Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.

Artificial neural networks

Mutation Testing is a fault-based software testing technique that has been widely studied for over three decades The literature on Mutation Testing has contributed a set of approaches, tools, developments, and empirical results This paper provides a comprehensive analysis and survey of Mutation Testing The paper also presents the results of several development trend analyses These analyses provide evidence that Mutation Testing techniques and tools are reaching a state of maturity and applicability, while the topic of Mutation Testing itself is the subject of increasing interest

An Analysis and Survey of the Development of Mutation Testing

Regression testing is a testing activity that is performed to provide confidence that changes do not harm the existing behaviour of the software. Test suites tend to grow in size as software evolves, often making it too costly to execute entire test suites. A number of different approaches have been studied to maximize the value of the accrued test suite: minimization, selection and prioritization. Test suite minimization seeks to eliminate redundant test cases in order to reduce the number of tests to run. Test case selection seeks to identify the test cases that are relevant to some set of recent changes. Test case prioritization seeks to order test cases in such a way that early fault detection is maximized. This paper surveys each area of minimization, selection and prioritization technique and discusses open problems and potential directions for future research. Copyright © 2010 John Wiley & Sons, Ltd.

Regression testing minimization, selection and prioritization: a survey

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 had and can be expected to have.

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Supporting Controlled Experimentation with Testing Techniques: An Infrastructure and its Potential Impact

Empirical studies in software testing research may not be comparable, reproducible, or characteristic of practice. One reason is that real bugs are too infrequently used in software testing research. Extracting and reproducing real bugs is challenging and as a result hand-seeded faults or mutants are commonly used as a substitute. This paper presents Defects4J, a database and extensible framework providing real bugs to enable reproducible studies in software testing research. The initial version of Defects4J contains 357 real bugs from 5 real-world open source pro- grams. Each real bug is accompanied by a comprehensive test suite that can expose (demonstrate) that bug. Defects4J is extensible and builds on top of each program’s version con- trol system. Once a program is configured in Defects4J, new bugs can be added to the database with little or no effort. Defects4J features a framework to easily access faulty and fixed program versions and corresponding test suites. This framework also provides a high-level interface to common tasks in software testing research, making it easy to con- duct and reproduce empirical studies. Defects4J is publicly available at http://defects4j.org.

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Defects4J: a database of existing faults to enable controlled testing studies for Java programs

Software regression testing is an integral part of most major software projects. As projects grow larger and the number of tests increases, performing regression testing becomes more costly. If software engineers can identify and run tests that are more likely to detect failures during regression testing, they may be able to better manage their regression testing activities. In this paper, to help identify such test cases, we developed techniques that utilizes various types of information in software repositories. To assess our techniques, we conducted an empirical study using an industrial software product, Microsoft Dynamics AX, which contains real faults. Our results show that the proposed techniques can be effective in identifying test cases that are likely to detect failures.

Improving the effectiveness of test suite through mining historical data

Software engineering methodologies are subject to complex cost-benefit tradeoffs. Economic models can help practitioners and researchers assess methodologies relative to these tradeoffs. Effective economic models, however, can be established only through an iterative process of refinement involving analytical and empirical methods. Sensitivity analysis provides one such method. By identifying the factors that are most important to models, sensitivity analysis can help simplify those models; it can also identify factors that must be measured with care, leading to guidelines for better test strategy definition and application. In prior work we presented the first comprehensive economic model for the regression testing process, that captures both cost and benefit factors relevant to that process while supporting evaluation of these processes across entire system lifetimes. In this work we use sensitivity analysis to examine our model analytically and assess the factors that are most important to the model. Based on the results of that analysis, we propose two new models of increasing simplicity. We assess these models empirically on data obtained by using regression testing techniques on several non-trivial software systems. Our results show that one of the simplified models assesses the relationships between techniques in the same way as the full model.

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Using sensitivity analysis to create simplified economic models for regression testing

Increasingly, modern-day software systems are being built by combining externally-developed software components with application-specific code. For such systems, existing program-analysis-based software engineering techniques may not directly apply, due to lack of information about components. To address this problem, the use of component metadata has been proposed. Component metadata are metadata and metamethods provided with components, that retrieve or calculate information about those components. In particular, two component-metadata-based approaches for regression test selection are described: one using code-based component metadata and the other using specification-based component metadata. The results of empirical studies that illustrate the potential of these techniques to provide savings in re-testing effort are provided. Copyright (c) 2006 John Wiley & Sons, Ltd.

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Using component metadata to regression test component-based software†

Software security issues have been a major concern to the cyberspace community, so a great deal of research on security testing has been performed, and various security testing techniques have been developed. Most of these techniques, however, have focused on testing software systems after their implementation is completed. To build secure and dependable software systems in a cost-effective way, however, it is necessary to put more effort upfront during the software development life cycle. In this paper, we provided a security testing approach that derives test cases from design-level artifacts. The security testing approach we consider consists of four activities: building threat trees from threat modeling; generating security tests from threat trees; generating test inputs including valid and invalid inputs; and assigning input values to parameters. We also conducted an empirical study to show feasibility of our approach.

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Hyunsook Do

Papers

Improving the effectiveness of test suite through mining historical data

Using sensitivity analysis to create simplified economic models for regression testing

Using component metadata to regression test component-based software†

Security test generation using threat trees

Cost-effective regression testing through Adaptive Test Prioritization strategies