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

Requirements engineering is one of the most important and critical phases in the software development life cycle, and should be carefully performed to build high quality and reliable software. However, requirements are typically gathered through various sources and represented in natural language (NL), making requirements engineering a difficult, fault prone, and a challenging task. To address this challenge, we propose a model-based requirements verification method called NLtoSTD, which transforms NL requirements into a state transition diagram (STD) that can be verified through automated reasoning. This paper analyzes the effect of NLtoSTD method in improving the quality of requirements. To do so, we conducted an empirical study at North Dakota State University in which the participants employed the NLtoSTD method during the inspection of requirement documents to identify the amibiguities and incompleteness of requirements. The experiment results show that the proposed method is capable of finding ambiguities and missing functionalities in a set of NL requirements, and provided us with insights and feedback to improve the method. The results are promising and have motivated the refinement of NLtoSTD method and future empirical evaluation.

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Evaluating the use of model-based requirements verification method: A feasibility study

Software systems and their environment change are continuous. They are enhanced, corrected, and ported to new platforms. These changes can affect a system adversely, thus software engineers perform regression testing to ensure the quality of the modified systems. Regression testing is an integral part of most major software projects, but as projects grow larger and the number of tests increases, performing regression testing becomes more costly. To address this problem, many researchers and practitioners have proposed and empirically evaluated various regression testing techniques, such as regression test selection, test case prioritization, and test suite minimization. Recent surveys on these techniques indicate that this research area continues to grow, heuristics and the types of data utilized become diverse, and wider application domains have been considered. This chapter presents the current status and the trends of three regression testing techniques and discusses recent advances of each technique.

Recent Advances in Regression Testing Techniques

ContextRequirements engineering is one of the most important and critical phases in the software development life cycle, and should be carefully performed to build high quality and reliable software. However, requirements are typically gathered through various sources and are represented in natural language (NL), making requirements engineering a difficult, fault prone, and a challenging task. ObjectiveTo ensure high-quality software, we need effective requirements verification methods that can clearly handle and address inherently ambiguous nature of NL specifications. The objective of this paper is to propose a method that can address the challenges with NL requirements verification and to evaluate our proposed method through controlled experiments. MethodWe propose a model-based requirements verification method, called NLtoSTD, which transforms NL requirements into a State Transition Diagram (STD) that can help to detect and to eliminate ambiguities and incompleteness. The paper describes the NLtoSTD method to detect requirement faults, thereby improving the quality of the requirements. To evaluate the NLtoSTD method, we conducted two controlled experiments at North Dakota State University in which the participants employed the NLtoSTD method and a traditional fault checklist during the inspection of requirement documents to identify the ambiguities and incompleteness of the requirements. ResultsTwo experiment results show that the NLtoSTD method can be more effective in exposing the missing functionality and, in some cases, more ambiguous information than the fault-checklist method. Our experiments also revealed areas of improvement that benefit the method's applicability in the future. ConclusionWe presented a new approach, NLtoSTD, to verify requirements documents and two controlled experiments assessing our approach. The results are promising and have motivated the refinement of the NLtoSTD method and future empirical evaluation.

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Model-based requirements verification method

The use of relevant metrics of software systems could improve various software engineering tasks, but identifying relationships among metrics is not simple and can be very time consuming. Recommender systems can help with this decision-making process; many applications have utilized these systems to improve the performance of their applications. To investigate the potential benefits of recommender systems in regression testing, we implemented an item-based collaborative filtering recommender system that uses user interaction data and application change history information to develop a test case prioritization technique. To evaluate our approach, we performed an empirical study using three web applications with multiple versions and compared four control techniques. Our results indicate that our recommender system can help improve the effectiveness of test prioritization.

A collaborative filtering recommender system for test case prioritization in web applications

Exploratory testing provides an intuitive way for testing a software product that testers can apply, but the advantages of exploratory testing are generally outweighed by its disadvantages, mainly the time and resources necessary to perform it manually. To address this problem, in this research, we propose Model-Based Exploratory Testing (MBET), an approach that incorporates the advantages of exploratory testing and Model-Based Testing (MBT) that automates the testing processes. To support the MBET approach, we implemented an automated testing tool, the Crushinator. To assess our approach, we conducted an experiment using an educational game application with multiple versions and we collected the number and type of defects detected with the MBET and MBT approaches. Our results showed that, overall, MBET detected more defects than MBT. The results also showed that MBET detected certain defect types better than MBT while MBT detected other types better than MBET.

Hyunsook Do

Papers

Evaluating the use of model-based requirements verification method: A feasibility study

Recent Advances in Regression Testing Techniques

Model-based requirements verification method

A collaborative filtering recommender system for test case prioritization in web applications

Model-Based Exploratory Testing: A Controlled Experiment