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Quiver-theoretical approach to dynamical Yang-Baxter maps [article]

Diogo Kendy Matsumoto, Kenichi Shimizu
2017 arXiv   pre-print
A dynamical Yang-Baxter map, introduced by Shibukawa, is a solution of the set-theoretical analogue of the dynamical Yang-Baxter equation. In this paper, we initiate a quiver-theoretical approach for the study of dynamical Yang-Baxter maps. Our key observation is that the category of dynamical sets over a set Λ, introduced by Shibukawa to establish a categorical framework to deal with dynamical Yang-Baxter maps, can be embedded into the category of quivers with vertices Λ. By using this
more » ... g, we shed light on Shibukawa's classification result of a certain class of dynamical Yang-Baxter maps and extend his construction to obtain a new class of dynamical Yang-Baxter maps. We also discuss a relation between Shibukawa's bialgebroid associated to a dynamical Yang-Baxter map and Hayashi's weak bialgebra associated to a star-triangular face model.
arXiv:1703.10412v1 fatcat:fnn7kbfh7rbzrkzzrvycrwy5f4

DevReplay: Automatic Repair with Editable Fix Pattern [article]

Yuki Ueda, Takashi Ishio, Akinori Ihara, Kenichi Matsumoto
2020 arXiv   pre-print
Static analysis tools, or linters, detect violation of source code conventions to maintain project readability. Those tools automatically fix specific violations while developers edit the source code. However, existing tools are designed for the general conventions of programming languages. These tools do not check the project/API-specific conventions. We propose a novel static analysis tool DevReplay that generates code change patterns by mining the code change history, and we recommend
more » ... using the matched patterns. Using DevReplay, developers can automatically detect and fix project/API-specific problems in the code editor and code review. Also, we evaluate the accuracy of DevReplay using automatic program repair tool benchmarks and real software. We found that DevReplay resolves more bugs than state-of-the-art APR tools. Finally, we submitted patches to the most popular open-source projects that are implemented by different languages, and project reviewers accepted 80% (8 of 10) patches. DevReplay is available on https://devreplay.github.io.
arXiv:2005.11040v1 fatcat:apz76pygcnanrgdspro47udglu

Studies on Stomach Poison against Oncomelania nosophora

Kazuo Mori, Kenichi Okamoto, Kenichi Sugiura, Katsuhiko Matsumoto, Tsuko Nakagome
1959 Journal of The Showa Medical Association  
doi:10.14930/jsma1939.19.245 fatcat:nxpz3yethfekzmzbegejlzjq5y

Analysis of the Conurbation between the two Cities, Matsumoto and Shiojiri
松本・塩尻両市における conurbation の分析

Yukio NAGANO, Kenichi YAMAMOTO
1979 The New Geography  
Yukio NAGANO, Kenichi YAMAMOTO Distinctive features can be seen in the direction and form of expansion of an urban district during urbanization.  ...  Analysis of the Conurbation between the two Cities , Matsumoto and Shiojiri.  ... 
doi:10.5996/newgeo.27.2_1 fatcat:4c7l6rrkpnc6nomn73din6yyme

Mutant Frequency is not Increased in Mice Orally Exposed to Sodium Dichromate

Yasunobu Aoki, Michiyo Matsumoto, Michi Matsumoto, Kenichi Masumura, Takehiko Nohmi
2019 Food Safety  
The in vivo mutagenicity of hexavalent chromium in the small intestine, the target organ of tumorgenicity, was examined by means of a transgenic mouse gene mutation assay. Sodium dichromate dihydrate was administered orally in drinking water to male gpt delta mice at a dose of 85.7 or 257.4 mg/L for 28 days or at a dose of 8.6, 28.6 or 85.7 mg/L for 90 days. No significant increase in gpt mutant frequency relative to that in control mice was observed in the small intestine in either the 28- or
more » ... 0-day study, whereas 28-day oral administration of potassium bromate, a positive control substance, increased mutant frequency.
doi:10.14252/foodsafetyfscj.2018014 pmid:31998582 pmcid:PMC6977768 fatcat:k6ok7k6ndjgqzhdbbwsduo7tle

Biomethanol Production from Forage Grasses, Trees, and Crop Residues [chapter]

Hitoshi Nakagawa, Masayasu Sakai, Toshirou Harada, Toshimitsu Ichinose, Keiji Takeno, Shinji Matsumoto, Makoto Kobayashi, Keigo Matsumoto, Kenichi Yakushido
2011 Biofuel's Engineering Process Technology  
., Inc. with a gasifier capacity of 2 t dry biomass/day (Matsumoto et al. 2008).  ...  Development Organization (NEDO) Project, has, however, achieved ca.20% of methanol yield by weight by its operation(Ishii et al. 2005;Matsumoto et al. 2008;Ogi et al. 2008).  ...  , Makoto Kobayashi,Keigo Matsumoto and Kenichi Yakushido (2011) .  ... 
doi:10.5772/18168 fatcat:dvpxtck6hna77dtwzyb6sqln2q

Predicting Defective Lines Using a Model-Agnostic Technique [article]

Supatsara Wattanakriengkrai, Patanamon Thongtanunam, Chakkrit Tantithamthavorn, Hideaki Hata, Kenichi Matsumoto
2020 arXiv   pre-print
Defect prediction models are proposed to help a team prioritize source code areas files that need Software QualityAssurance (SQA) based on the likelihood of having defects. However, developers may waste their unnecessary effort on the whole filewhile only a small fraction of its source code lines are defective. Indeed, we find that as little as 1%-3% of lines of a file are defective. Hence, in this work, we propose a novel framework (called LINE-DP) to identify defective lines using a
more » ... stic technique, i.e., an Explainable AI technique that provides information why the model makes such a prediction. Broadly speaking, our LINE-DP first builds a file-level defect model using code token features. Then, our LINE-DP uses a state-of-the-art model-agnostic technique (i.e.,LIME) to identify risky tokens, i.e., code tokens that lead the file-level defect model to predict that the file will be defective. Then, the lines that contain risky tokens are predicted as defective lines. Through a case study of 32 releases of nine Java open source systems, our evaluation results show that our LINE-DP achieves an average recall of 0.61, a false alarm rate of 0.47, a top 20%LOC recall of0.27, and an initial false alarm of 16, which are statistically better than six baseline approaches. Our evaluation shows that our LINE-DP requires an average computation time of 10 seconds including model construction and defective line identification time. In addition, we find that 63% of defective lines that can be identified by our LINE-DP are related to common defects (e.g., argument change, condition change). These results suggest that our LINE-DP can effectively identify defective lines that contain common defectswhile requiring a smaller amount of inspection effort and a manageable computation cost.
arXiv:2009.03612v1 fatcat:ggqapwzdwzhoxlc3es7xbopuwe

Influence of Outliers on Estimation Accuracy of Software Development Effort

Kenichi ONO, Masateru TSUNODA, Akito MONDEN, Kenichi MATSUMOTO
2021 IEICE transactions on information and systems  
Kenichi Matsumoto is a professor in the Graduate School of Information Science at Nara Institute of Science and Technology, Japan.  ...  Matsumoto received a PhD in information and computer sciences from Osaka University, Japan.  ... 
doi:10.1587/transinf.2020mpp0005 fatcat:7itoynru4fhc5fmdiqtaopymui

Contrasting Third-Party Package Management User Experience [article]

Syful Islam, Raula Gaikovina Kula, Christoph Treude, Bodin Chinthanet, Takashi Ishio, Kenichi Matsumoto
2021 arXiv   pre-print
The management of third-party package dependencies is crucial to most technology stacks, with package managers acting as brokers to ensure that a verified package is correctly installed, configured, or removed from an application. Diversity in technology stacks has led to dozens of package ecosystems with their own management features. While recent studies have shown that developers struggle to migrate their dependencies, the common assumption is that package ecosystems are used without any
more » ... e. In this study, we explore 13 package ecosystems to understand whether their features correlate with the experience of their users. By studying experience through the questions that developers ask on the question-and-answer site Stack Overflow, we find that developer questions are grouped into three themes (i.e., Package management, Input-Output, and Package Usage). Our preliminary analysis indicates that specific features are correlated with the user experience. Our work lays out future directions to investigate the trade-offs involved in designing the ideal package ecosystem.
arXiv:2108.06262v1 fatcat:ttuxlt3wabcs5kfpnqajc5a4v4

Mega Software Engineering [chapter]

Katsuro Inoue, Pankaj K. Garg, Hajimu Iida, Kenichi Matsumoto, Koji Torii
2005 Lecture Notes in Computer Science  
In various fields of computer science, rapidly growing hardware power, such as high-speed network, high-performance CPU, huge disk capacity, and large memory space, has been fruitfully harnessed. Examples of such usage are large scale data and web mining, grid computing, and multimedia environments. We propose that such rich hardware can also catapult software engineering to the next level. Huge amounts of software engineering data can be systematically collected and organized from tens of
more » ... ands of projects inside organizations, or from outside an organization through the Internet. The collected data can be analyzed extensively to extract and correlate multi-project knowledge for improving organization-wide productivity and quality. We call such an approach for software engineering Mega Software Engineering. In this paper, we propose the concept of Mega Software Engineering, and demonstrate some novel data analysis characteristic of Mega Software Engineering. We describe a framework for enabling Mega Software Engineering.
doi:10.1007/11497455_32 fatcat:uejwxigntfckpdnf66qmqlxn5u

Bug or Not? Bug Report Classification Using N-Gram IDF [article]

Pannavat Terdchanakul, Hideaki Hata, Passakorn Phannachitta, Kenichi Matsumoto
2017 arXiv   pre-print
Previous studies have found that a significant number of bug reports are misclassified between bugs and non-bugs, and that manually classifying bug reports is a time-consuming task. To address this problem, we propose a bug reports classification model with N-gram IDF, a theoretical extension of Inverse Document Frequency (IDF) for handling words and phrases of any length. N-gram IDF enables us to extract key terms of any length from texts, these key terms can be used as the features to
more » ... bug reports. We build classification models with logistic regression and random forest using features from N-gram IDF and topic modeling, which is widely used in various software engineering tasks. With a publicly available dataset, our results show that our N-gram IDF-based models have a superior performance than the topic-based models on all of the evaluated cases. Our models show promising results and have a potential to be extended to other software engineering tasks.
arXiv:1709.05763v1 fatcat:elxholvf2vg43ckqvfg72bfvmq

The Impact of Automated Parameter Optimization on Defect Prediction Models [article]

Chakkrit Tantithamthavorn, Shane McIntosh, Ahmed E. Hassan, Kenichi Matsumoto
2018 arXiv   pre-print
Matsumoto is with the Graduate School of Information Science, Nara Institute of Science and Technology, Japan. E-mail: matumoto@is.naist.jp.  ...  Kenichi Matsumoto is a professor in the Graduate School of Information Science at Nara Institute of Science and Technology, Japan.  ...  More about Kenichi and his work is available online at http://se-naist.jp/. Shane  ... 
arXiv:1801.10270v1 fatcat:cq63gkxivzdxrl23jtnd6rtwam

Monkey Features Location Identification Using Convolutional Neural Networks [article]

Rollyn Labuguen, Vishal Gaurav, Salvador Negrete Blanco, Jumpei Matsumoto, Kenichi Inoue, Tomohiro Shibata
2018 bioRxiv   pre-print
Understanding animal behavior in its natural habitat is a challenging task. One of the primary step for analyzing animal behavior is feature detection. In this study, we propose the use of deep convolutional neural network (CNN) to locate monkey features from raw RGB images of monkey in its natural environment. We train the model to identify features such as the nose and shoulders of the monkey at about 0.01 model loss.
doi:10.1101/377895 fatcat:sxwmb7scgjbitdvyv64txcafju

pycefr: Python Competency Level through Code Analysis [article]

Gregorio Robles, Raula Gaikovina Kula, Chaiyong Ragkhitwetsagul, Tattiya Sakulniwat, Kenichi Matsumoto, Jesus M. Gonzalez-Barahona
2022 arXiv   pre-print
Python is known to be a versatile language, well suited both for beginners and advanced users. Some elements of the language are easier to understand than others: some are found in any kind of code, while some others are used only by experienced programmers. The use of these elements lead to different ways to code, depending on the experience with the language and the knowledge of its elements, the general programming competence and programming skills, etc. In this paper, we present pycefr, a
more » ... ol that detects the use of the different elements of the Python language, effectively measuring the level of Python proficiency required to comprehend and deal with a fragment of Python code. Following the well-known Common European Framework of Reference for Languages (CEFR), widely used for natural languages, pycefr categorizes Python code in six levels, depending on the proficiency required to create and understand it. We also discuss different use cases for pycefr: identifying code snippets that can be understood by developers with a certain proficiency, labeling code examples in online resources such as Stackoverflow and GitHub to suit them to a certain level of competency, helping in the onboarding process of new developers in Open Source Software projects, etc. A video shows availability and usage of the tool: https://tinyurl.com/ypdt3fwe.
arXiv:2203.15990v1 fatcat:v2eesiwrj5hf3ibo2sh4ujmkry

Toward Imitating Visual Attention of Experts in Software Development Tasks [article]

Yoshiharu Ikutani, Nishanth Koganti, Hideaki Hata, Takatomi Kubo, Kenichi Matsumoto
2019 arXiv   pre-print
Expert programmers' eye-movements during source code reading are valuable sources that are considered to be associated with their domain expertise. We advocate a vision of new intelligent systems incorporating expertise of experts for software development tasks, such as issue localization, comment generation, and code generation. We present a conceptual framework of neural autonomous agents based on imitation learning (IL), which enables agents to mimic the visual attention of an expert via
more » ... her eye movement. In this framework, an autonomous agent is constructed as a context-based attention model that consists of encoder/decoder network and trained with state-action sequences generated by an experts' demonstration. Challenges to implement an IL-based autonomous agent specialized for software development task are discussed in this paper.
arXiv:1903.06320v1 fatcat:2msydrv6kjb2lewhdiyxn62nuy
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