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A Multi-label Classification Bot for Issue Management System in GitHub
Doje Park, Yyejin Yang, Gwang Choi, Seonah Lee, Sungwon Kang
http://doi.org/10.5626/JOK.2021.48.8.928
The GitHub platform, on which many developers develop open-source software projects, provides an issue management system. Using the system, the stakeholders can report software problems or functional requests as issues. The issue management system provides issue report forms and allows developers to create and use labels to classify issues. However, since the labeling work is manually done, it requires a lot of effort from the developers and inaccurate labeling can easily occur. In addition, it takes a lot of time for a project manager to read and give feedback on each issue. To mitigate this problem, previous studies have proposed attaching a single label to an issue automatically. However, in practice, there are a number of issue reports that need multiple labels to be attached. Therefore, in this study, we proposed a multi-labeling bot that automatically attaches multiple labels to an issue report in order to reduce the effort required by a project manager to read issue reports and give feedback in GitHub. The multi-label classification of our bot showed F-score ranging from 0.54 to 0.78.
Reducing the Learning Time of Code Change Recommendation System Using Recurrent Neural Network
Byeong-il Bae, Sungwon Kang, Seonah Lee
http://doi.org/10.5626/JOK.2020.47.10.948
Since code change recommendation systems select and recommend files that needing modifications, they help developers save time spent on software system evolution. However, these recommendation systems generally spend a significant amount of time in learning accumulated data and relearning whenever new data are accumulated. This study proposes a method to reduce the time spent on learning when using Code change Recommendation System using Recurrent Neural Network (RNN-CRS), which works by avoiding the learning that is unlikely to contribute to new knowledge. For the five products used in the experimental evaluation, our proposed method reduced the time to relearn data and re-generate a learning model by as much as 49.08%-68.15%, and by 10.66% in the least effective case, compared to the existing method.
Design of Extended Real-time Data Pipeline System Architecture
Hoseung Shin, Sungwon Kang, Jihyun Lee
Big data systems are widely used to collect large-scale log data, so it is very important for these systems to operate with a high level of performance. However, the current Hadoop-based big data system architecture has a problem in that its performance is low as a result of redundant processing. This paper solves this problem by improving the design of the Hadoop system architecture. The proposed architecture uses the batch-based data collection of the existing architecture in combination with a single processing method. A high level of performance can be achieved by analyzing the collected data directly in memory to avoid redundant processing. The proposed architecture guarantees system expandability, which is an advantage of using the Hadoop architecture. This paper confirms that the proposed architecture is approximately 30% to 35% faster in analyzing and processing data than existing architectures and that it is also extendable.
A Software Architecture Design Method that Matches Problem Frames and Architectural Patterns
Jungmin Kim, Sungwon Kang, Jihyun Lee
While architectural patterns provide software development solutions by providing schemas for structural organizations of software systems based on empirical knowledge, Jackson’s problem frames provide a method of analyzing software problems. Problem frames are useful to understanding the software development problem, by putting emphasis on the problem domain, rather than on the solution space. Research exists that relates problem frames and software architecture, but most of this research uses problem frames only to understand given problems. Moreover, none of the existing research derives architectural patterns by considering both problem frames and quality attributes. In this paper, we propose a software architecture design method for pattern-based architecture design, by matching problem frames and architectural patterns. To that end, our approach first develops the problem model based on the problem frames approach, and then uses it to match with candidate architectural patterns, from the perspectives of both functionality, and quality attributes. Functional matching uses the problem frame diagram to match the problem model of an architectural pattern. We conduct a case study to show that our approach can systematically decide the right architectural patterns, and provide a basis for fine-grained software architecture design.
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