• KIPS Transactions on Software and Data Engineering
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• v.2 no.7
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• pp.451-460
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• 2013

In order to achieve high test coverage, it is usual to generate test data using various techniques including symbolic execution, data flow analysis or constraints solving. Recently, a technique for automated test data generation that fulfills high coverage effectively without those sophisticated means has been proposed. However, the technique shows its weakness in the generation of test data that leads to high coverage for programs having branch conditions where different memory locations are binded during execution. For certain programs with flag conditions, in particular, high coverage can not be achieved because specific branches are not executed. To address the problem, this paper presents dynamic branch coverage criteria and a test data generation technique based on the notion of dynamic branch. It is shown that the proposed technique compared to the previous approach is more effective by conducting experiments involving programs with flag conditions.

• The Journal of the Korea Contents Association
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• v.12 no.11
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• pp.39-48
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• 2012

It is well known that software testing amounts for a significant portion of software development cost. In order to reduce the cost of software testing. a lot of researches on automated test data generation have been performed. Sophisticated tools for performing symbolic execution or solving a system of path constraints are required to support automated test data generation. Developing or purchasing those tools leads to another factor of increasing the cost involving software testing. In this paper, we propose a dynamic test data generation approach that does not depend on symbolic execution or constraint solving at all. The proposed approach extends Korel's path-oriented method to satisfy the branch coverage criterion effectively. We conducted an experiment to evaluate the effectiveness of the proposed technique with a triangle classification program to show that branch coverage can be easily achieved.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06b
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• pp.150-152
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• 2012

일반적으로 테스트 데이터 자동 생성을 지원하기 위해 심볼릭 실행기나 제약 해결기와 같은 도구를 요구한다. 그러나 이와 같은 도구들을 개발하는 것은 상당한 노력이 요구되는 것도 사실이다. 이 논문에서는 이러한 도구들의 지원 없이 분기 커버리지를 효과적으로 달성할 수 있는 테스트 데이터 생성 방법을 제안한다. 이를 위해 경로 지향 테스트 데이터 생성을 위해 개발된 Korel의 방법을 확장하여 프로그램의 분기들을 가능한 많이 실행할 수 있는 테스트 데이터를 효과적으로 생성하는 방법을 제안한다.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.24 no.3
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• pp.437-444
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• 2014

In this paper, we introduce a Graph based Binary Code Execution Path Exploration Platform. In the graph, a node is defined as a conditional branch instruction, and an edge is defined as the other instructions. We implemented prototype of the proposed method and works well on real binary code. Experimental results show proposed method correctly explores execution path of target binary code. We expect our method can help Software Assurance, Secure Programming, and Malware Analysis more correct and efficient.

• Smart Structures and Systems
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• v.21 no.5
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• pp.687-694
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• 2018

The problem of wheel tread defects has become a major challenge for the health management of high-speed rail as a wheel defect with small radius deviation may suffice to give rise to severe damage on both the train bogie components and the track structure when a train runs at high speeds. It is thus highly desirable to detect the defects soon after their occurrences and then conduct wheel turning for the defective wheelsets. Online wheel condition monitoring using wheel impact load detector (WILD) can be an effective solution, since it can assess the wheel condition and detect potential defects during train passage. This study aims to develop an FBG-based track-side wheel condition monitoring method for the detection of wheel tread defects. The track-side sensing system uses two FBG strain gauge arrays mounted on the rail foot, measuring the dynamic strains of the paired rails excited by passing wheelsets. Each FBG array has a length of about 3 m, slightly longer than the wheel circumference to ensure a full coverage for the detection of any potential defect on the tread. A defect detection algorithm is developed for using the online-monitored rail responses to identify the potential wheel tread defects. This algorithm consists of three steps: 1) strain data pre-processing by using a data smoothing technique to remove the trends; 2) diagnosis of novel responses by outlier analysis for the normalized data; and 3) local defect identification by a refined analysis on the novel responses extracted in Step 2. To verify the proposed method, a field test was conducted using a test train incorporating defective wheels. The train ran at different speeds on an instrumented track with the purpose of wheel condition monitoring. By using the proposed method to process the monitoring data, all the defects were identified and the results agreed well with those from the static inspection of the wheelsets in the depot. A comparison is also drawn for the detection accuracy under different running speeds of the test train, and the results show that the proposed method can achieve a satisfactory accuracy in wheel defect detection when the train runs at a speed higher than 30 kph. Some minor defects with a depth of 0.05 mm~0.06 mm are also successfully detected.