• Journal of KIISE:Software and Applications
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• v.33 no.10
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• pp.836-850
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• 2006

Nowadays, due to the rapidly increasing number of package software products, quality test has been emphasized for package software products. When testing software products, one of the most important factors is to select metrics which form the bases for tests. In this paper, the types of package software are represented as characteristic vectors having probabilistic relationships with metrics. The characteristic vectors could be regarded as indicators of software type. To assign the metrics for each software type, the past test metrics are collected and analyzed. Using Bayesian belief network, the dependency relationship network of the characteristic vectors and metrics is constructed. The dependency relationship network is then used to find the proper metrics for the test of new package software products.

• The KIPS Transactions:PartD
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• v.10D no.4
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• pp.719-726
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• 2003

A SPICE (Software Process Improvement and Capability dEtermination) and CMM (Capability Maturity Model) assessment can be considered as representative software process assessment models since assessors assign ratings to indicators and metrics to measure the capability of software precesses. But ail of process assessment models don't provide a subjective measurement procedure and metrics. In this paper we define basic quality process metrics and propose standard metrics for each process based on SPICE and CMM. This study also proposed a guideline for a subjective measurement procedure and the metrics are effectively use to SPI.

• Journal of Information Processing Systems
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• v.8 no.2
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• pp.241-262
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• 2012

An understanding of quality attributes is relevant for the software organization to deliver high software reliability. An empirical assessment of metrics to predict the quality attributes is essential in order to gain insight about the quality of software in the early phases of software development and to ensure corrective actions. In this paper, we predict a model to estimate fault proneness using Object Oriented CK metrics and QMOOD metrics. We apply one statistical method and six machine learning methods to predict the models. The proposed models are validated using dataset collected from Open Source software. The results are analyzed using Area Under the Curve (AUC) obtained from Receiver Operating Characteristics (ROC) analysis. The results show that the model predicted using the random forest and bagging methods outperformed all the other models. Hence, based on these results it is reasonable to claim that quality models have a significant relevance with Object Oriented metrics and that machine learning methods have a comparable performance with statistical methods.

• Journal of Internet Computing and Services
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• v.7 no.2
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• pp.151-160
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• 2006

Software quality evaluation system and software evaluation criterion have to be defined to evaluate software reliability. In this study, we develop software reliability quality metrics for evaluating of software reliability and research for testing of suitable criteria of software reliability. To accomplish a study successfully, we have to estimate the failure time of software using the number of software bugs. Software reliability is very important in software quality. We propose the estimation of software reliability. We propose the software reliability metrics and the software testing method that the basis of international standard ISO/IEC 9126 in software testing metrics.

• Journal of the Korean Society for Railway
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• v.12 no.4
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• pp.450-456
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• 2009

In accordance with the development of recent computer technology, the dependency of railway signaling system on the computer software is being increased further, and accordingly, the testing for the safety and reliability of railway signaling system software became more important This thesis suggested automated an analysis tool for S/W metrics on this railway signaling system, and presented its result of implementation. The analysis items in the implemented tool had referred to the international standards in relation to the software for railway system, such as IEC61508 and IEC 62279. This automated analysis tool for railway signaling system can be utilized at the assessment stage for railway signaling system software also, and it is anticipated that it can be utilized usefully at the software development stage also.

• Journal of Electrical Engineering and Technology
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• v.9 no.5
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• pp.1739-1745
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• 2014

Prediction of fault-prone modules continues to attract researcher's interest due to its significant impact on software development cost. The most important goal of such techniques is to correctly identify the modules where faults are most likely to present in early phases of software development lifecycle. Various software metrics related to modules level fault data have been successfully used for prediction of fault-prone modules. Goal of this research is to predict the faulty modules at design phase using design metrics of modules and faults related to modules. We have analyzed the effect of pre-processing and different machine learning schemes on eleven projects from NASA Metrics Data Program which offers design metrics and its related faults. Using seven machine learning and four preprocessing techniques we confirmed that models built from design metrics are surprisingly good at fault proneness prediction. The result shows that we should choose Naïve Bayes or Voting feature intervals with discretization for different data sets as they outperformed out of 28 schemes. Naive Bayes and Voting feature intervals has performed AUC > 0.7 on average of eleven projects. Our proposed framework is effective and can predict an acceptable level of fault at design phases.

• The Journal of Society for e-Business Studies
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• v.6 no.2
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• pp.73-85
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• 2001

Usability is defined by the effectiveness, efficiency and satisfaction with which specified users can achieve specified goal in particular environment. Today, many factors such as software acquisition, business re-engineering and the trend of software market have software industry considers usability as an important quality of software products. Therefore, the specification and measurement method for establishing the acceptable usability level of software is required during software development acquisition and evaluation. To evaluate whether software products is usable or not, we need evaluation method for software usability. This paper is not only to provide a framework for specifying the usability attributes of a product and establishing a comprehensive set of usability criteria, but also assessing usability metrics of the software. The usability metrics and checklist is developed based on the properties of the user interface using ISO standard and general usability principles with product-oriented view which means the Product has attributes contributing to determine the usability. This paper also suggested the way to weight the relative importance of the individual usability item depending on the software categories.

• KIISE Transactions on Computing Practices
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• v.24 no.1
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• pp.46-52
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• 2018

Software metrics as a way to evaluate software play a significant role in reducing software development costs and improving quality. However, the emergence of various software metrics creates the problem that the user must select the correct metric. Various strategies have been studied to solve this problem. However, existing studies still have difficulties in selecting metrics by requiring high user interventions. Therefore, in this paper, we propose a method that helps to select the right metric and the metric tools by using their various characteristics as selection criteria, instead of using weighted expressions to minimize user intervention.

• The KIPS Transactions:PartD
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• v.13D no.2 s.105
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• pp.241-250
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• 2006

Software maintenance is an important and very expensive activity in software life cycle. To estimate the maintainability cost of software, many software metrics have been proposed. This paper presents the result of an experimental study to explore the relationship between maintainability and some software metrics. LCOM, RFC, DAC, and LOC are employed as metrics and time really spent for maintenance activity has been collected. In the experimental study, we have found that for some systems, the existing metrics may not be an indicator to maintenance effort, which is not consistent with our general knowledge on the relationship between them. Specifically speaking, we recognized that there should be more empirical study on the relationship between metrics and maintainability of softwares which have been developed using recent technologies such as software architecture and design pattern.

• Journal of KIISE:Software and Applications
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• v.30 no.7_8
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• pp.618-641
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• 2003

Various quality models using quality factor, quality criteria and metrics have been proposed in order to evaluate quality of software products. However, a customized quality model which is specific to the characteristics of software component is required. In this paper, we propose external quality prediction techniques enable us to predict what external quality the final software product will have by using metrics as with internal attributes of software in development. We also propose a model not only for measuring quality by using metrics but also for applying internal attributes of ISO 9126 into artifacts of software component development.