• KIISE Transactions on Computing Practices
• /
• v.22 no.8
• /
• pp.393-398
• /
• 2016

Collective Adaptive System is an adaptive multi-agent system which accomplishes its goal by collaborating various agents. Because the collective property of the Collective Adaptive System is accomplished by the goal of the system being based on collaboration, testing the goal accomplishment and their interactions among heterogeneous agents is important. This paper presents a feasibility study of applying a model-based testing approach using task-based test model to a Collective Adaptive System. This paper describes additional information to be applied for Collective Adaptive System for future studies. To analyze our approach, we applied the proposed approach to a smart home system as a case study; our results indicated that we can systematically derive test cases to check whether the Collective Adaptive System successfully achieved its goals by modifying and extending the existing task model.

• Journal of KIISE:Computing Practices and Letters
• /
• v.9 no.4
• /
• pp.431-438
• /
• 2003

In this study, we have developed a web-based adaptive testing system using item response theory´s computerized adaptive testing to diagnose underachievers, and to check the evaluation results immediately. Adaptive testing system simple is not the fact that it presents a question to students. It calculates information of a question and presents the question to students. It controls the response of the students under extraction conditions of the next question. It extracts the question which is the most suitable it presents. In this adaptive testing system, you can extract questions according to the level of the students, and adjust the length and the level of the difficulty according to the response of the students.

• Journal of Digital Convergence
• /
• v.4 no.1
• /
• pp.43-54
• /
• 2006

New technologies have turned theories of education engineering into practice and also made education more efficient. One of them is the theory of 'Computer Adaptive Testing(CAT)'. This study is aimed to design and develop a web-based teaming system by making up for the weak points in the existing computer adaptive testing(CAT). The characteristics of the system are as follows: Firstly, tests can be controlled to fit the teachers' purposes by putting in different levels of both questions and samples. Secondly, this system does not test the same number of questions as students with different levels of ability do on a paper test, but a test can be taken according to their level. Thus this system is able to correctly judge learners' ability in a short time.

• Journal of Institute of Control, Robotics and Systems
• /
• v.21 no.7
• /
• pp.699-708
• /
• 2015

In the testing of a software component-based robot system, generating test cases for the system is a time-consuming and difficult task that requires the combining of test data. This paper proposes an adaptive combinatorial testing method which is based on the input/output relationship among components and which automatically generates the test cases for the system. The proposed algorithm first generates an input/output relationship graph in order to analyze the input/output relationship of the system. It then generates the reduced set of test cases according to the analyzed type of input/output relationship. To validate the proposed algorithm some comparisons are given in terms of the time complexity and the number of test cases.

• Journal of the Korea Society for Simulation
• /
• v.20 no.3
• /
• pp.19-28
• /
• 2011

Adaptive Random Testing (ART) aims to enhance the performance of pure random testing by detecting failure region in a software. The ART algorithm generates effective test cases which requires less number of test cases than that of pure random testing. However, all ART algorithms currently proposed are designed for the tests of monolithic system or unit level. In case of integrated system tests, ART approaches do not achieve same performances as those of ARTs applied to the unit or monolithic system. In this paper, we propose an extended ART algorithm which can be applied to the integrated system testing environment without degradation of performance. The proposed approach investigates an input distribution of the unit under a test with limited number of seed input data and generates information to be used to resizing input domain partitions. The simulation results show that our approach in an integration environment could achieve similar level of performance as an ART is applied to a unit testing. Results also show resilient effectiveness for various failure rates.

• Journal of KIISE:Computer Systems and Theory
• /
• v.26 no.12
• /
• pp.1447-1457
• /
• 1999

본 논문에서는 일반적인 네트워크에서 적응력 있는(adaptive) 분산형 시스템 레벨 결함 진단을 위한 분할 기법을 제안한다. 적응력 있는 분산형 시스템 레벨 결함 진단 기법에서는 시스템의 형상이 변경될 때마다 시험 할당 알고리즘이 수행되므로 적응력 없는 결함 진단 기법에 비하여 결함 감지를 위한 시험의 갯수를 줄일 수 있다. 기존의 시험 할당 알고리즘들은 전체 시스템을 대상으로 하는 비분할(non-partitioning) 방식을 이용하였는데, 이 기법은 불필요한 과다한 메시지를 생성한다. 본 논문에서는 전체 시스템을 이중 연결 요소(biconnected component) 단위로 분할한 후, 시험 할당은 각 이중 연결 요소 내에서 수행한다. 이중 연결 요소의 관절점(articulation point)의 특성을 이용하여 각 시험 할당에 필요한 노드의 수를 줄임으로서, 비분할 기법들에 비해 초기 시험 할당에 필요한 메시지의 수를 감소시켰다. 또한 결함이 발생한 경우나 복구가 완료된 경우의 시험 재 할당은 직접 영향을 받는 이중 연결 요소내로 국지화(localize) 시켰다. 본 논문의 시스템 레벨 결함 진단 기법의 정확성을 증명하였으며, 기존 비분할 방식의 시스템 레벨 결함 진단 기법과의 성능 분석을 수행하였다.Abstract We propose an adaptive distributed system-level diagnosis using partitioning method in arbitrary network topologies. In an adaptive distributed system-level diagnosis, testing assignment algorithm is performed whenever the system configuration is changed to reduce the number of tests in the system. Existing testing assignment algorithms adopt a non-partitioning approach covering the whole system, so they incur unnecessary extra message traffic and time. In our method, the whole system is partitioned into biconnected components, and testing assignment is performed within each biconnected component. By exploiting the property of an articulation point of a biconnected component, initial testing assignment of our method performs better than non-partitioning approach by reducing the number of nodes involved in testing assignment. It also localizes the testing reassignment caused by system reconfiguration within the related biconnected components. We show that our system-level diagnosis method is correct and analyze the performance of our method compared with the previous non-partitioning ones.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.8 no.6
• /
• pp.2139-2151
• /
• 2014

Embedded system testing, especially long-term reliability testing, of flash memory solutions such as embedded multi-media card, secure digital card and solid-state drive involves strategic decision making related to test sample size to achieve high test coverage. The test sample size is the number of flash memory devices used in a test. Earlier, there were physical limitations on the testing period and the number of test devices that could be used. Hence, decisions regarding the sample size depended on the experience of human testers owing to the absence of well-defined standards. Moreover, a lack of understanding of the importance of the sample size resulted in field defects due to unexpected user scenarios. In worst cases, users finally detected these defects after several years. In this paper, we propose that a large number of potential field defects can be detected if an adequately large test sample size is used to target weak features during long-term reliability testing of flash memory solutions. In general, a larger test sample size yields better results. However, owing to the limited availability of physical resources, there is a limit on the test sample size that can be used. In this paper, we address this problem by proposing a self-adaptive reliability testing scheme to decide the sample size for effective long-term reliability testing.

• The Journal of Korean Association of Computer Education
• /
• v.4 no.2
• /
• pp.21-29
• /
• 2001

The purpose of this study is to design and implementation of the Web-based Individual Computerized Adaptive Testing(CAT) system. The Web-based Individual CAT is a kind of test system to present a set of the problems divided into basic level, intermediate level and advanced level according to the saved results after doing a diagnostic test related to each unit. The diagnostic test is done to pull out the necessary items which learners have to study. The strong points of the web-based computerized adaptive testing system are to reduce the problems of distributed teachers and to have the effect of individual learning by using this system.

• Journal of Information Processing Systems
• /
• v.13 no.4
• /
• pp.703-715
• /
• 2017

Mammogram images are sensitive in nature and even a minor change in the environment affects the quality of the images. Due to the lack of expert radiologists, it is difficult to interpret the mammogram images. In this paper an algorithm is proposed for a computer-aided diagnosis system, which is based on the wavelet based adaptive sigmoid function. The cascade feed-forward back propagation technique has been used for training and testing purposes. Due to the poor contrast in digital mammogram images it is difficult to process the images directly. Thus, the images were first processed using the wavelet based adaptive sigmoid function and then the suspicious regions were selected to extract the features. A combination of texture features and gray-level co-occurrence matrix features were extracted and used for training and testing purposes. The system was trained with 150 images, while a total 100 mammogram images were used for testing. A classification accuracy of more than 95% was obtained with our proposed method.

• The Journal of Korean Association of Computer Education
• /
• v.9 no.2
• /
• pp.69-78
• /
• 2006

Educational evaluation is not the work to rank learners hierarchically, but the thing to increase the educational effectiveness by solving a learner's problem and improving the education process from the proper evaluation. The conventional evaluation systems have measured a learner's recognition level by dichotomy. Although they support the evaluation depending on a learner's academic ability and supply the feedback for wrong selection, it is insufficient to take out study-motive and give the establishment for a guidance point of learning. In this paper, we propose the web-based individual adaptive testing system in considering partial score for a learner. Our system are effective to estimate the ability of learner by considering partial score in detail and offer an feedback study from the self-diagnosis function for a learning results.