• Journal of the Korean Society of Safety
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• v.33 no.3
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• pp.83-91
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• 2018

This paper suggests a weibull time delay model to evaluate failure risks in FMEA(failure modes and effects analysis). Assuming three types of loss functions for delayed time in failure cause detection, the risk of each failure cause is evaluated as its occurring frequency and expected loss. Since the closed form solution of the risk metric cannot be obtained, a statistical computer software R program is used for numerical calculation. When the occurrence and detection times have a common shape parameter, though, some simple results of mathematical derivation are also available. As an enormous quantity of field data becomes available under recent progress of data acquisition system, the proposed risk metric will provide a more practical and reasonable tool for evaluating the risks of failure causes in FMEA.

• ETRI Journal
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• v.46 no.1
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• pp.106-117
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• 2024

Large language models (LLMs) have revolutionized various applications in natural language processing and exhibited proficiency in generating programming code. We propose a framework for evaluating the code generation ability of LLMs and introduce a new metric, pass-ratio@n, which captures the granularity of accuracy according to the pass rate of test cases. The framework is intended to be fully automatic to handle the repetitive work involved in generating prompts, conducting inferences, and executing the generated codes. A preliminary evaluation focusing on the prompt detail, problem publication date, and difficulty level demonstrates the successful integration of our framework with the LeetCode coding platform and highlights the applicability of the pass-ratio@n metric.

• Journal of KIISE
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• v.43 no.12
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• pp.1334-1341
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• 2016

In recent years, the importance of managing software defects in the implementation stage has emerged because of the rapid development and wide-range usage of intelligent smart devices. Even if not a few studies have been conducted on the prediction models for software defects, their outcomes have not been widely shared. This paper proposes an efficient probabilistic management model of software metrics based on the Bayesian network, to overcome limits such as binary defect prediction models. We expect the proposed model to configure the Bayesian network by taking advantage of various software metrics, which can help in identifying improvements for refactoring. Once the source code has improved through code refactoring, the measured related metric values will also change. The proposed model presents probability values reflecting the effects after defect removal, which can be achieved by improving metrics through refactoring. This model could cope with the conclusive binary predictions, and consequently secure flexibilities on decision making, using indeterminate probability values.

• The Journal of Society for e-Business Studies
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• v.8 no.2
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• pp.113-129
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• 2003

Component-based software development (CBSD) has been recognized effective reuse techniques for software development by many of researchers and companies. The purpose of CBSD is to produce a high quality software system quickly through using verified software component which is contained fine-grained business logics. This paper suggests the metrics and techniques for to extract component and its interface from legacy object oriented application. For extract component, we apply metrics to measure complexity, cohesion and coupling to the legacy system.

• Journal of Korean Society for Quality Management
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• v.25 no.4
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• pp.115-130
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• 1997

SQM (Software Quality Metrics) is a methodology whose primary objective is the measurement of compliances to requirements using a set of software life cycle properties called quality factors, which is based on the hierachical relationshiips between factors, criteria and elements. For this study, two factors (Correctness, Maintainability) and five criteria were selected. In addition, several tens of quality elements were developed to su, pp.rt them. Qualities of three computer programs which are being used for engineering purpose were measured. As a result, it is concluded that SQM is a valuable method for continuously monitoring the pulse of software quality development and that it can be used as a tool for software quality assurance.

• Journal of Internet Computing and Services
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• v.9 no.5
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• pp.85-97
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• 2008

Recently, service-oriented systems have been issued by their properties of reducing software development time and effort by reusing functional service units. The reusability of services can effectively promote through loose coupling between services and loose coupling between services depends on component-based system. That is, the component-based system is designed by grouping the tightly coupled classes of the object-oriented system and the service-oriented system is designed by the component-based system. Therefore, to design the component-based system and service-oriented system efficiently, a metric to measure the coupling between classes accurately needs. In this paper, we propose a coupling metric between classes applying a structural property, a dynamic property, and the normalized value by 0-1. We prove the theoretical soundness of the proposed metric by the axioms of briand et al, and suggest the accuracy and practicality through a case study. We suggest the evaluation results of the proposed metric through a comparison with the conventional metrics.

• Journal of KIISE:Software and Applications
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• v.36 no.10
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• pp.786-792
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• 2009

A kernel in support vector machines can be described as a similarity measure between data, and this measure is used to find an optimal hyperplane that classifies patterns. It is therefore important to effectively incorporate the characteristics of data into the similarity measure. To find an optimal similarity between amino acid sequences, we propose a real exponent exponential form of the two metrices, which are derived from the evolutionary relationships of amino acids and the hydrophobicity of amino acids. We prove that the proposed metric satisfies the conditions to be a metric, and we find a relation between the proposed metric and the metrics in the string kernels which are widely used for the processing of amino acid sequences and DNA sequences. In the prediction experiments on the cleavage site of the signal peptide, the optimal metric can be found in the proposed metrics.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.12
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• pp.3715-3724
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• 2009

Recently, component-based software development is getting accepted in industry as a new effective software development paradigm. Since the introduction of component-based software engineering (CBSE) in later 90's, the CBSD research has focused largely on component modeling, methodology, architecture and component platform. However, as the number of components available on the market increases, it becomes more important to devise metrics to quantify the various characteristics of components. In this Paper, we propose metrics for measuring the complexity, customizability, and reusability of software components. Complexity metric can be used to evaluate the complexity of components. Customizability is used to measure how efficiently and widely the components can be customized for organization specific requirement. Reusability can be used to measure the degree of features that are reused in building applications. We expect that these metrics can be effectively used to quantify the characteristics of components.

• Journal of the Korea Society of Computer and Information
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• v.20 no.1
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• pp.197-206
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• 2015

Defect severity that is a measure of the impact caused by the defect plays an important role in software quality activities because not all software defects are equal. Earlier studies have concentrated on defining defect severity levels, but there have almost never been trials of measuring module severity. In this paper, first, we define a defect severity metric in the form of an exponential function using the characteristics that defect severity values increase much faster than severity levels. Then we define a new metrics set for software module severity using the number of defects in a module and their defect severity metric values. In order to show the applicability of the proposed metrics, we performed an analytical validation using Weyuker's properties and experimental validation using NASA open data sets. The results show that ms is very useful for measuring the module severity and msd can be used to compare different systems in terms of module severity.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.406-409
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• 2023

With the explosion of information, recommendation algorithms are becoming increasingly important in providing people with appropriate content, enhancing their online experience. In this paper, we propose a recommender system using advanced deep reinforcement learning(DRL) techniques. This method is more adaptive and integrative than traditional methods. We selected the MovieLens dataset and employed the precision metric to assess the effectiveness of our algorithm. The result of our implementation outperforms other baseline techniques, delivering better results for Top-N item recommendations.