• Journal of the Korea Society of Computer and Information
• /
• v.23 no.9
• /
• pp.81-86
• /
• 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 Academia-Industrial cooperation Society
• /
• v.16 no.1
• /
• pp.691-696
• /
• 2015

The interest in software defects is growing. Companies are working on reducing them by various ways with a software development part, customer service part and quality management part. The defect data collected from them will be analyzed according to the condition and purpose to minimize the defect, and it will contribute to improving the quality of products. This study analyzed the software defect progress for companies based on the test maturity model, and set up the defect preventing process. They were applied to the project, and the number of defects decreased from 106 to 16. This study focused on the existing defect, which is a basic requirement for software quality management, and have importance by presenting a way to improve the software product quality with minimum resources.

• The KIPS Transactions:PartD
• /
• v.11D no.4
• /
• pp.873-884
• /
• 2004

This research provides the solution of defect problem and detection of defect and its causes that happen on software development. For developing a reliable software, a key factor is to find and manage defects that are during software development. Based on defect items analysis, we understand associated relation between defects and design defect opportunity tree. Developing the similar project, we can estimate defect and prepare to solve defect by using defect management opportunity tree.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.11
• /
• pp.4028-4042
• /
• 2021

Aiming at the problem of software defect prediction difficulty caused by insufficient software defect marker samples and unbalanced classification, a semi-supervised software defect prediction model based on a tri-training algorithm was proposed by combining feature normalization, over-sampling technology, and a Tri-training algorithm. First, the feature normalization method is used to smooth the feature data to eliminate the influence of too large or too small feature values on the model's classification performance. Secondly, the oversampling method is used to expand and sample the data, which solves the unbalanced classification of labelled samples. Finally, the Tri-training algorithm performs machine learning on the training samples and establishes a defect prediction model. The novelty of this model is that it can effectively combine feature normalization, oversampling techniques, and the Tri-training algorithm to solve both the under-labelled sample and class imbalance problems. Simulation experiments using the NASA software defect prediction dataset show that the proposed method outperforms four existing supervised and semi-supervised learning in terms of Precision, Recall, and F-Measure values.

• Journal of KIISE
• /
• v.42 no.11
• /
• pp.1349-1360
• /
• 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 KIISE
• /
• v.45 no.3
• /
• pp.242-250
• /
• 2018

For high-quality software development, it is necessary to detect and fix the defects inserted. If defect management activities are not properly performed, it will lead to the project delay and project failure due to rework. Therefore, organizations need to establish defect management process and institutionalize it. Process standard models handle defect management in the area of project monitoring and control. However, small organizations experience difficulties in implementing and applying defect management process in a real situation. In this paper, we propose a defect management process for small organization which is designed in accordance with the characteristics of a small projects such as few participants and short development period. The proposed defect management process will be based on a tool chain with open source software such as Redmine, Subversion, Maven, Jenkins that support a defect management process and SW Visualization in systematic way. We also proposed a way of constructing defect database and various methods of analyzing and controlling defect data based on it. In an effort to prove the effectiveness of the proposed process, we applied the process and tool chain to a small organization.

• The KIPS Transactions:PartD
• /
• v.15D no.3
• /
• pp.313-320
• /
• 2008

A typical software development life cycle consists of a series of phases, each of which has some ability to insert and detect defects. To achieve desired quality, we should progress the defect removal with the all phases of the software development. The well-known model of phase-based defect profile is Gaffney model. This model assumes that the defect removal profile follows Rayleigh curve and uses the parameters as the phase index number. However, these is a problem that the location parameter cannot present the peak point of removed defects when you apply Gaffney model to the actual situation. Therefore, Gaffney model failed to represent the actual defect profile. This paper suggests two different models: One is modified Gaffney model that introduce the parameter of Putnam's SLIM model to replace of the location parameter, the other is the growth function model because the cumulative defect profile shows S-shaped. Suggested model is analyzed and verified by the defect profile sets that are obtained from 5 different software projects. We could see from the experiment, the suggested model performed better result than Gaffney model.

• Nuclear Engineering and Technology
• /
• v.52 no.7
• /
• pp.1597-1601
• /
• 2020

Background: Non-destructive evaluation of defects in metals or composites specimens is a regular method in radiographic imaging. The maintenance examination of metallic structures is a relatively difficult effort that requires robust techniques for use in industrial environments. Methods: In this research, iron plate, lead marker and tungsten defect with a 0.1 cm radius in spherical shape were separately simulated by MCNP code and SuperMC software. By ¹⁹²Ir radiation source, two exposures were considered to determine the depth of the actual defined defect in the software. Also by the code, displacement shift of the defect were computed derived from changing the source location along the x- or y-axis. Results: The computed defect depth was identified 0.71 cm in comparison to the actual one with accuracy of 13%. Meanwhile, the defect position was recognized by disorder and reduction in obtained gamma flux. The flux amount along the x-axis was approximately 0.5E+11 units greater than the y-axis. Conclusion: This study provides a method for detecting the depth and position of the defect in a particular sample by combining code and software simulators.

• 한국IT서비스학회:학술대회논문집
• /
• 2003.11a
• /
• pp.713-721
• /
• 2003

The time to market and productivity of embedded system needs a quality measurement process management of embedded software. But, defect management without preemptive analysis or prediction is not useful for quality measurement process management. This subject is focused on the defect that is one of the most important attributes of software measure in the process. Defining of defect attribute and quality measurement process management is according to understanding of embedded sw characteristics and defect data. So, this study contributes to propose the good method of the quantitative based on defect management in the test phase of sw lifecycle.

• The KIPS Transactions:PartD
• /
• v.15D no.3
• /
• pp.321-326
• /
• 2008

The software defects that are not found in the course of a project frequently appear during the conduct of the maintenance procedure after the complete development of the software. As the frequency of surfacing of defects during the maintenance procedure increases, the cost likewise increases, and the quality and customer reliability decreases. The defect rate will go down only if cause analysis and process improvement are constantly performed. This study embodies the defect tracking system (DTS) by considering the Pareto principle: that most defects are repetitions of defects that have previously occurred. Based on the records of previously occurring defects found during the conduct of a maintenance procedure, DTS tracks the causes of the software defects and provides the developer, operator, and maintenance engineer with the basic data for the improvement of the software concerned so that the defect will no longer be manifested or repeated. The basic function of DTS is to analyze the defect type, provide the measurement index for it, and aggregate the program defect type. Doing these will pave the way for the full correction of all the defects of a software as it will enable the defect correction team to check the measured defect type. When DTS was applied in the software configuration management system of the W company, around 65% of all its software defects were corrected.