• Korean Journal of Agricultural Science
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• v.39 no.4
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• pp.483-489
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• 2012

Preference of stink bugs to various leguminous seeds was evaluated by using VTS (Video Tracking System) in laboratory. Major soybean stink bugs such as bean bug, Riptortus pedestris (Fabricius), one-banded stink bug, Piezodorus hybneri (Gmelin), eastern green stink bug, Nezara antennata (Scott), and sole bug, Dolycoris baccarum (L.) were significantly most attracted to Cheongjakong, a soybean variety, baited fish-net trap, followed by soybean varieties Ilpumgeomjeongkong and Taekwangkong, a peanut variety Daekwangdangkong, a kidney bean variety Gangnangkong, and a adzuki bean variety Jungwonpat, respectively in a soybean field. VTS observation in laboratory showed that R. pedestris and D. baccarum had significantly higher frequency of visit on Cheongjakong, followed by Ilpumgeomjeongkong. However, P. hybneri, N. antennata and Halyomorpha halys (Stal) had significantly higher number of visits on Cheongjakong, Seonnogkong and Jinpumkong, followed by Ilpumgeomjeongkong. The sojourned time of stink bugs, however, was significantly longer on Cheongjakong regardless of species of the bugs. Accordingly, Cheongjakong was evaluated as the most preferred soybean seed by fish-net trap and VTS. Thus, VTS is found to be an effective means to evaluate the food preference of stink bugs.

• Soft Computing and Machine Intelligence
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• v.1 no.1
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• pp.1-10
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• 2021

Analysis and Tracking of bug reports is a challenging field in software repositories mining. It is one of the fundamental ways to explores a large amount of data acquired from defect tracking systems to discover patterns and valuable knowledge about the process of bug triaging. Furthermore, bug data is publically accessible and available of the following systems, such as Bugzilla and JIRA. Moreover, with robust machine learning (ML) techniques, it is quite possible to process and analyze a massive amount of data for extracting underlying patterns, knowledge, and insights. Therefore, it is an interesting area to propose innovative and robust solutions to analyze and track bug reports originating from different open source projects, including Mozilla and Eclipse. This research study presents an ML-based classification model to analyze and track bug defects for enhancing software engineering management (SEM) processes. In this work, Artificial Neural Network (ANN) and Naive Bayesian (NB) classifiers are implemented using open-source bug datasets, such as Mozilla and Eclipse. Furthermore, different evaluation measures are employed to analyze and evaluate the experimental results. Moreover, a comparative analysis is given to compare the experimental results of ANN with NB. The experimental results indicate that the ANN achieved high accuracy compared to the NB. The proposed research study will enhance SEM processes and contribute to the body of knowledge of the data mining field.

• Korean journal of communication and information
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• v.64
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• pp.5-30
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• 2013

This paper conceptualizes the two-faced platform based on interface studies and two-sided market theory to critically analyze today's information technology culture such as the Internet. With this conceptual framework, the Internet can be investigated to have two faces, in which the square of user's openness, sharing, and participation is unfold on the front face, whereas the market of tracking, surveillance, and control of user's activities is formed on the other face. The two faces contradictorily coexist, resulting in technological and cultural dynamics on the Internet. Therefore, the Internet today as a two-faced platform can be described as a square-market interface. By analyzing on transformation of the web architecture, shift of informational commodity from content to data, and web bugs' user tracking across two faces, it examines how the world wide web itself can function as a two-faced platform. Lastly, implications and further works are suggested to improve the two-faced platform as a conceptual framework and to deeply analyze broader information technology culture based on it.

• 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.