• 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 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 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 Korean Society for Aeronautical & Space Sciences
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• v.46 no.11
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• pp.961-968
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• 2018

Airborne display systems provide pilots with a variety of information needed to operate aircraft. Software faults in the display system can seriously affect the operation of the aircraft, because it can provide inaccurate information to the pilot. Therefore, the software faults are identified and eliminated through ground testing and flight testing. This paper presents an analysis tool called FDR (flight data replay) for flight test of airborne display software. This tool works in real time with the mission computer of aircraft. Also, the tool reproduces the functional error conditions that appear in the display systems by applying flight test data to the display software.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.6
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• pp.113-119
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• 2022

In software fault prediction, a multi classification model that predicts the fault severity category of a module can be much more useful than a binary classification model that simply predicts the presence or absence of faults. A small number of severity-based fault prediction models have been proposed, but no classifier using deep learning techniques has been proposed. In this paper, we construct MLP models with 3 or 5 hidden layers, and they have a structure with a fixed or variable number of hidden layer nodes. As a result of the model evaluation experiment, MLP-based deep learning models shows significantly better performance in both Accuracy and AUC than MLPs, which showed the best performance among models that did not use deep learning. In particular, the model structure with 3 hidden layers, 32 batch size, and 64 nodes shows the best performance.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.49 no.10
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• pp.871-877
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• 2021

Aircraft defects are important matters directly related to the operation of the aircraft and the life of the pilot. The defects in the mission software that occur during aircraft control seriously affect the pilot's mission performance and safety. Therefore, the organization in charge of aircraft development or software defects are reinforced in the process to identify and eliminate defects in the early stages of development, and a lot of labor and time are spent, but due to the nature of the mission software, strong functional coupling with other avionics and high complexity, so there are restrictions on the identification and removal of software defects through the existing test method. This study analyzes the effect of securing mission software integrity and reducing test cost through data integrity verification by developing a tool that automates the verification of expected value of feedback data among communication data of mission computer interlocking equipment.

• 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:Computer Systems and Theory
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• v.28 no.3
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• pp.173-180
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• 2001

고가용도 클러스터 시스템에서 가동되는 인터넷 기반 소프트웨어의 복잡도가 증가됨에 따라 소프트웨어의 설계, 구현, 또는 그 밖의 여러 가지 원인과 관련된 결함으로 인하여 시스템 서비스의 오동작 또는 수행 중단으로 이어지는 사례가 늘어나고 있다. 특히 대량 트랜잭션을 처리하는 인터넷 기반 컴퓨팅 소프트웨어는 빈번한 통신 두절과 데이터 유실로 인하여, 이들이 탑재된 클러스터 시스템의 결함 발생이 더욱 심각할 가능성이 높다. 본 연구는 소프트웨어 재활 결함 허용 기법을 활용하여, 별도의 추가되는 하드웨어 없이도 가용도를 개선할 수 있다는 '소프트웨서 재활 기법을 적용한 다중계 시스템 가용도 분석'에 관한 논문에서 언급된 문제점들에 대한 해결 방안을 제시하였으며, 구체적으로는 1) 주서버의 고장 발생시 여분서버로의 작업전이(switchover) 상태를 클러스터 시스템 모델링에 포함시켰으며, 2) 작업전이 상태와 재활(rejuvenation) 상태에서 머무는 시간을 지수분포 대신에 k-stage Erlangian 분포를 사용하여 확정시간(deterministic time)을 표현할 수 있도록 하였다. 즉 본 논문에서는 고가용도 cold standby 클러스터 시스템의 운영 상태에 대한 상태전이도(state transition diagram)에서, 임의의 상태에서 머무는 시간분포가 memoryless 성질을 만족하지 않아도 되는 semi-Markov 프로세스 문제를 해결하였다.