• KIPS Transactions on Software and Data Engineering
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• v.3 no.12
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• pp.511-522
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• 2014

During the development of the software, a variety of bugs are reported. Several bug tracking systems, such as, Bugzilla, MantisBT, Trac, JIRA, are used to deal with reported bug information in many open source development projects. Bug reports in bug tracking system would be triaged to manage bugs and determine developer who is responsible for resolving the bug report. As the size of the software is increasingly growing and bug reports tend to be duplicated, bug triage becomes more and more complex and difficult. In this paper, we present an approach to assign bug reports to appropriate developers, which is a main part of bug triage task. At first, words which have been included the resolved bug reports are classified according to each developer. Second, words in newly bug reports are selected. After first and second steps, vectors whose items are the selected words are generated. At the third step, TF-IDF(Term frequency - Inverse document frequency) of the each selected words are computed, which is the weight value of each vector item. Finally, the developers are recommended based on the similarity between the developer's word vector and the vector of new bug report. We conducted an experiment on Eclipse JDT and CDT project to show the applicability of the proposed approach. We also compared the proposed approach with an existing study which is based on machine learning. The experimental results show that the proposed approach is superior to existing method.

• Journal of KIISE
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• v.42 no.6
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• pp.755-763
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• 2015

As fixing bugs is a large part of software development and maintenance, estimating the time to fix bugs -bug fixing time- is extremely useful when planning software projects. Therefore, in this study, we propose a way to estimate bug fixing time using bug reports. First, we classify previous bug reports with meta fields by applying a k-NN method. Next, we compute the similarity of the new bug and previous bugs by using data from bug reports. Finally, we estimate how long it will take to fix the new bug using the time it took to repair similar bugs. In this study, we perform experiments with open source software. The results of these experiments show that our approach effectively estimates the bug fixing time.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.10a
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• pp.512-514
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• 2018

최근 보안 버그의 중요성이 증가함에 따라, 버그 리포트 중 보안과 관련된 리포트를 빠르게 분류하는 기술이 필요하다. 기존 기술들은 버그 리포트의 단어들을 가지고 기계학습을 위한 훈련 데이터를 생성한다. 이 때 기계학습에 잡음이 발생하면 성능을 떨어뜨릴 수 있다. 이를 보완하기 위해 본 연구에서는 감정 단어를 활용하여 잡음을 줄인 보안 버그리포트를 자동으로 식별하는 기계학습기반 기술을 제안한다. 제안 기술은 기계학습 시 사용되는 훈련 데이터의 품질을 높이기 위해 감정 단어를 활용한다. 실험 결과 감정 단어를 활용했을 때 기존 기술 대비 보안 버그를 분류하는 정확도가 3.03% 향상되었다.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.5
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• pp.235-241
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• 2015

Nowadays, research and industry on the internet of things is rapidly developing. Bug fixed field of the Software development related internet of things is a very important things. In this study, we analyze the properties that can affect what the bug fix-time by analyzing the time required to fix a bug associated with the Internet of Things. Using the k-NN classification method based on the attribute information to be classified as bug reports. Extracts a bug report based on the results of a similar property. Bug fixed by calculating the time of a similar bug report predicts the fix-time for new bugs. Depending on the prediction of the properties that affect the bug correction time, the properties of os, component, reporter, and assignee showed the best prediction accuracy.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.11a
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• pp.480-483
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• 2017

소프트웨어 개발 및 유지보수 단계에서 발생한 문제들은 버그 추적 시스템을 통해 버그리포트로 등록되고 관리된다. 등록된 버그리포트를 기반으로 배정자는 해당 문제를 해결할 수 있는 개발자들을 배정하고, 배정된 개발자는 이를 해결한다. 그러나 버그리포트에서 제공되는 정보가 정확하지 않을 경우 문제 해결에 많은 시간이 소모된다. 본 논문에서는 Eclipse 오픈소스 프로젝트들에 대해 12가지의 도메인으로 분류하여 총 395,967개의 버그리포트에 대해 초기 정보의 불완전성을 분석한다. 이를 위해 초기 버그리포트에서 제공되는 정보 중 메타필드 내 정보에 초점을 맞춘다. 분석결과 필드들이 도메인 별로 최소 6%, 평균 20%, 최대 33%가 최소 한 번 이상 변경되는 것을 확인하였으며, 프로젝트 도메인 별로 상이하게 변경되는 것을 확인할 수 있었다.

• Journal of KIISE
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• v.44 no.11
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• pp.1156-1164
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• 2017

Existing studies on automatic bug triage were mostly used the method of designing the prediction system based on the machine learning algorithm. Therefore, it can be said that applying a high-performance machine learning model is the core of the performance of the automatic bug triage system. In the related research, machine learning models that have high performance are mainly used, such as SVM and Naïve Bayes. In this paper, we apply Deep Learning, which has recently shown good performance in the field of machine learning, to automatic bug triage and evaluate its performance. Experimental results show that the Deep Learning based Bug Triage system achieves 48% accuracy in active developer experiments, un improvement of up to 69% over than conventional machine learning techniques.

• Proceedings of the Korean Information Science Society Conference
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• 2004.10b
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• pp.724-726
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• 2004

본 논문에서는 말스버그가 주장한 가보 웨이블릿을 기반으로 하는 얼굴 인식과 표정에 강인한 얼굴 인식에 대한 내용을 소개하였다. 표정을 분류하는 방법론에 대한 연구는 활발한 편이지만, 유사한 표정을 지니는 타인에 대한 구분이라던가, 동일인의 다양한 표정을 한 사람으로 정확히 인식하는 연구는 전무한 실정이다. 본 논문에서는 얼굴을 구성하는 특징 중에서 표정에 가장 많이 영향을 받는 특징을 분석하기 위한 실험과정과 결과, 그리고 근거를 제시하였고, 그에 따르는 방법론에 대한 연구를 제안한다.

• Journal of Software Engineering Society
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• v.28 no.1
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• pp.23-28
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• 2019

Recently, in order to effectively test deep neural network model for image processing application, researches have actively conducted to automatically generate data in corner-case that is not correctly predicted by the model. This paper proposes test data generation method that selects arbitrary words from input of system and transforms them into synonyms in order to test the bug reporter automatic assignment system based on sentence classification deep neural network model. In addition, we compare and evaluate the case of using proposed test data generation and the case of using existing difference-inducing test data generations based on various neuron coverages.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.11
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• pp.273-280
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• 2020

Automatic Static Analysis Tools help developers to quickly find potential defects in source code with less effort. However, the tools reports a large number of false positive warnings which do not have to fix. In our study, we proposed an artificial neural network-based warning classification method using topic models of source code blocks. We collect revisions for fixing bugs from software change management (SCM) system and extract code blocks modified by developers. In deep learning stage, topic distribution values of the code blocks and the binary data that present the warning removal in the blocks are used as input and target data in an simple artificial neural network, respectively. In our experimental results, our warning classification model based on neural network shows very high performance to predict label of warnings such as true or false positive.

• Journal of the Korea Convergence Society
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• v.9 no.10
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• pp.81-91
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• 2018

This study to investigate the effects of proprioceptive motor program on proprioceptive and balance ability for patients with chronic stroke. Twenty nine subjects were recruited by means of a convenience sampling from Gyeonggi-do B hospital. The subjects were divided into two group: a proprioceptive motor program and general physical therapy(n=15), general physical therapy(n=14). The stimulation and exercises were conducted for 30 min per day, three days, per week for six weeks. All participants were assessed before and after taping training using proprioceptive, berg balance scale(BBS), timed up and go test(TUG), and balance ability was measured using a BioRescue. After training, the change values of the proprioception and balance ability in experimental group were significantly greater than control group(p<0.05). This findings show that progressive and proprioceptive motor program training convergence can be effective for stroke patients proprioception and balance ability. Continued development of convergence interventions for chronic stroke patients with proprioception and balance ability in the practice are suggested.