• ETRI Journal
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• v.31 no.2
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• pp.140-150
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• 2009

Although mutation testing is potentially powerful, it is a computationally expensive testing method. To investigate how we can reduce the cost of object-oriented mutation testing, we have conducted empirical studies on class mutation operators. We applied class mutation operators to 866 classes contained in six open-source programs. An analysis of the number and the distribution of class mutants generated and preliminary data on the effectiveness of some operators are provided. Our study shows that the overall number of class mutants is smaller than for traditional mutants, which offers the possibility that class mutation can be made practically affordable.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.5
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• pp.571-575
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• 2010

Mutation testing is known as a very useful technique for measuring the effectiveness of a test data set and finding weak points of the test set. An equivalent mutant degrades the effectiveness of mutation testing. Elimination of equivalent mutants is a very important problem in mutation testing.In this paper, we proposed kinds of methods for detecting class-level equivalent mutants. These methods judge the equivalency of mutants through structural informations and behavioral information of the original program and mutants using static analysis. We found that our approach can detect not a few of equivalent mutants and expected that the cost of mutation testing can be saved considerably.

• The KIPS Transactions:PartD
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• v.16D no.4
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• pp.517-526
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• 2009

It is possible to indicate the complex design and execution of object-oriented program with dynamic UML diagram. This paper shows the way how to make several test cases from sequence, state, and activity diagram among dynamic UML diagram. Three dynamic UML diagrams about withdrawal work of ATM simulation program are drawn. Then different test cases are created from these diagrams using previously described ways. To evaluate effectiveness of test cases, mutation testing is executed. Mutants are made from MuClipse plug-in tool based on Eclipse which supports many traditional and class mutation operators. Finally we've got the result of mutation testing and compare effectiveness of test cases, etc. Through this document, we've known some hints that how to choose the way of making test cases.

• Journal of Information Processing Systems
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• v.7 no.2
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• pp.363-384
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• 2011

As the complexity of software is increasing, generating an effective test data has become a necessity. This necessity has increased the demand for techniques that can generate test data effectively. This paper proposes a test data generation technique based on adequacy based testing criteria. Adequacy based testing criteria uses the concept of mutation analysis to check the adequacy of test data. In general, mutation analysis is applied after the test data is generated. But, in this work, we propose a technique that applies mutation analysis at the time of test data generation only, rather than applying it after the test data has been generated. This saves significant amount of time (required to generate adequate test cases) as compared to the latter case as the total time in the latter case is the sum of the time to generate test data and the time to apply mutation analysis to the generated test data. We also use genetic algorithms that explore the complete domain of the program to provide near-global optimum solution. In this paper, we first define and explain the proposed technique. Then we validate the proposed technique using ten real time programs. The proposed technique is compared with path testing technique (that use reliability based testing criteria) for these ten programs. The results show that the adequacy based proposed technique is better than the reliability based path testing technique and there is a significant reduce in number of generated test cases and time taken to generate test cases.

• Journal of Information Processing Systems
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• v.13 no.5
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• pp.1089-1102
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• 2017

In software systems, it has been observed that a fault is often caused by an interaction between a small number of input parameters. Even for moderately sized software systems, exhaustive testing is practically impossible to achieve. This is either due to time or cost constraints. Combinatorial (t-way) testing provides a technique to select a subset of exhaustive test cases covering all of the t-way interactions, without much of a loss to the fault detection capability. In this paper, an approach is proposed to generate 2-way (pairwise) test sets using genetic algorithms. The performance of the algorithm is improved by creating an initial solution using the overlap coefficient (a similarity matrix). Two mutation strategies have also been modified to improve their efficiency. Furthermore, the mutation operator is improved by using a combination of three mutation strategies. A comparative survey of the techniques to generate t-way test sets using genetic algorithms was also conducted. It has been shown experimentally that the proposed approach generates faster results by achieving higher percentage coverage in a fewer number of generations. Additionally, the size of the mixed covering arrays was reduced in one of the six benchmark problems examined.

• The KIPS Transactions:PartD
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• v.8D no.1
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• pp.81-86
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• 2001

one key goal of software testing is to generate a 'good' test data set, which is consideres as the most difficult and time-consuming task. This paper discusses how genetic algorithns can be used for automatic generation of test data set for software testing. We employ mutation testing to show the effectiveness of genetic algorithms (GAs) in automatic test data generation. The approach presented in this paper is different from other in that test generation process requireas no lnowledge of implementation details of a program under test. In addition, we have conducted some experiments and compared our approach with random testing which is also regarded as a black-box test generation technique to show its effectiveness.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.18
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• pp.7885-7889
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• 2014

Background: Activating mutations of epidermal growth factor receptor (EGFR) could predict response to tyrosine kinase inhibitor (TKI) treatment in patients with non-small cell lung cancer (NSCLC). However, the detection of EGFR mutation is frequently challenging in clinical practice for the lack of tumor tissue. The aim of this study was to investigate the feasibility of performing EGFR mutation testing on various types of liquid-based cytology (LBC) samples. Materials and Methods: A total of 434 liquid-based cytology samples were collected from March 2010 and November 2013. Among them, 101 with diagnosis of lung adenocarcinoma had paired surgically resected specimens. The ADx Amplification Refractory Mutation System (ADx-ARMS) was used to determine EGFR mutation status both in LBC and resected samples. Results: All liquid-based cytology samples were adequate for EGFR mutation analysis. The mutation rate was 50.5% in the 434 NSCLC patients with LBC samples and the incidence rates of EGFR mutation were consistent among different specimens. We also detected EGFR positives in 52.5% (53/101) patients with paired histologic specimens. The concordance rate of EGFR mutation between LBC samples and paired histologic specimens was 92.1%. Conclusions: Our results suggest that liquid-based cytology samples are highly reliable for EGFR mutation testing in patients with NSCLC.

• Journal of KIISE
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• v.43 no.9
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• pp.974-982
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• 2016

Mutation analysis (or software mutation analysis) generates variants of a target program by injecting systematic code changes to the target program, and utilizes the variants to analyze the target program behaviors. Effective mutation analyses require adequate mutation operators that generate diverse variants for use in the analysis. However, the current mutation analysis tools for Java programs have limitations, since they support only limited types of mutation operators and do not support recent language features such as Java8. In this study, we present Mutagen4J, a new mutant generation tool for Java programs. Mutagen4J additionally supports mutation operators recently shown to generate various mutants and fully supports recent Java language features. The experimental results show that Mutagen4J generates useful mutants for analyses 2.3 times more than the existing Java mutation tools used for the study.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.4
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• pp.115-124
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• 2021

Since function block diagram (FBD) programs are widely used to implement safety-critical systems, effective testing for FBD programs has become important. Mutation testing, a fault-based testing, is highly effective in fault detection but computationally expensive. To support testers for FBD programs, we propose an automated mutant generator for FBD programs. We designed the MuGenFBD tool with the cost and equivalent mutant issues in consideration. We conducted experiments on real industrial examples to present the performance of MuGenFBD. The results show that MuGenFBD can generate mutants for FBD programs automatically with low probability of equivalent mutants and low cost. This tool can effectively support mutation analysis and mutation-adequate test generation for FBD programs.

• Tuberculosis and Respiratory Diseases
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• v.75 no.5
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• pp.181-187
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• 2013

The emergence of new therapeutic agents for non-small cell lung cancer (NSCLC) implies that histologic subtyping and molecular predictive testing are now essential for therapeutic decisions. Histologic subtype predicts the efficacy and toxicity of some treatment agents, as do genetic alterations, which can be important predictive factors in treatment selection. Molecular markers, such as epidermal growth factor receptor (EGFR) mutation and anaplastic lymphoma kinase (ALK) rearrangement, are the best predictors of response to specific tyrosine kinase inhibitor treatment agents. As the majority of patients with NSCLC present with unresectable disease, it is therefore crucial to optimize the use of tissue samples for diagnostic and predictive examinations, particularly for small biopsy and cytology specimens. Therefore, each institution needs to develop a diagnostic approach requiring close communication between the pulmonologist, radiologist, pathologist, and oncologist in order to preserve sufficient biopsy materials for molecular analysis as well as to ensure rapid diagnosis. Currently, personalized medicine in NSCLC is based on the histologic subtype and molecular status. This review summarizes strategies for tissue acquisition, histologic subtyping and molecular analysis for predictive testing in NSCLC.