• 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.

• Communications of the Korean Institute of Information Scientists and Engineers
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• v.26 no.7
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• pp.44-51
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• 2008

• Science & Technology Policy
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• v.25 no.6
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• pp.10-11
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• 2015

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.6
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• pp.722-726
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• 2010

The recent deployment of RIA (Rich Internet Application) is often involved with the complex JavaScript code, which leads to the announcement of high performance JavaScript engines for its efficient execution. And the Sunspider benchmark is being widely used for the performance evaluation of these JavaScript engines. In this paper, we compare the execution methods of three high-performance JavaScript engines, Mozilla TraceMonkey, Google V8, and Apple SquirrelFish Extreme, and measure their performances using the SunSpider benchmark. We also evaluate the pros and cons of each engine, based on its execution method and the code characteristics of the SunSpider benchmarks.

• Journal of the Korea Society of Computer and Information
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• v.19 no.9
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• pp.103-115
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• 2014

In this paper, we propose UniBAS(Unified Bug Analysis System) to provide a unified repository model by integrating the extracted data from the heterogeneous bug tracking systems. The UniBAS reduces the cost and complexity of the MSR(Mining Software Repositories) research process and enables the researchers to focus on their logics rather than the tedious and repeated works such as extracting repositories, processing data and building analysis models. Additionally, the system not only extracts the data but also automatically generates database tables, views and stored procedures which are required for the researchers to perform query-based analysis easily. It can also generate various types of exported files for utilizing external analysis tools or managing research data. A case study of detecting duplicate bug reports from the Firfox project of the Mozilla site has been performed based on the UniBAS in order to evaluate the usefulness of the system. The results of the experiments with various algorithms of natural language processing and flexible querying to the automatically extracted data also showed the effectiveness of the proposed system.

• Journal of Digital Contents Society
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• v.9 no.1
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• pp.7-15
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• 2008

The paper proposes information system for providing proper well known delicious restaurants as interactions with users. The system calls 'Moloke', which is an agent for recommending sensitive information on mobile environment The proposing agent differs from existing ones that guide the telephone number and the name of the restaurant. The differences are as following goals. First, the agent gets existing from users as interactive communications on mobile devices through the proper requests on each time zone such as morning, afternoon, and evening. Second, the agent also can recommend a specific restaurant for current personal states such as parties, special community meetings, bio-rhythms and so on. Among them, specially the bio-rhythm is used for recommending proper restaurants to each user. In addition to through proposal suitable design for the mobile agent design more effectively. The research used mobile environment for recommendation service and web environment for data management. Server environment for service used Apache, PHP4, Mysql and mobile page was implemented m-html for approach. Mobile Service was optimized Mozilla-1.22, KUN-1.2.3 Browser

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.5
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• pp.1167-1174
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• 2015

As the market share of smartphone exponentially increases in mobile market, a number of manufacturers have developed their own operating system. Firefox OS is an open source operating system for the smartphone and tablet which is being developed by the Mozilla Foundation. This OS is designed using JavaScript and operated based on HTML5. Even though the number of manufacturers which release the Firefox OS smartphone is consistently increasing, However it is difficult to analyze artifacts in a smartphone in terms of investigation since existing researches on Firefox OS focused on imaging velocity according to abstract forensic process and block size. In this paper, we propose how to collect data in Firefox OS while minimizing data loss and forensic analysis framework based on analysis results on system and user data leaving in a smartphone.

• KIISE Transactions on Computing Practices
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• v.21 no.9
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• pp.616-621
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• 2015

Recently developed software systems have many components, and their complexity is thus increasing. Last year, about 375 bug reports in one day were reported to a software repository in Eclipse and Mozilla open source projects. With so many bug reports submitted, developers' time and efforts have increased unnecessarily. Since the bug severity is manually determined by quality assurance, project manager or other developers in the general bug fixing process, it is biased to them. They might also make a mistake on the manual decision because of the large number of bug reports. Therefore, in this study, we propose an approach of bug severity prediction to solve these problems. First, we find similar topics within a new bug report and reduce the candidate reports of the topic by using the meta field of the bug report. Next, we train the reduced reports by applying Naive Bayes Multinomial. Finally, we predict the severity of the new bug report. We compare our approach with other prediction algorithms by using bug reports in open source projects. The results show that our approach better predicts bug severity than other algorithms.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.5
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• pp.731-742
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• 2022

PIPO is the latest domestic lightweight block cipher announced in ICISC'20, which is characterized by being lightweight to facilitate implementation on IoT with limited resources. In this paper, PIPO 64/128-bit and 64/256-bit were implemented using web-based languages such as Javascript and WebAsembly. Two methods of performance evaluation were conducted by implementing bitsice and TLU, and the performance was compared by implementing Looped written using for statements and Unrolled written for statements. It performs performance evaluations in various web browsers such as Google Chrome, Mozilla Firefox, Opera, and Microsoft Edge, as well as OS-specific environments such as Windows, Linux, Mac, iOS, and Android. In addition, a performance comparison was performed with PIPO implemented in C language. This can be used as an indicator for applying PIPO block cipher on the web.

• Journal of information and communication convergence engineering
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• v.12 no.4
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• pp.252-256
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• 2014

Traditional block cipher Advanced Encryption Standard (AES) is widely used in the field of network security, but it has high overhead on each operation. In the 15th international workshop on information security applications, a novel lightweight and low-power encryption algorithm named low-power encryption algorithm (LEA) was released. This algorithm has certain useful features for hardware and software implementations, that is, simple addition, rotation, exclusive-or (ARX) operations, non-Substitute-BOX architecture, and 32-bit word size. In this study, we further improve the LEA encryptions for cloud computing. The Web-based implementations include JavaScript and assembly codes. Unlike normal implementation, JavaScript does not support unsigned integer and rotation operations; therefore, we present several techniques for resolving this issue. Furthermore, the proposed method yields a speed-optimized result and shows high performance enhancements. Each implementation is tested using various Web browsers, such as Google Chrome, Internet Explorer, and Mozilla Firefox, and on various devices including personal computers and mobile devices. These results extend the use of LEA encryption to any circumstance.