• The KIPS Transactions:PartD
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• v.16D no.3
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• pp.407-416
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• 2009

To support an efficient management of software verification and validation activities, much research has been conducted to predict defects in early phase. And defect prediction models have been proposed to predict defects. But the generality of the models has not been experimentally studied for other software system. In other words, most of prediction models were applied only to the same system that had been used to build the prediction models themselves. Therefore, we performed an experiment to explore generality of major prediction models. In the experiment, we applied three defects prediction models to three different systems. As a result, we cannot find their generality of defect prediction capability. The cause is analyzed to result from a different metric distribution between the systems.

• Journal of Information Processing Systems
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• v.14 no.3
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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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.290-291
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• 2021

Defects in the coating process of plastic automotive components are caused by various causes and phenomena. The correlation between defect occurrence rate and meteorological and environmental conditions such as temperature, humidity, and fine dust was analyzed. The defect rate data categorized by type and cause was collected for a year from a automotive parts coating company. This data and its correlation with environmental condition was acquired and experimented by machine learning techniques to predict the defect rate at a certain environmental condition. Correspondingly, the model predicted 98% from fine dust and 90% from curtaining (runs, sags) and hence proved its reliability.

• Journal of Mechanical Science and Technology
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• v.16 no.7
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• pp.889-895
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• 2002

This paper presents the effect of external corrosion, material properties, operation condition and design thickness in pipeline on failure prediction using a failure probability model. The predicted failure assessment for the simulated corrosion defects discovered in corroded pipeline is compared with that determined by ANSI/ASME B31G code and a modified B31G method. The effects of environmental, operational, and random design variables such as defect depth, pipe diameter, defect length, fluid pressure, corrosion rate, material yield stress and pipe thickness on the failure probability are systematically studied using a failure probability model for the corrosion pipeline.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.3
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• pp.151-157
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• 2018

Most previous studies of software fault prediction have focused on supervised learning models for binary classification that determines whether an input module has faults or not. However, binary classification model determines only the presence or absence of faults in the module without considering the complex characteristics of the fault, and supervised model has the limitation that it requires a training data set that most development groups do not have. To solve these two problems, this paper proposes severity-based ternary classification model using unsupervised learning algorithms, and experimental results show that the proposed model has comparable performance to the supervised models.

• 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.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1996.03a
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• pp.24-29
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• 1996

The seniconductor, which is precision product, requires many inspection processes. The surface conditions of the semiconductor chip effect on the functions of the semiconductors. The defects of the chip surface is crack or void. Because general inspection method requires many inspection processes, the inspection system which searches immediately and preciselythe defects of the semiconductor chip surface. We propose the inspection method by using the computer vision system. This study presents an image processing algorithm for inspecting the surface defects(crack, void)of the semiconductor test samples. The proposed image processing algorithm aims to reduce inspection time, and to analyze those experienced operator. This paper regards the chip surface as random texture, and deals with the image modeling of randon texture image for searching the surface defects. For texture modeling, we consider the relation of a pixel and neighborhood pixels as noncasul model and extract the statistical characteristics from the radom texture field by using the 2D AR model(Aut oregressive). This paper regards on image as the output of linear system, and considers the fidelity or intelligibility criteria for measuring the quality of an image or the performance of the processing techinque. This study utilizes the variance of prediction error which is computed by substituting the gary level of pixel of another texture field into the two dimensional AR(autoregressive model)model fitted to the texture field, estimate the parameter us-ing the PAA(parameter adaptation algorithm) and design the defect detection filter. Later, we next try to study the defect detection search algorithm.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.6
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• pp.369-376
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• 2013

Finance Information System is critical national infrastructure. Therefore it is important to select variables of defect causal factor for the system defect management effectively. We research and analyze detected errors in A Company's Finance Information System for three years. In the result of research and analysis, we have selected 9 variables of defect factor: the trading volume, the fluctuation of KOSDAQ index, and the number of public announcements, etc. Then we have assumed that these variables affect real system errors and analyzed correlation between the hypothesis and the detected system errors. After analyzing, we have extracted the trading volume, the number of orders and fills, changing tasks, and the fluctuations of NASDAQ index as valid variables of defect factor. These variables are proposed for failure prediction model as the variables to manage defects in the finance information system afterward.

• Information Systems Review
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• v.14 no.2
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• pp.103-115
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• 2012

Improvement of the capability to cope with risk based on prior preventive management is very important for efficient operation of financial information system. In order to do this, understanding, analysis, and countermeasures for the risk that happened already in the past is essential. In this study, the defect data which happened in the financial information system including account system, business system and data feeding system during 4 years and 5 months were categorized and analyzed by the domain, defect factors, period, day of the week, phases of software development, and defect cause. As a result, it was identified that the defect data had characteristics and trends along the phase of software development, day of the week, and the cause, also that building risk prediction model was necessary for the risk management of whole financial domain due to the relation of the information systems.

• Journal of the Korean Society for Precision Engineering
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• v.28 no.9
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• pp.1078-1085
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• 2011

Prediction of a minimum crack size for growth, which is defined as a crack size that grows fast enough to keep ahead of its removal by contact wear and periodic grinding, is the most demanding work to prevent rail from fatigue failure and develop cost effective railway maintenance strategy In this study, we investigated the wheel load increment due to a rail defect during a train ran over it, and its effect on the minimum crack size for growth. For this purpose, we developed simulation software based on the Fletcher and Kapoor's "2.5D" model and measured wheel load increment during a train passed over a defect. A maximum contact pressure and contact patch size were calculated by 3D FEM and crack growth analyses were performed by varying two of dominant contact contributors; surface friction coefficient(0.1, 0.2, 0.3 and 0.4) and crack aspect ratio. The minimum crack sizes for growth were calculated from 0.29 to 1.44mm depending on the contact conditions. They were decreasing with increasing surface friction coefficient and decreasing with crack aspect ratio(a/b).