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Random testing and partition testing are two major families of software testing techniques. They have been compared both theoretically and empirically in numerous studies for decades, and it has been widely acknowledged that they have their own advantages and disadvantages and that their innate characteristics are fairly complementary to each other. Some work has been conducted to develop advanced testing techniques through the integration of random testing and partition testing, attempting to preserve the advantages of both while minimizing their disadvantages. In this paper, we propose a new testing approach, adaptive partition testing, where test cases are randomly selected from some partition whose probability of being selected is adaptively adjusted along the testing process. We particularly develop two algorithms, Markov-chain based adaptive partition testing and reward-punishment based adaptive partition testing, to implement the proposed approach. The former algorithm makes use of Markov matrix to dynamically adjust the probability of a partition to be selected for conducting tests; while the latter is based on a reward and punishment mechanism. We conduct empirical studies to evaluate the performance of the proposed algorithms using ten faulty versions of three large-scale open source programs. Our experimental results show that, compared with two baseline techniques, namely random partition testing (RPT) and dynamic random testing (DRT), our algorithms deliver higher fault-detection effectiveness with lower test case selection overhead. It is demonstrated that the proposed adaptive partition testing is an effective testing approach, taking advantages of both random testing and partition testing. [ABSTRACT FROM AUTHOR]

Test case prioritization is the process of ordering the execution of test cases to achieve a certain goal, such as increasing the rate of fault detection. Increasing the rate of fault detection can provide earlier feedback to system developers, improving fault fixing activity and, ultimately, software delivery. Many existing test case prioritization techniques consider that tests can be run in any order. However, due to functional dependencies that may exist between some test cases—that is, one test case must be executed before another—this is often not the case. In this paper, we present a family of test case prioritization techniques that use the dependency information from a test suite to prioritize that test suite. The nature of the techniques preserves the dependencies in the test ordering. The hypothesis of this work is that dependencies between tests are representative of interactions in the system under test, and executing complex interactions earlier is likely to increase the fault detection rate, compared to arbitrary test orderings. Empirical evaluations on six systems built toward industry use demonstrate that these techniques increase the rate of fault detection compared to the rates achieved by the untreated order, random orders, and test suites ordered using existing "coarse-grained” techniques based on function coverage. [ABSTRACT FROM AUTHOR]

In this paper, we propose two general frameworks for deriving several software reliability growth models based on a non-homogeneous Poisson process (NHPP) in the presence of imperfect debugging and error generation. The proposed models are initially formulated for the case when there is no differentiation between failure observation and fault removal testing processes, and then extended for the case when there is a clear differentiation between failure observation and fault removal testing processes. During the last three decades, many software reliability growth models (SRGM) have been developed to describe software failures as a random process, and can be used to evaluate development status during testing. With SRGM, software engineers can easily measure (or forecast) the software reliability (or quality), and plot software reliability growth charts. It is not easy to select the best model from a plethora of models available. There are few SRGM in the literature of software engineering that differentiates between failure observation and fault removal processes. In real software development environments, the number of failures observed need not be the same as the number of faults removed. Due to the complexity of software systems, and an incomplete understanding of software, the testing team may not be able to remove the fault perfectly on observation of a failure, and the original fault may remain, resulting in a phenomenon known as imperfect debugging, or get replaced by another fault causing error generation. In the case of imperfect debugging, the fault content of the software remains the same; while in the case of error generation, the fault content increases as the testing progresses. Removal of observed faults may result in the introduction of new faults. [ABSTRACT FROM AUTHOR]

Event-Driven Software (EDS) can change state based on incoming events; common examples are GUI and Web applications. These EDSs pose a challenge to testing because there are a large number of possible event sequences that users can invoke through a user interface. While valuable contributions have been made for testing these two subclasses of EDS, such efforts have been disjoint. This work provides the first single model that is generic enough to study GUI and Web applications together. In this paper, we use the model to define generic prioritization criteria that are applicable to both GUI and Web applications. Our ultimate goal is to evolve the model and use it to develop a unified theory of how all EDS should be tested. An empirical study reveals that the GUI and Web-based applications, when recast using the new model, show similar behavior. For example, a criterion that gives priority to all pairs of event interactions did well for GUI and Web applications; another criterion that gives priority to the smallest number of parameter value settings did poorly for both. These results reinforce our belief that these two subclasses of applications should be modeled and studied together. [ABSTRACT FROM AUTHOR]