In software development, there are numerous reasons why a development organization changes. These include cost reduction and retiring developers. Often the quality of software developed by multiple organizations is low, leading to problems such as difficulty in the evolution of source code. To detect factors indicating low evolvability of software, we focus two maintain-ability characteristics of software development: defect proneness and refactoring tendencies of the source code. We investigate the changes in the relationship of software metrics and the number of defects. Then we compare the results and editing tendencies for refactoring and evolving software in experiments using OpenOffice.org, which is developed by multiple companies, and a tool developed by outsourcing. Defects tend to concentrate on files that depend on many other files. Additionally, the code review confirms that refactoring before and after a development organization change is important for future developments.
Online community networks provide a variety of interaction mechanisms, many of which focus on the confirmation of functional roles or the validation of user experience results. This study is concerned with the increasing emphasis on the publicity of community media among colleges and universities. Therefore, this study adopts the fan pages of university institutions as the object of research, in order to investigate the relationships between different types of information formats (links, photos, and videos) and the emotions and behaviors of their fans. Using stimulus-organism-response theory, a model of the relationship between information format, emotional response, and behavioral response is tested. Data were collected from the twenty largest university fan pages in the USA in this study. A total of 13,006 posts and user comments are collected, compared, and analyzed. Findings validate an interactive correlation between user behavior and information format. Notably, university fan pages with larger fan bases are more capable of applying information formats to prompt user interaction and users' willingness to comment and share, consequently expanding the organic reach of their posts. These results reiterate that users' emotions significantly affect their behaviors and that information serves as a key function in user experiences. Effectively triggering user emotions has become the primary means for community administrators to produce viral posts.
An innovative engineer who can address a social challenge using big data processing, AI and cloud computing technologies with the generation of new business and value is required from industry. enPiT is an education project to develop the advanced IT engineer based on practical education in cooperation between industry and academia promoted by Ministry of Education, Culture, Sports, Science and Technology (MEXT) of Japan. In this paper, we introduce how we designed PBL centered curriculum named AiBiC Spiral under the framework of enPiT education project, and analyze educational effect of our program based on work products and the questionnaire result targeting students who took and completed to the program of 2017.
This paper proposes extended association rule mining that can deal with correlation functions. The extended association rule is expressed in the form of: A ⇒ Correl(X, Y ) where Correl(X, Y ) is a correlation function with two variables X and Y . By this extension, data analysts can discover the condition A that lead to low (or high) correlation between two given variables from a large dataset. In order to show the efficacy of the proposed method, a case study is performed on an industry dataset of software developments, assuming the scenario of discovering a condition, where software development effort is predictable (or unpredictable) from the size of the project, i.e. there exists a significantly high (or low) correlation between size and effort. Since such a condition cannot be obtained by conventional association rule mining, we confirm the efficiency of the proposed extended association rule mining.
In real-world applications such as fraud detection, target class values have an unequal size. This problem is called class-imbalanced data. Many strategies have been proposed to deal with this situation. Most of them focused on changing the data characteristics. For example, adjusting class distribution is one of the most popular approaches for this matter. In this work, we proposed TOP (TwO-levels of Positive resampling framework), an alternative framework to resolve such a problem. Our technique exploits DBSCAN mechanism and other resampling algorithms in order to maximize classification performance. It is able to dynamically draw two boundaries that represent similarity level between consideration positive and other instances. Many possible resampling techniques such as undersampling or over-sampling are allowed to perform inside those areas. We benchmarked TOP with three types of resampling techniques including over-sampling, down-sampling, and hybrid-sampling by training eleven machine learning algorithms on fifteen datasets. As a result, our technique outperformed other techniques in several evaluation metrics.
