• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.3
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• pp.557-564
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• 2019

As the number and type of software increases, those security vulnerabilities are also increasing. Various types of software may have multiple vulnerabilities and those vulnerabilities as they can cause irrecoverable significant damage must be detected and deleted quickly. Various studies have been carried out to detect the vulnerability of the current software, but it is slow, and prediction accuracy is low. Therefore, in this paper, we describe a method to efficiently predict software vulnerability by using neural network algorithm and compare prediction accuracy with conventional system using machine learning algorithm. As a result of the experiment, the prediction system proposed in this paper showed the highest prediction rate.

• Journal of the Korean Society for Railway
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• v.9 no.5 s.36
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• pp.601-605
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• 2006

Reliability of the Railway signaling system which is safety critical is determined by reliability of hardware and software. Reliability of hardware is easily predicted and demonstrated through lots of different studies and environmental tests, while that of software is estimated by the iterative test outcomes so estimates of reliability will depend on the inputs. Combinations of inputs to and outputs from the software may be mostly combinatoric and therefore all the combinations could not be tested. As a result, it has been more important to calculate reliability by means of a simpler method. This paper identifies the reliability prediction equation applicable to reliability prediction for railway signaling system software, and performs the simulation of onboard equipment of automatic train control for high speed train to review reliability prediction and validity.

• Journal of Korea Multimedia Society
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• v.21 no.2
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• pp.271-280
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• 2018

Recently, big data has been growing rapidly due to the development of IT technology. Especially in the medical field, big data is utilized to provide services such as patient-customized medical care, disease management and disease prediction. In Korea, 'National Health Alarm Service' is provided by National Health Insurance Corporation. However, the prediction model has a problem of short-term prediction within 3 days and unreliability of social data used in prediction model. In order to solve these problems, this paper proposes a disease prediction model using medicine prescription data generated from actual patients. This model predicts the total number of patients and the risk of disease in each region and uses the ARIMA model for long-term predictions.

• 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.

• Advances in Computational Design
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• v.2 no.2
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• pp.121-131
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• 2017

Collapse was one of the typical common geological hazards during the construction of tunnels. The risk assessment of collapse was an effective way to ensure the safety of tunnels. We established a prediction model of collapse based on Bayesian Network. 76 large or medium collapses in China were analyzed. The variable set and range of the model were determined according to the statistics. A collapse prediction software was developed and its veracity was also evaluated. At last the software was used to predict tunnel collapses. It effectively evaded the disaster. Establishing the platform can be subsequent perfect. The platform can also be applied to the risk assessment of other tunnel engineering.

• Journal of KIISE:Software and Applications
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• v.27 no.4
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• pp.393-401
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• 2000

This paper explores neuro-fuzzy system in order to improve the software reliability predictability from failure data. We perform numerical simulations for actual 10 failure count and 4 failure time data sets from different software projects with the various number of rules. Comparative results for next-step prediction problem is presented to show the prediction ability of the neuro-fuzzy system. Experimental results show that neuro-fuzzy system is adapt well across different software projects. Also, performance of neuro-fuzzy system is favorably with the other well-known neural networks and statistical SRGMs.

• The Transactions of the Korea Information Processing Society
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• v.7 no.5
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• pp.1419-1425
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• 2000

When we predict of software reliability, we can't know the testing stopping time and how many faults be residues in software the (the maximum value of data) during these software testing process, therefore we assume the maximum value and the training result can be inaccuracy. In this paper, we present neural network approach for software reliability prediction with unnormalized (actual or original collected) data. This approach is not consider the maximum value of data and possible use the network without normalizing but the predictive accuracy is better. Also, the unnormalized method shows better predictive accuracy than the normalized method given by maximum value. Therefore, we can make the best use of this model in software reliability prediction using unnormalized data.

• Journal of Information Processing Systems
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• v.13 no.4
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• pp.778-804
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• 2017

Software today has become an inseparable part of our life. In order to achieve the ever demanding needs of customers, it has to rapidly evolve and include a number of changes. In this paper, our aim is to study the relationship of object oriented metrics with change proneness attribute of a class. Prediction models based on this study can help us in identifying change prone classes of a software. We can then focus our efforts on these change prone classes during testing to yield a better quality software. Previously, researchers have used statistical methods for predicting change prone classes. But machine learning methods are rarely used for identification of change prone classes. In our study, we evaluate and compare the performances of ten machine learning methods with the statistical method. This evaluation is based on two open source software systems developed in Java language. We also validated the developed prediction models using other software data set in the same domain (3D modelling). The performance of the predicted models was evaluated using receiver operating characteristic analysis. The results indicate that the machine learning methods are at par with the statistical method for prediction of change prone classes. Another analysis showed that the models constructed for a software can also be used to predict change prone nature of classes of another software in the same domain. This study would help developers in performing effective regression testing at low cost and effort. It will also help the developers to design an effective model that results in less change prone classes, hence better maintenance.

• Journal of Applied Reliability
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• v.17 no.4
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• pp.296-304
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• 2017

Purpose: Recent weapon systems in defense have increased the complexity and importance of software when developing multifunctional equipment. In this study, we analyze the accuracy of the proposed software reliability model when applied to weapon systems. Methods: Determine the similarity between software reliability analysis results (prediction/estimation) utilizing data from developing weapon systems and system failures data during operation of weapon systems. Results: In case of a software reliability prediction model, the predicted failure rate was higher than the actual failure rate, and the estimation model was consistent with actual failure history data. Conclusion: The software prediction model needs to adjust the variables that are appropriate for the domestic weapon system environment. As the reliability of software is increasingly important in the defense industry, continuous efforts are needed to ensure accurate reliability analysis in the development of weapon systems.

• The Journal of the Korea Contents Association
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• v.10 no.6
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• pp.134-143
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• 2010

This paper proposes a framework for classifying metric-based software quality prediction models, especially case of software criticality, into four types. Models are classified along two vectors: input metric forms and the necessity of past project data. Each type has its own characteristics and its strength and weakness are compared with those of other types using newly defined criteria. Through this qualitative evaluation each organization can choose a proper model to suit its environment. My earlier studies of criticality prediction model implemented specific models in each type and evaluated their prediction performances. In this paper I analyze the experimental results and show that the characteristics of a model type is the another key of successful model selection.