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.

/pdf/supporting-controlled-experimentation-with-testing-pe7sdxpo8h.pdf

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.

/pdf/defects4j-a-database-of-existing-faults-to-enable-controlled-1x92gx1c9e.pdf

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

Towards semantically enhanced detection of emerging quality-related concerns in source code

Requirements engineering is a critical phase in software development that describes the customer needs and the specifications for the software solution. Requirements are gathered through various sources and the output is a list of requirements for a software product to be developed, written in Natural Language (NL). NL requirements are fault prone because stakeholders can interpret NL differently due to the inherent imprecision, ambiguity, and vagueness of NL. To address these problems, a model-based requirements verification method called NL to state transition diagram (STD) is proposed. This paper evaluates the ability of the NLtoSTD method to detect faults when used on NL requirements and to improve the software reliability. Overall, the result shows that the NLtoSTD is an effective requirements verification method.

/pdf/evaluating-the-use-of-model-based-requirement-verification-4i0kmdlzlm.pdf

Evaluating the Use of Model-Based Requirement Verification Method: An Empirical Study.

Many problems in software systems can ultimately be traced to problematic system requirements. To mitigate this problem, some systems builders have adopted a more systematic approach to requirements elicitation and analysis. However, many of these approaches treat requirements as isolated entities having no direct connection to the actual system implementation. We propose Veri-CCM, an approach that addresses potential problems both in the semantics and the syntax of requirements models. We also evaluate our approach with an empirical study.

VeriCCM: improving the syntax and semantics of requirements models

Quality bugs are difficult to detect because the implemented quality-related features are commonly scattered across the codebase. Unfortunately, this scattered information prevents software developers from holistically understanding the root cause of quality bugs. The traditional view of a system does not support a hierarchical code view for monitoring and tracing how quality features are topically related and how they interact with each other. In this paper, we show how these limitations can be overcome by leveraging a Hierarchical Dirichlet Process (HDP) topic modeling technique along with other supporting intermediary techniques such as structural and textual analyses to capture hierarchical topical relationships among quality features across the codebase that yield to detection of quality bugs. We present SoftQualTopicDetector, that is capable of clustering scattered quality concerns into a meaningful hierarchy to infer a set of candidate classes relevant for recommending repair of quality bugs. The higher the ranking of classes into a hierarchy the more relevant they are regarded to contain information about the bug under investigation. Additionally, SoftQualTopicDetector incorporates three rich visualization features for monitoring, prioritizing, and 3-D tracing of suspicious classes to enhance aspects of maintainability, functional suitability, and tracability. We conduct an empirical evaluation of SoftQualTopicDetector that shows an improvement over the baseline and the state-of-the-art by terms ≈17% and in of average precision and ≈21% recall respectively.

Hyunsook Do

Papers

Towards semantically enhanced detection of emerging quality-related concerns in source code

Evaluating the Use of Model-Based Requirement Verification Method: An Empirical Study.

VeriCCM: improving the syntax and semantics of requirements models

A Hierarchical Topical Modeling Approach for Recommending Repair of Quality Bugs

A multi-model framework for semantically enhancing detection of quality-related bug report descriptions