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

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.3
• /
• pp.1583-1598
• /
• 2019

Recently, software has increased in complexity and been applied in various industrial fields. As a result, the presence of software bugs cannot be avoided. Various bug severity prediction methodologies have been proposed, but their performance needs to be further improved. In this study, we propose a novel technique for bug severity prediction in cross projects such as Eclipse, Mozilla, WireShark, and Xamarin by using topic modeling and similarity (i.e., KL-divergence). First, we construct topic models from bug repositories in cross projects using Latent Dirichlet Allocation (LDA). Then, we find topics in each project that contain the most numerous similar bug reports by using a new bug report. Next, we extract the bug reports belonging to the selected topics and input them to a Naïve Bayes Multinomial (NBM) algorithm. Finally, we predict the bug severity in the new bug report. In order to evaluate the performance of our approach and to verify the difference between cross projects and single project, we compare it with the Naïve Bayes Multinomial approach; the Lamkanfi methodology, which is a well-known bug severity prediction approach; and an emotional similarity-based bug severity prediction approach. Our approach exhibits a better performance than the compared methods.

• ETRI Journal
• /
• v.42 no.2
• /
• pp.196-204
• /
• 2020

Open-source software development projects are well suited for exploring new ideas and acquiring knowledge from developers outside of the project. In this paper, we examine the impact of external developers on innovation in open-source software development from the perspective of organizational learning theory. We examine the roles of external and internal developers, who "explore" and "exploit," respectively, on the innovation performance of 17 691 open-source software development projects whose data is stored in the GitHub platform. The results indicate that a multifaceted strategy, in which the exploitation successfully supports the exploration, is most effective for their success. The results also indicate that the role of exploration decreases after the release of the software.

• Korean Journal of Construction Engineering and Management
• /
• v.14 no.1
• /
• pp.82-90
• /
• 2013

In the construction industry, there are so many qualitative factors affecting the performance of a project. So it is crucial to measure the factors in an effective way in order to analyze the interrelationship among the various factors. To improve the performance level of a project, it is also important to identify the most appropriate management practices which are inter-linked with the subject project. The purpose of this study is to develop a project performance management system (PPMS) to quantitatively analyze the variety of project performance data and identify the best management practice to increase the potential level of a particular performance area. Using a comparative statistical method, this study developed a quantification method and web-based computerized system to enhance the usage of the system. The system, however, is still under the validation stage because of the shortage of data set. In the future, when more and more completed project data are stored in the system, the system would play a crucial role in predicting the performance level and matching the best management practice for a subject project. In addition, the system can also be modified as a tool for a business- or industry-level system by incorporating the existing enterprise resource programs.

• Proceedings of the KIEE Conference
• /
• 2000.07b
• /
• pp.1381-1383
• /
• 2000

A multi-train operation simulation software is under development, in this G7 Project for a High Speed Train System, to simulate the running performance, power consumption, signalling and operation. In the first stage, a Train Performance Simulation (TPS) software is introduced in this paper. This is a core module of whole system and gives some parameters of a train, e.g., its position, speed traction and braking power and electric power system state, etc. In this paper, calculation technique was used for voltage drop at the train's positions and major posts along the catenary line. The final program will be used as an evaluation tool for system performance in constructing a new line or introducing a new train system.

• KIPS Transactions on Software and Data Engineering
• /
• v.7 no.3
• /
• pp.77-90
• /
• 2018

Software Defect Prediction (SDP) is a field of study that identifies defective modules. With insufficient local data, a company can exploit Cross-Project Defect Prediction (CPDP), a way to build a classifier using dataset collected from other companies. Most machine learning algorithms for SDP have used more than one parameter that significantly affects prediction performance depending on different values. The objective of this study is to propose a parameter selection technique to enhance the performance of CPDP. Using a Harmony Search algorithm (HS), our approach tunes parameters of cost-sensitive boosting, a method to tackle class imbalance causing the difficulty of prediction. According to distributional characteristics, parameter ranges and constraint rules between parameters are defined and applied to HS. The proposed approach is compared with three CPDP methods and a Within-Project Defect Prediction (WPDP) method over fifteen target projects. The experimental results indicate that the proposed model outperforms the other CPDP methods in the context of class imbalance. Unlike the previous researches showing high probability of false alarm or low probability of detection, our approach provides acceptable high PD and low PF while providing high overall performance. It also provides similar performance compared with WPDP.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2016.05a
• /
• pp.627-629
• /
• 2016

Open source software when promoting development in R&D has many advantages and is becoming an open software - based R&D projects continuously created in a number of countries. However, there are still limits to the creation of effective performance in R&D projects, as well as difficulty of the project implementation because it not yet established an appropriate management plan for the open source R&D projects in the public sector. Therefore, through investigating a number of previous studies and practices, we intend to enhance the effectiveness of the system by suggesting concrete measures for promoting and managing open source nature in a preperiodic point of view on the public sector open source software R&D projects.

• Journal of Information Technology Services
• /
• v.4 no.1
• /
• pp.117-128
• /
• 2005

When we conduct development of a large-size SI project, it is highly necessary to utilize an appropriate application framework which helps to build a qualified software with high productivity in a short period of time. In this paper, we propose the architecture of a dynamically reconfigurable CBD application integration framework that has been used for developing large-scale e-business applications to achieve high development productivity and maintainability. This Integration framework can easily extend its functionalities, and dynamically change its configuration during run time according to the business category, such as applying interaction patterns among main components in software architecture, rules, policies, and environmental parameters. Dynamic reconfiguration has the feature to make applications be easily customized for changeable requirements. Through our application integration framework, huge sizes of contents can be managed according to the business category as well, by keeping configuration informations and huge volumes of source codes. In order to evaluate out application integration framework in terms of performance criteria, we present experimental results of throughputs from the framework by yielding dynamic configuration without any performance degradation.

• 유체기계공업학회:학술대회논문집
• /
• 2004.12a
• /
• pp.711-714
• /
• 2004

A project to develop internet based collaborative engineering environments using agent technology is started to develop an agent based soft system for design and performance analysis of centrifugal pumps. This paper introduces the feasible technology needed to construct a pump design system based on software agent.

• The KIPS Transactions:PartD
• /
• v.12D no.3 s.99
• /
• pp.417-428
• /
• 2005

Today, IT organizations perform projects with vision related to marketing and financial profit. The objective of realizing the vision is to improve the project performing ability in terms of QCD. Organizations have made a lot of efforts to achieve this objective through process improvement. Large companies such as IBM, Ford, and GE have made over $80\%$ of success through business process re-engineering using information technology instead of business improvement effect by computers. It is important to collect, analyze and manage the data on performed projects to achieve the objective, but quantitative measurement is difficult as software is invisible and the effect and efficiency caused by process change are not visibly identified. Therefore, it is not easy to extract the strategy of improvement. This paper measures and analyzes the project performance, focusing on organizations' external effectiveness and internal efficiency (Qualify, Delivery, Cycle time, and Waste). Based on the measured project performance scores, an OT (Opportunity Tree) model was designed for optimizing the project performance. The process of design is as follows. First, meta data are derived from projects and analyzed by quantitative GQM(Goal-Question-Metric) questionnaire. Then, the project performance model is designed with the data obtained from the quantitative GQM questionnaire and organization's performance score for each area is calculated. The value is revised by integrating the measured scores by area vision weights from all stakeholders (CEO, middle-class managers, developer, investor, and custom). Through this, routes for improvement are presented and an optimized improvement method is suggested. Existing methods to improve software process have been highly effective in division of processes' but somewhat unsatisfactory in structural function to develop and systemically manage strategies by applying the processes to Projects. The proposed OT model provides a solution to this problem. The OT model is useful to provide an optimal improvement method in line with organization's goals and can reduce risks which may occur in the course of improving process if it is applied with proposed methods. In addition, satisfaction about the improvement strategy can be improved by obtaining input about vision weight from all stakeholders through the qualitative questionnaire and by reflecting it to the calculation. The OT is also useful to optimize the expansion of market and financial performance by controlling the ability of Quality, Delivery, Cycle time, and Waste.