• Journal of the Korea Society of Computer and Information
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• v.23 no.9
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• pp.81-86
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• 2018

Software defect severity is very important in projects with limited historical data or new projects. But general software defect prediction is very difficult to collect the label information of the training set and cross-project defect prediction must have a lot of data. In this paper, an unclassified data set with defect severity is clustered according to the distribution ratio. And defect severity-based prediction model is proposed by way of labeling. Proposed model is applied CLAMI in JM1, PC4 with the least ambiguity of defect severity-based NASA dataset. And it is evaluated the value of ACC compared to original data. In this study experiment result, proposed model is improved JM1 0.15 (15%), PC4 0.12(12%) than existing defect severity-based prediction models.

• 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.

• KIISE Transactions on Computing Practices
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• v.22 no.12
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• pp.681-686
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• 2016

Software defect prediction is an important factor in efficient project management and success. The severity of the defect usually determines the degree to which the project is affected. However, existing studies focus only on the presence or absence of a defect and not the severity of defect. In this study, we proposed an ensemble model using FCM based on defect severity. The severity of the defect of NASA data set's PC4 was reclassified. To select the input column that affected the severity of the defect, we extracted the important defect factor of the data set using Random Forest (RF). We evaluated the performance of the model by changing the parameters in the 10-fold cross-validation. The evaluation results were as follows. First, defect severities were reclassified from 58, 40, 80 to 30, 20, 128. Second, BRANCH_COUNT was an important input column for the degree of severity in terms of accuracy and node impurities. Third, smaller tree number led to more variables for good performance.

• Journal of Software Assessment and Valuation
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• v.15 no.1
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• pp.79-86
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• 2019

Software dimension reduction identifies the commonality of elements and extracts important feature elements. So it reduces complexity by simplify and solves multi-collinearity problems. And it reduces redundancy by performing redundancy and noise detection. In this study, we proposed defect severity-based dimension reduction model. Proposed model is applied defect severity-based NASA dataset. And it is verified the number of dimensions in the column that affect the severity of the defect. Then it is compares and analyzes the dimensions of the data before and after reduction. In this study experiment result, the number of dimensions of PC4's dataset is 2 to 3. It was possible to reduce the dimension.

• Journal of KIISE
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• v.42 no.11
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• pp.1349-1360
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• 2015

In order to improve software quality, it is necessary to efficiently and effectively remove software defects in source codes. In the development field, defects are removed according to removal ratio or severity of defects. There are several studies on the removal of defects based on software quality attributes, and several other studies have been done to improve the software quality using classification of the severity of defects, when working on projects. These studies have thus far been insufficient in terms of identifying if there exists relationships between defects or whether any type of defect is more important than others. Therefore, in this study, we collected various types of software defects, standards organization, companies, and researchers. We modeled the defects types using an ANP model, and developed the weighted severities of the defects types, with respect to the general application software, using the ANP model. When general application software is developed, we will be able to use the weight for each severity of defect type, and we expect to be able to remove defects efficiently and effectively.

• Journal of the Korea Society of Computer and Information
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• v.20 no.5
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• pp.73-81
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• 2015

Most of the software fault prediction studies focused on the binary classification model that predicts whether an input entity has faults or not. However the ability to predict entity fault-proneness in various severity categories is more useful because not all faults have the same severity. In this paper, we propose fault prediction models at different severity levels of faults using traditional size and complexity metrics. They are ternary classification models and use four machine learning algorithms for their training. Empirical analysis is performed using two NASA public data sets and a performance measure, accuracy. The evaluation results show that backpropagation neural network model outperforms other models on both data sets, with about 81% and 88% in terms of accuracy score respectively.

• Journal of Industrial Technology
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• v.32 no.A
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• pp.125-128
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• 2012

In this paper, we proposed a prioritization method of test cases using a value estimation model of test sets, that are key elements for highly effective software testings as well as involve a large cost factor in software developments and maintenances. Based on previous studies, our idea includes introducing some practical factors of the test case prioritization which critically influence the value of a test case: Relative values of test sets before and after the test running, Average value of these two relative values, Severity of the defect, Risks that are covered, Frequency of use, Change related values, Systematic elicitations, etc. Finally we discussed the usefulness and the expected effects of the proposed scheme.

• 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.

• Archives of Plastic Surgery
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• v.49 no.2
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• pp.184-194
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• 2022

The face and the external nose define an individual's physical appearance. Nasal deformities can cause facial disfigurement along with unwanted psychological repercussions. Nasal deformities range in severity, with the most severe cases being indications for a rhinectomy, due to the complexity of the nasal defect. According to published literature, there is no consensus among otolaryngologists and plastic surgeons on which technique or flap use is preferred in terms of complications, aesthetic outcome, or patient satisfaction. The goal of this study is to provide a comprehensive analysis of published studies on nasal reconstruction following rhinectomy. Using the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols guidelines for writing systematic reviews, a systematic review was conducted. Four databases were searched using a search strategy. These articles were then imported into the COVIDENCE software and went screening and thorough article review. After screening 2,237 articles, 23 studies were then extracted for data collection analysis. We collected data from 12 case series, 4 case studies, 1 prospective case series, and 4 retrospective chart review studies. The most commonly reported flaps were forehead flaps, superior extended nasal myocutaneous island, forearm free flaps, anterolateral thigh (ALT) free flap, medial femoral condyle free flap (n = 8), and zygomaticus implants (n = 6), and retained nasal prosthesis. Although not specifically indicated by a certain number, the most common indication for the rhinectomy was malignancy, followed by traumas, postsurgical complications, radionecrosis, and congenital nasal malformations.

• Journal of the Society of Cosmetic Scientists of Korea
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• v.45 no.1
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• pp.95-103
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• 2019

Lips have a defect in maintenance of moisture due to their thin layer. As aging progresses, lips lose volume and redness, and become wrinkled. Fat grafting and filler surgery have been used to achieve attractive lips, but little research has been reported to develop better materials to replace the present methods. Recently, a study suggests that the increase of adipocyte number can be enhancing the expansion endogenous fat. In previous study, we identified that the efficacy of Magnolia officinalis bark extract (MOBE) was effective on the induction of adipogenic differentiation. In this study, we confirmed that MOBE enhanced the differentiation of human adipose-derived stem cells on the fat mimic 3D structure built by 3D bioprinting method From further experiments in human, we established a method to quantify the severity of lip wrinkle by measurement of standard deviation of gray value using Image J software. Finally, we found that topical treatment with 1% MOBE formulated lip balm significantly improved the lip wrinkle after using for 12 weeks. In conclusion, these findings suggest that MOBE has great potential, as a cosmetic ingredient, to reduce the lip wrinkle through the effect of promoting adipogenic differentiation.