• The KIPS Transactions:PartD
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• v.10D no.4
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• pp.709-718
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• 2003

This research introduces defect and its causes that happen on software development. Based on defect cause analysis, we understand associated relation between defects and them design defect trigger. So, when we achieve similar project, we can forecast defect and prepare to solve defect by using defect trigger.

• The Transactions of the Korea Information Processing Society
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• v.13 no.5
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• pp.236-242
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• 2024

Software Defect Prediction (SDP) enhances the efficiency of software development by proactively identifying modules likely to contain errors. A major challenge in SDP is improving prediction performance. Recent research has applied deep learning techniques to the field of SDP, with the SAINT model particularly gaining attention for its outstanding performance in analyzing structured data. This study compares the SAINT model with other leading models (XGBoost, Random Forest, CatBoost) and investigates the latest deep learning techniques applicable to SDP. SAINT consistently demonstrated superior performance, proving effective in improving defect prediction accuracy. These findings highlight the potential of the SAINT model to advance defect prediction methodologies in practical software development scenarios, and were achieved through a rigorous methodology including cross-validation, feature scaling, and comparative analysis.

• The KIPS Transactions:PartD
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• v.10D no.4
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• pp.727-734
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• 2003

Recently various quality assurance activities have been applied in software industry for the purpose of software qualify improvement, and the ultimate target of those activities are focused on removing defects from its developed applications. We declared "ZERO DEFECT 21" movement on March 1999 whose purpose is to deliver defect-free applications to the customer. In this paper we would like to introduce the followings $\circled1$ Approaching Methods, $\circled2$ Achievements of "ZERO DEFECT 21". After accomplishing first you of "ZERO DEFECT 21" movement which consist of Audits and Software inspections, we could get the following improvement . $\circled1$ due to conducting the "Audits," we could prevent 22 cases of customer claims, enhance 11.7% of design quality and improve 23.3% of deliverable reusability : $\circled2$ also, due to conducting the "Periodic Sampling inspection and Final inspection," we could enhance 123% of defect rate compared with early stage of development and 247% of defect rate compared with previous yew. Based on the survey results, we could conclude that "ZERO DEFECT 21 " movement provides confidence to project team members and reliability to our customers.ce to project team members and reliability to our customers.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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• pp.687-701
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• 2020

In the production of industrial fabric, it needs automatic real-time system to detect defects on the fabric for assuring the defect-free products flow to the market. At present, many visual-based methods are designed for detecting the fabric defects, but they usually lead to high false alarm. Base on this reason, we propose a Sobel operator combined with patch statistics (SOPS) algorithm for defects detection. First, we describe the defect detection model. mean filter is applied to preprocess the acquired image. Then, Sobel operator (SO) is applied to deal with the defect image, and we can get a coarse binary image. Finally, the binary image can be divided into many patches. For a given patch, a threshold is used to decide whether the patch is defect-free or not. Finally, a new image will be reconstructed, and we did a loop for the reconstructed image to suppress defects noise. Experiments show that the proposed SOPS algorithm is effective.

• 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:Software and Applications
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• v.33 no.7
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• pp.605-614
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• 2006

The success of a software development project can be determined by the use of QCD. And as a software's size and complexity increase, the importance of early quality assurance rises. Therefore, more effort should be given to prevention, as opposed to correction. In order to provide a framework for the prevention of defects, defect detection activities such as peer review and testing, along with analysis of previous defects, is required. This entails a systematization and use of quality data from previous development efforts. FMEA, which is utilized for system safety assurance, can be applied as a means of software defect prevention. SW-FMEA (Software Failure Mode Effect Analysis) attempts to prevent defects by predicting likely defects. Presently, it has been applied to requirement analysis and design. SW-FMEA utilizes measured data from development activities, and can be used for defect prevention on both the development and management sides, for example, in planning, analysis, design, peer reviews, testing, risk management, and so forth. This research discusses about related methodology and proposes defect prevention model based on SW-FMEA. Proposed model is extended SW-FMEA that focuses on system analysis and design. The model not only supports verification and validation effectively, but is useful for reducing defect detection.

• 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.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.1-10
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• 2022

Nowadays software defect prediction (SDP) is most active research going on in software engineering. Early detection of defects lowers the cost of the software and also improves reliability. Machine learning techniques are widely used to create SDP models based on programming measures. The majority of defect prediction models in the literature have problems with class imbalance and high dimensionality. In this paper, we proposed Centroid and Nearest Neighbor based Class Imbalance Reduction (CNNCIR) technique that considers dataset distribution characteristics to generate symmetry between defective and non-defective records in imbalanced datasets. The proposed approach is compared with SMOTE (Synthetic Minority Oversampling Technique). The high-dimensionality problem is addressed using Ant Colony Optimization (ACO) technique by choosing relevant features. We used nine different classifiers to analyze six open-source software defect datasets from the PROMISE repository and seven performance measures are used to evaluate them. The results of the proposed CNNCIR method with ACO based feature selection reveals that it outperforms SMOTE in the majority of cases.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.10
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• pp.297-302
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• 2020

Software design are an important phase in the software developments. In order to manage software design, we propose additional information. Additional information suggests a standard and quantitative methods. In this study, we propose additional information at the design stage for defect management.