| XIAO TAO | M, 2回目発表 | ソフトウェア工学 | 松本 健一, 笠原 正治, 石尾 隆, 畑 秀明, Raula Gaikovina Kula |
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title: *** Characterizing and Mitigating Self-Admitted Technical Debt in Build Systems ***
abstract: *** Technical debt is a metaphor used to describe the situation in which long-term code quality is traded for short-term goals in software projects. In recent years, the concept of self-admitted technical debt (SATD) was proposed, which focuses on debt that is intentionally introduced and described by developers. Although prior work has made important observations about SATD in source code, little is known about SATD in build systems, i.e., the specifications that describe how source code is transformed into deliverables. Build systems often suffer massive maintenance activities during the development process. The part of these activities is produced by SATD, since SATD changes are more difficult to perform and SATD inevitably generates long-term maintenance problems from the short-term hack. Thus, I set out to better understand the characteristics of SATD in build systems. To do so, I classify SATD by locations, reasons, and purposes. To automate the detection of SATD reasons and purposes, I train classifiers to label comments according to the surrounding document content. My work presents the first step towards understanding SATD comment in build systems and opens up avenues for future work on tools to support SATD management in build systems. *** language of the presentation: *** English *** | |||
| ASSAVAKAMHAENGHAN NOPPADOL | M, 2回目発表 | ソフトウェア工学 | 松本 健一, 安本 慶一, 石尾 隆, 畑 秀明, Raula Gaikovina Kula |
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title: A Study of Responses to First-time Contributors in Open Source Software
abstract: Open Source Software (OSS) projects rely on a continuous stream of new contributors for sustainable livelihood. Recent studies reported that new contributors experience many barriers in their first contribution, with the social barrier being critical. Although a number of studies investigated the social barriers to new contributors, we hypothesize that negative first responses may cause an unpleasant feeling, and subsequently lead to the discontinuity of any future contribution. We execute protocols of a registered report to mine 72,635 active projects in GitHub, and analyze 2,765,917 first contributions as Pull Requests (PRs) with 642,841 first responses. Our findings characterize first response instead as positive, but less responsive, non-toxic and exhibit sentiments of fear, joy and love. Positively and responsiveness has no relationship with a future interaction. Qualitatively, we find that negative (non-toxic) first responses are mainly comments, suggestions and questions that are mostly constructive (50.71%) or criticizing (37.68%) in nature . Running different machine learning models, first response interactions are positively correlated with a future contribution, but other dimensions (i.e., project, contributor, contribution) have a larger effect. Although negative comments may be cause unpleasant negative feelings, instead should not be seen as a barrier for both OSS projects and first time contributors. language of the presentation: English | |||
| KANNEE KANCHANOK | M, 2回目発表 | ソフトウェア工学 | 松本 健一, 安本 慶一, 石尾 隆, 畑 秀明, Raula Gaikovina Kula |
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title: Understanding the Release Synchronicity of Third Party Libraries across Software Ecosystems
abstract: With an increase in diverse technology stacks and third-party library usage, developers may inevitably need to switch programming language when developing an application across technologies. To assist developers, maintainers now expand their support by releasing to multiple technologies and their ecosystems, i.e., a cross-ecosystem library. As Open Source Software (OSS), these libraries potentially requires sustained contributions, especially when synchronizing across ecosystems. The goal of this study is to evaluate the extent to which a cross-ecosystem library synchronizes across ecosystems. We perform a large-scale empirical study of 1.1 million libraries from five different software ecosystems, i.e., PyPI for Python, CRAN for R, Maven for Java, RubyGems for Ruby, and NPM for JavaScript. We find that a significant majority (median of 37.5%) of contributors come from a single ecosystem, with a portion being independent (median of 24.06%). Three, i.e., PyPI, CRAN, RubyGems, out of the five ecosystems has the majority of source code is written using languages that are not specific to that ecosystem. Except for Maven, there is a significant difference in semantic versioning (median of 71.43% to 83.33%) across releases. In addition, the release cycle is library dependent, with a trend of 2 to 15 months between releases. Results find that a cross-ecosystem library may not be as synchronized as we assume, opening up new opportunities on how an ecosystem can integrate these libraries. language of the presentation: English | |||
| FAN YOUMEI | M, 1回目発表 | ソフトウェア工学 | 松本 健一, 安本 慶一, 石尾 隆, 畑 秀明, Raula Gaikovina Kula |
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title: *** An Empirical Study of The Impact of Tweets on GitHub Sponsors ***
abstract: *** Nowadays, more and more people prefer open source software (OSS) since it is more secure and stable than proprietary software due to anyone can view and modify open source software, and developers might spot and correct errors or omissions that a program's original authors might have missed. Since GitHub introduced GitHub sponsors lots of developers try to advertise to get more GitHub sponsors, the study will reveal how the GitHub profile was discussed on Twitter and how the GitHub sponsor number will improve by using Twitter to advertise. *** language of the presentation: *** English *** | |||
| ZHANG YONGXIN | M, 2回目発表 | 計算システムズ生物学 | 金谷 重彦, 峠 隆之, MD.ALTAF-UL-AMIN, 小野 直亮, 黄 銘 |
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title: Complicated Human Activity Recognition Based on Temporal-Spatial Graph in Wearable Scenario
abstract: In the field of human activity recognition (HAR), fundamental difficulties in complicated activities recognition (CAR) remain. In this work, to lay a framework for CAR, try to reconcile the distance of simple actions of the complex activities in the physical space with that in the latent feature space by utilizing the Variational Auto-Encoder (VAE) and the Uniform Manifold Approximation and Projection (UMAP), and a new approach proposed to make a graph structure, corresponding graph neural newwork was conducted to classificate the complicated activities. language of the presentation: *** English ** | |||
| ZHU GUANGXIAN | M, 2回目発表 | 計算システムズ生物学 | 金谷 重彦, 峠 隆之, MD.ALTAF-UL-AMIN, 小野 直亮, 黄 銘 |
| MUHAMMAD ALQAAF SUBANDOKO | M, 1回目発表 | 計算システムズ生物学 | 金谷 重彦, 峠 隆之, MD.ALTAF-UL-AMIN, 黄 銘, 小野 直亮 |
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title: *** X-ray Image Utilization to Classify COVID-19 Using Machine Learning *** abstract: *** The global community has been fighting the COVID-19 pandemic for more than a year. COVID-19, which originated in the Chinese city of Wuhan, has rapidly expanded throughout the world. Countries such as India, Bangladesh, Indonesia, and other developing countries are still reluctant to detect COVID-19 infections, particularly if the country is highly populated, therefore detection must occur immediately to halt the spread of the virus and ease tracing to suspect patients. X-rays or others medical image can be used as a method for examining patients with COVID-19. By using principle of image processing, we are able to calculate the area of white patches present in the patient's lungs. Often, medical image data is analyzed manually which takes a lot of time and prone to human misinterpretation. To overcome this, the data analysis can be done by using artificial intelligence (AI) in image processing. AI-based picture examination strategies can give the precise and fast conclusion of the infection for an excessive number of patients in a short time. One of AI techniques which popularly used in various medical field is Deep Neural Networks (DNNs). *** language of the presentation: *** English *** | |||
| YANG SHUO | M, 1回目発表 | 計算システムズ生物学 | 金谷 重彦, 峠 隆之, 黄 銘, MD.ALTAF-UL-AMIN |
| HONG RUIXUN | M, 1回目発表 | 計算システムズ生物学 | 金谷 重彦, 佐藤 嘉伸, 黄 銘, MD.ALTAF-UL-AMIN, 小野 直亮 |