• 한국정보처리학회논문지
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• 제7권4호
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• pp.1112-1121
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• 2000

Design metrics that quantify the design phase play an important role in reducing system development cost because the problems in early phases of software development seriously affected the quality of the late products. Real-time systems such as telecommunication systems are so large that design quantification is more important in real-time system design. Although many metrics have been proposed, few of them are correctly validated. This paper revises the SDL metrics set proposed in earlier study [9] and perform an analytical validation o the metrics set. Axiomatic approach and dimensional analysis are used for metrics validation and the old metrics are revised ot satisfy the validation criteria.

• 한국컴퓨터정보학회논문지
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• 제20권1호
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• pp.197-206
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• 2015

모든 소프트웨어 결함들이 시스템에 같은 정도의 영향을 미치는 것이 아니므로 결함이 미치는 충격의 정도를 나타내는 결함 심각도는 소프트웨어 품질 관련 작업들에 중요한 역할을 하고 있다. 결함 심각도 관련 기존 연구들은 심각도 레벨은 정의하였지만 품질 작업의 기본 단위인 모듈의 심각도에 관한 언급은 거의 없었다. 본 논문에서는 심각도 레벨이 증가함에 따라 심각도 값이 급격히 증가하는 심각도 성질을 이용하여 결함 심각도 메트릭을 지수 함수 형태로 정의한 후, 모듈 내부의 결함 수와 결함 심각도 메트릭에 기반한 새로운 모듈 심각도 메트릭 집합을 정의하였다. 제안 메트릭들의 적용가능성을 보이기 위해 Weyuker 기준들을 이용한 분석적 검증과 NASA 공개 데이터 집합을 이용한 실험적 검증을 수행하였으며, 제안 메트릭들 중 ms는 모듈의 심각도 정량화에, msd는 심각도에 기반한 시스템간의 비교에 매우 유용하게 사용될 수 있다는 것을 보였다.

• Journal of Information Processing Systems
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• 제8권2호
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• pp.241-262
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• 2012

An understanding of quality attributes is relevant for the software organization to deliver high software reliability. An empirical assessment of metrics to predict the quality attributes is essential in order to gain insight about the quality of software in the early phases of software development and to ensure corrective actions. In this paper, we predict a model to estimate fault proneness using Object Oriented CK metrics and QMOOD metrics. We apply one statistical method and six machine learning methods to predict the models. The proposed models are validated using dataset collected from Open Source software. The results are analyzed using Area Under the Curve (AUC) obtained from Receiver Operating Characteristics (ROC) analysis. The results show that the model predicted using the random forest and bagging methods outperformed all the other models. Hence, based on these results it is reasonable to claim that quality models have a significant relevance with Object Oriented metrics and that machine learning methods have a comparable performance with statistical methods.

• Asia pacific journal of information systems
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• 제30권3호
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• pp.457-496
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• 2020

This study aims to extensively analyze the performance of various Machine Learning (ML) techniques for predicting version to version change-proneness of source code Java files. 17 object-oriented metrics have been utilized in this work for predicting change-prone files using 31 ML techniques and the framework proposed has been implemented on various consecutive releases of two Java-based software projects available as plug-ins. 10-fold and inter-release validation methods have been employed to validate the models and statistical tests provide supplementary information regarding the reliability and significance of the results. The results of experiments conducted in this article indicate that the ML techniques perform differently under the different validation settings. The results also confirm the proficiency of the selected ML techniques in lieu of developing change-proneness prediction models which could aid the software engineers in the initial stages of software development for classifying change-prone Java files of a software, in turn aiding in the trend estimation of change-proneness over future versions.

• International Journal of Computer Science & Network Security
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• 제21권7호
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• pp.284-296
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• 2021

Quality Models are important element of the software industry to develop and implement the best quality product in the market. This type of model provides aid in describing quality measures, which directly enhance the user satisfaction and software quality. In software development, the inheritance technique is an important mechanism used in object-oriented programming that allows the developers to define new classes having all the properties of super class. This technique supports the hierarchy design for classes and makes an "is-a" association among the super and subclasses. This paper describes a standard procedure for validating the inheritance metric in Quality Model for Object-Oriented Design (QMOOD) by using a set of nine properties established by Weyuker. These properties commonly using for investigating the effectiveness of the metric. The integration of two measuring methods (i.e. QMOOD and Weyuker) will provide new way for evaluating the software quality based on the inheritance context. The output of this research shows the extent of satisfaction of the inheritance metric in QMOOD against Weyuker nine properties. Further results proved that Weyker's property number nine could not fulfilled by any inheritance metrics. This research introduces a way for measuring software that developed using object-oriented approach. The theoretical validation of the inheritance metric presented in this paper is a small step taken towards producing quality software and in providing assistance to the software industry.

• Korean Journal of Radiology
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• 제21권1호
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• pp.88-100
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• 2020

Objective: We aimed to develop and validate a deep learning system for fully automated segmentation of abdominal muscle and fat areas on computed tomography (CT) images. Materials and Methods: A fully convolutional network-based segmentation system was developed using a training dataset of 883 CT scans from 467 subjects. Axial CT images obtained at the inferior endplate level of the 3rd lumbar vertebra were used for the analysis. Manually drawn segmentation maps of the skeletal muscle, visceral fat, and subcutaneous fat were created to serve as ground truth data. The performance of the fully convolutional network-based segmentation system was evaluated using the Dice similarity coefficient and cross-sectional area error, for both a separate internal validation dataset (426 CT scans from 308 subjects) and an external validation dataset (171 CT scans from 171 subjects from two outside hospitals). Results: The mean Dice similarity coefficients for muscle, subcutaneous fat, and visceral fat were high for both the internal (0.96, 0.97, and 0.97, respectively) and external (0.97, 0.97, and 0.97, respectively) validation datasets, while the mean cross-sectional area errors for muscle, subcutaneous fat, and visceral fat were low for both internal (2.1%, 3.8%, and 1.8%, respectively) and external (2.7%, 4.6%, and 2.3%, respectively) validation datasets. Conclusion: The fully convolutional network-based segmentation system exhibited high performance and accuracy in the automatic segmentation of abdominal muscle and fat on CT images.

• 한국정보과학회논문지:컴퓨팅의 실제 및 레터
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• 제7권2호
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• pp.151-164
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• 2001

최근 가장 대표적인 객체지향 언어로 사용되는 Java는 일반적인 애플리케이션뿐만 아니라 인터넷/인트라넷 기반 프로그램 개발, 나아가 컴포넌트 기반 개발에 이르기까지 다양한 분야에서 개발 언어로 사용되고 있다. 따라서 개발된 프로그램의 재사용 및 유지보수 관점에서 프로그램 품잘평가는 보다 중요한 쟁점이 되고 있으므로 기존의 Java 애플리케이션을 포함하여 현재 개발된 프로그램의 품질평가에 필요한 메트릭 측정이 필요하다. 하지만, 이미 제안된 객체지향 소프트에어 메트릭이 현재의 Java 프로그램의 특성에 적합한지에 대한 타당성 검증이 필요하므로 본 논문에서는 기존의 객체지향 메트릭이 Java 프로그램에 적합한지 여부를 결정하기 위해 필요한 메트릭 측정 시스템을 구축하여 Java 프로그램에 적합한 메트릭 제안을 지원하고자 한다. 본 시스템은 Briand가 기존의 객체지향 소프트웨어 메트릭을 수학적으로 정형화시켜 분류한 메트릭을 Java 프로그램에 적용시켜 제안된 메트릭이 프로그램에 타당성 있는지 검증함으써 명확한 품질평가도구 개발을 지원하고자 한다. 본 시스템을 통해 Java 소스 프로그램으로부터 정량적 정보를 보다 빠르고 정확하게 산출함으로써 기존의 객체지향 메트릭에 대한 검증을 비교 및 분석 수행할 수 있으며, 타당성 문제가 있다면 새로운 메트릭의 제안 및 보완을 고려함으로써 Java 프로그램에 적합한 메트릭 확립을 가능하게 할 것이다.

• 대한인간공학회지
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• 제29권4호
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• pp.647-654
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• 2010

In a service environment, the spatial layout is an important factor that has a great impact on customers' behavioral characteristics including wayfinding and purchasing. Previous studies have shown a gap between marketing, focusing solely on profitability and satisfaction, and architecture, looking only into efficiency of pedestrian flow. To balance such disparity, this study suggests an integrated approach for assessing behavioral patterns in complex service environments. With the objective that complex service environments should aim to increase its profitability and efficiency while guaranteeing customer satisfaction, quantitative metrics was developed for evaluation. The metrics was defined to use data from behavior observation including path tracking, population counting, and gaze analysis, while previous studies have relied on abstract survey methods that were prone to sampling errors and loss of data. For validation of the metrics in a real world setting, a case study was conducted at 4 train stations in Korea. In the case study, experiments were conducted to gather the required data in all 4 train stations, while their physical layouts were also analyzed. With the results from the case study, comparative evaluation of the 4 train stations in terms of behavioral efficiency was possible, together with a discussion on the effect of their physical settings.

• 한국항공운항학회지
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• 제29권4호
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• pp.37-44
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• 2021

In case of South Korea, the airspace which airlines can operate is extremely limited due to the military operational area located within the Incheon flight information region. As a result, safety problems such as mid-air collision between aircraft or Traffic alert and Collision Avoidance System Resolution Advisory (TCAS RA) may occur with higher probability than in wider airspace. In order to prevent such safety problems, an mid-air collision risk detection model based on Detect-And-Avoid (DAA) well clear metrics is investigated. The model calculates the risk of mid-air collision between aircraft using aircraft trajectory data. In this paper, the practical use of DAA well clear metrics based model has been validated. Aviation safety data such as aviation safety mandatory report and Automatic Dependent Surveillance Broadcast is used to measure the performance of the model. The attributes of individual aircraft track data is analyzed to correct the threshold of each parameter of the model.

• Journal of Information Processing Systems
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• 제14권3호
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• pp.751-770
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• 2018

Web applications are indispensable in the software industry and continuously evolve either meeting a newer criteria and/or including new functionalities. However, despite assuring quality via testing, what hinders a straightforward development is the presence of defects. Several factors contribute to defects and are often minimized at high expense in terms of man-hours. Thus, detection of fault proneness in early phases of software development is important. Therefore, a fault prediction model for identifying fault-prone classes in a web application is highly desired. In this work, we compare 14 machine learning techniques to analyse the relationship between object oriented metrics and fault prediction in web applications. The study is carried out using various releases of Apache Click and Apache Rave datasets. En-route to the predictive analysis, the input basis set for each release is first optimized using filter based correlation feature selection (CFS) method. It is found that the LCOM3, WMC, NPM and DAM metrics are the most significant predictors. The statistical analysis of these metrics also finds good conformity with the CFS evaluation and affirms the role of these metrics in the defect prediction of web applications. The overall predictive ability of different fault prediction models is first ranked using Friedman technique and then statistically compared using Nemenyi post-hoc analysis. The results not only upholds the predictive capability of machine learning models for faulty classes using web applications, but also finds that ensemble algorithms are most appropriate for defect prediction in Apache datasets. Further, we also derive a consensus between the metrics selected by the CFS technique and the statistical analysis of the datasets.