Where the creation, understanding, and assessment of software testing and regression testing techniques are concerned, controlled experimentation is an indispensable research methodology. Obtaining the infrastructure necessary to support such experimentation, however, is difficult and expensive. As a result, progress in experimentation with testing techniques has been slow, and empirical data on the costs and effectiveness of techniques remains relatively scarce. To help address this problem, we have been designing and constructing infrastructure to support controlled experimentation with testing and regression testing techniques. This paper reports on the challenges faced by researchers experimenting with testing techniques, including those that inform the design of our infrastructure. The paper then describes the infrastructure that we are creating in response to these challenges, and that we are now making available to other researchers, and discusses the impact that this infrastructure has and can be expected to have.

The importance of using requirements information in the testing phase has been well recognized by the requirements engineering community, but to date, a vast majority of regression testing techniques have primarily relied on software code information. Incorporating requirements information into the current testing practice could help software engineers identify the source of defects more easily, validate the product against requirements, and maintain software products in a holistic way. In this paper, we investigate whether the requirements-based clustering approach that incorporates traditional code analysis information can improve the effectiveness of test case prioritization techniques. To investigate the effectiveness of our approach, we performed an empirical study using two Java programs with multiple versions and requirements documents. Our results indicate that the use of requirements information during the test case prioritization process can be beneficial.

Regression testing is an expensive process used to validate modified software. Test case prioritization techniques improve the cost-effectiveness of regression testing by ordering test cases such that those that are more important are run earlier in the testing process. Many prioritization techniques have been proposed and evidence shows that they can be beneficial. It has been suggested, however, that the time constraints that can be imposed on regression testing by various software development processes can strongly affect the behavior of prioritization techniques. If this is correct, a better understanding of the effects of time constraints could lead to improved prioritization techniques, and improved maintenance and testing processes. We therefore conducted a series of experiments to assess the effects of time constraints on the costs and benefits of prioritization techniques. Our first experiment manipulates time constraint levels and shows that time constraints do play a significant role in determining both the cost-effectiveness of prioritization and the relative cost-benefit tradeoffs among techniques. Our second experiment replicates the first experiment, controlling for several threats to validity including numbers of faults present, and shows that the results generalize to this wider context. Our third experiment manipulates the numbers of faults present in programs to examine the effects of faultiness levels on prioritization, and shows that faultiness level affects the relative costeffectiveness of prioritization techniques. Taken together, these results have several implications for test engineers wishing to cost-effectively regression test their software systems. These include suggestions about when and when not to prioritize, what techniques to employ, and how differences in testing processes may relate to prioritization cost-effectiveness.

Regression testing is an important activity for controlling the quality of a software product, but it accounts for a large proportion of the costs of software. We believe that an understanding of the underlying relationships in data about software systems, including data correlations and patterns, could provide information that would help improve regression testing techniques. As an initial approach to investigating the relationships in massive data in software repositories, in this paper, we consider a clustering approach to help improve test case prioritization. We implemented new prioritization techniques that incorporate a clustering approach and utilize history data on real faults and code complexity. To assess our approach, we conducted empirical studies using an industrial software product, Microsoft Dynamics Ax, which contains real faults. Our results show that test case prioritization that utilizes a clustering approach can improve the rate of fault detection of test suites, and reduce the number of faults that slip through testing when testing activities are cut short and test cases must be omitted due to time constraints.I would also like to thank my beautiful wife for supporting me through this wild endeavor while caring for our four wonderful children, Anika, Austin, Bridger, and Cierra.

Test case prioritization provides a way to run test cases with the highest priority earliest. Numerous empirical studies have shown that prioritization can improve a test suite's rate of fault detection, but the extent to which these results generalize is an open question because the studies have all focused on a single procedural language, C, and a few specific types of test suites. In particular, Java and the JUnit testing framework are being used extensively to build software systems in practice, and the effectiveness of prioritization techniques on Java systems tested under JUnit has not been investigated. We have therefore designed and performed a controlled experiment examining whether test case prioritization can be effective on Java programs tested under JUnit, and comparing the results to those achieved in earlier studies. Our analyses show that test case prioritization can significantly improve the rate of fault detection of JUnit test suites, but also reveal differences with respect to previous studies that can be related to the language and testing paradigm. To investigate the practical implications of these results, we present a set of cost-benefits models for test case prioritization, and show how the effectiveness differences observed can result in savings in practice, but vary substantially with the cost factors associated with particular testing processes.