• Asia pacific journal of information systems
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• v.30 no.3
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• pp.457-496
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• 2020

This study aims to extensively analyze the performance of various Machine Learning (ML) techniques for predicting version to version change-proneness of source code Java files. 17 object-oriented metrics have been utilized in this work for predicting change-prone files using 31 ML techniques and the framework proposed has been implemented on various consecutive releases of two Java-based software projects available as plug-ins. 10-fold and inter-release validation methods have been employed to validate the models and statistical tests provide supplementary information regarding the reliability and significance of the results. The results of experiments conducted in this article indicate that the ML techniques perform differently under the different validation settings. The results also confirm the proficiency of the selected ML techniques in lieu of developing change-proneness prediction models which could aid the software engineers in the initial stages of software development for classifying change-prone Java files of a software, in turn aiding in the trend estimation of change-proneness over future versions.

• Communications for Statistical Applications and Methods
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• v.17 no.6
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• pp.909-916
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• 2010

A trend in software reliability engineering is to take into account the coverage growth behavior during testing. A coverage growth function that represents the coverage growth behavior is an essential factor in software reliability models. When multiple competitive coverage growth functions are available, there is a need for a criterion to select the best coverage growth functions. This paper proposes a selection criterion based on the prediction error. The conditional coverage growth function is introduced for predicting future coverage growth. Then the sum of the squares of the prediction error is defined and used for selecting the best coverage growth function.

• IE interfaces
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• v.21 no.1
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• pp.109-119
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• 2008

Reliability of embedded softwares, as one of factors which affect system reliability, is the probability of failure-free software operation for a specified period of time in a specified environment. and Embedded software is different from general package software because hardware and operating system are tightly coupled to each other. Reliability evaluation models for embedded softwares currently used do not separate estimation and prediction models clearly, and even a standard model has not been proposed yet. In this respect, we choose a reliability estimation model suitable for embedded softwares among software evaluation models being used, and modified the model so as to accomodate recent software environments. In addtion, based on the model, the web-based reliability prediction tool RPX is developed. Finally, an embedded software is analyzed using the tool.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.5
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• pp.1356-1376
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• 2023

Software effort estimation is one of the most difficult tasks in software development whereas predictability is also of equal importance for strategic management. Accurate prediction of the actual cost that will be incurred in software development can be very beneficial for the strategic management. This study discusses the latest trends in software estimation focusing on analogy-based techniques to show how they have improved the accuracy for software effort estimation. It applies the standard deviation technique to the expected value of analogy-based estimates to improve accuracy. In more than 60 percent cases the applied technique of this study helped in improving the accuracy of software estimation by reducing the Magnitude of Relative Error (MRE). The technique is simple and it calculates the expected value of cost or time and then uses different confidence levels which help in making more accurate commitments to the customers.

• ETRI Journal
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• v.28 no.6
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• pp.721-731
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• 2006

A fast intra skip detection algorithm based on the ratedistortion (RD) cost for an inter frame (P-slices) is proposed for H.264/AVC video encoding. In the H.264/AVC coding standard, a robust rate-distortion optimization technique is used to select the best coding mode and reference frame for each macroblock (MB). There are three types of intra predictions according to profiles. These are $16{\times}16$ and $4{\times}4$ intra predictions for luminance and an $8{\times}8$ intra prediction for chroma. For the high profile, an $8{\times}8$ intra prediction has been added for luminance. The $4{\times}4$ prediction mode has 9 prediction directions with 4 directions for $16{\times}16$ and $8{\times}8$ luma, and $8{\times}8$ chrominance. In addition to the inter mode search procedure, an intra mode search causes a significant increase in the complexity and computational load for an inter frame. To reduce the computational load of the intra mode search at the inter frame, the RD costs of the neighborhood MBs for the current MB are used and we propose an adaptive thresholding scheme for the intra skip extraction. We verified the performance of the proposed scheme through comparative analysis of experimental results using joint model reference software. The overall encoding time was reduced up to 32% for the IPPP sequence type and 35% for the IBBPBBP sequence type.

• ETRI Journal
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• v.28 no.6
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• pp.807-810
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• 2006

This letter presents a prediction model for sentence-final intonations for Korean conversational-style text-to-speech systems in which we introduce the linguistic feature of 'modality' as a new parameter. Based on their function and meaning, we classify tonal forms in speech data into tone types meaningful for speech synthesis and use the result of this classification to build our prediction model using a tree structured classification algorithm. In order to show that modality is more effective for the prediction model than features such as sentence type or speech act, an experiment is performed on a test set of 970 utterances with a training set of 3,883 utterances. The results show that modality makes a higher contribution to the determination of sentence-final intonation than sentence type or speech act, and that prediction accuracy improves up to 25% when the feature of modality is introduced.

• Journal of Broadcast Engineering
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• v.20 no.1
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• pp.171-182
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• 2015

HEVC can increase the coding efficiency significantly compared with H.264/AVC however it requires much larger computational complexities in both encoder and decoder. In this paper, the decision process of inter-prediction modes in the HEVC reference software has been studied and a fast algorithm to reduce the computational complexity of encoder and decoder is introduced. The proposed scheme introduces a early decision criteria using the outputs of uni-directional predictions to skip the bi-directional prediction estimation. From the experimental results, it was proven that the proposed method can reduce the encoding complexity by 12.0%, 14.6% and 17.2% with 0.6%, 1.0% and 1.5% of coding efficiency penalty, respectively. In addition, the ratio of bi-directional prediction mode was reduced by 6.3%, 11.8% and 16.6% at the same level of coding efficiency penalty, respectively, which should lead to the decoder complexity reduction. Finally, the effects of the proposed scheme are maintained regardless of the use of the early skip decision algorithm which is implemented in the HEVC reference software.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2004.05a
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• pp.1064-1067
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• 2004

The aim of this paper is to develop a GUI based software that can predict the flow and noise generated by fan. This user-friendly software is designed for the usual fan user in the various industrial companies as well as researcher related to rotating blade:;. Software consists of 3-modules; (1) concept design and performance prediction module using simple and fast methods, (2) preliminary design and flow/noise prediction module using free-wake potential solver and acoustic analogy and (3) detail design module using accurate CFD-software and acoustic formula. Some validations and applications in various fields are described.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.9
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• pp.339-348
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• 2021

In recent software defect prediction research, defect prediction between cross projects and cross-version projects are actively studied. Cross-version defect prediction studies assume WP(Within-Project) so far. However, in the CV(Cross-Version) environment, the previous work does not consider the distribution difference between project versions is important. In this study, we propose an automated Bayesian optimization framework that considers distribution differences between different versions. Through this, it automatically selects whether to perform transfer learning according to the difference in distribution. This framework is a technique that optimizes the distribution difference between versions, transfer learning, and hyper-parameters of the classifier. We confirmed that the method of automatically selecting whether to perform transfer learning based on the distribution difference is effective through experiments. Moreover, we can see that using our optimization framework is effective in improving performance and, as a result, can reduce software inspection effort. This is expected to support practical quality assurance activities for new version projects in a cross-version project environment.