thus sought ways to optimize regression testing by lowering the cost of test re-execution while preserving its effectiveness. To this end, researchers have proposed selecting a subset of test cases according to a variety of criteria (test case selection) and reordering test cases for execution to maximize a score function (test case prioritization). This dissertation presents a novel framework for optimizing regression testing activities, based on a probabilistic view of regression testing. The proposed framework is built around predicting the probability that each test case finds faults in the regression testing phase, and optimizing the test suites accordingly. To predict such probabilities, we model regression testing using a Bayesian Network (BN), a powerful probabilistic tool for modeling uncertainty in systems. We build this model using information measured directly from the software system. Our proposed framework builds upon the existing research in this area in many ways. First, our framework incorporates different information extracted from software into one model, which helps reduce uncertainty by using more of the available information, and enables better modeling of the system. Moreover, our framework provides flexibility by enabling a choice of which sources of information to use. Research in software measurement has proven that dealing with different systems requires different techniques and hence requires such flexibility. Using the proposed framework, engineers can customize their regression testing techniques to fit the characteristics of their systems using measurements most appropriate to their environment. We evaluate the performance of our proposed BN-based framework empirically. Although the framework can help both test case selection and prioritization, we propose using it primarily as a prioritization technique. We therefore compare our technique against other prioritization techniques from the literature. Our empirical evaluation examines a variety of objects and fault types. The results show that the proposed framework can outperform other techniques on some cases and performs comparably on the others. In sum, this thesis introduces a novel Bayesian framework for optimizing regression testing and shows that the proposed framework can help testers improve the cost effectiveness of their regression testing tasks