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Kazumasa Shimari

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2020 – today

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2024
[j2]
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Kazumasa Shimari, Takashi Ishio, Tetsuya Kanda, Katsuro Inoue:
Evaluating the effectiveness of size-limited execution trace with near-omniscient debugging. Sci. Comput. Program. 236: 103117 (2024)
[c14]
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Takashi Nakano, Kazumasa Shimari, Raula Gaikovina Kula, Christoph Treude, Marc Cheong, Kenichi Matsumoto:
Nigerian Software Engineer or American Data Scientist? GitHub Profile Recruitment Bias in Large Language Models. ICSME 2024: 624-629
[c13]
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  - conf/issre/FebriyantiFSMK24
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Indira Febriyanti, Youmei Fan, Kazumasa Shimari, Kenichi Matsumoto, Raula Gaikovina Kula:
How Maintainable is Proficient Code? A Case Study of Three PyPI Libraries. ISSRE (Workshops) 2024: 103-104
[c12]
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Tetsuya Kitaoka, Yuichiro Kanzaki, Takashi Ishio, Kazumasa Shimari, Kenichi Matsumoto:
Initial Investigation of Behavioral Changes of Obfuscated Programs Caused by Code Optimization. ISSRE (Workshops) 2024: 109-110
[c11]
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  - conf/wcre/MorisakiSIM24
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Keita Morisaki, Kazumasa Shimari, Takashi Ishio, Kenichi Matsumoto:
Test Case Generation for Python Libraries Using Dependent Projects' Test-Suites. SANER (Companion) 2024: 167-174
[c10]
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Atsuhito Yamaoka, Teyon Son, Kazumasa Shimari, Takashi Ishio, Kenichi Matsumoto:
Comparing Execution Trace Using Merkle- Tree to Detect Backward Incompatibilities. SANER 2024: 649-653
[i2]
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  - journals/corr/abs-2409-12544
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Takashi Nakano, Kazumasa Shimari, Raula Gaikovina Kula, Christoph Treude, Marc Cheong, Kenichi Matsumoto:
Nigerian Software Engineer or American Data Scientist? GitHub Profile Recruitment Bias in Large Language Models. CoRR abs/2409.12544 (2024)
[i1]
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  - journals/corr/abs-2410-05683
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Indira Febriyanti, Youmei Fan, Kazumasa Shimari, Kenichi Matsumoto, Raula Gaikovina Kula:
How Maintainable is Proficient Code? A Case Study of Three PyPI Libraries. CoRR abs/2410.05683 (2024)
2023
[c9]
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  - conf/csee/FukushimaISM23
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Kazuki Fukushima, Takashi Ishio, Kazumasa Shimari, Kenichi Matsumoto:
Towards Assessment of Practicality of Introductory Programming Course Using Vocabulary of Textbooks, Assignments, and Actual Projects. CSEE&T 2023: 199-200
[c8]
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Takafumi Sakura, Ryo Soga, Hideyuki Kanuka, Kazumasa Shimari, Takashi Ishio:
Leveraging Execution Trace with ChatGPT: A Case Study on Automated Fault Diagnosis. ICSME 2023: 397-402
2022
[c7]
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  - conf/icsm/ShimariTIMIT22
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Kazumasa Shimari, Masahiro Tanaka, Takashi Ishio, Makoto Matsushita, Katsuro Inoue, Satoru Takanezawa:
Selecting Test Cases based on Similarity of Runtime Information: A Case Study of an Industrial Simulator. ICSME 2022: 564-567
[c6]
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Tetsuya Kanda, Kazumasa Shimari, Katsuro Inoue:
didiffff: a viewer for comparing changes in both code and execution traces. ICPC 2022: 528-532
[c5]
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Kazuki Fukushima, Takashi Ishio, Kazumasa Shimari, Kenichi Matsumoto:
A Similarity-based Assisted Grading for Introductory Programming Course. IWSC 2022: 23-24
[c4]
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Fumiya Sato, Ayano Ikegami, Takashi Ishio, Kazumasa Shimari, Kenichi Matsumoto:
Comparing Execution Traces of Jupyter Notebook for Checking Correctness of Refactoring. IWSC 2022: 62-68
[c3]
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  - conf/wcre/SugiyamaKSI22
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Sakutaro Sugiyama, Takashi Kobayashi, Kazumasa Shimari, Takashi Ishio:
JISDLab: A web-based interactive literate debugging environment. SANER 2022: 497-501
2021
[j1]
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  - journals/scp/ShimariIKII21
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Kazumasa Shimari, Takashi Ishio, Tetsuya Kanda, Naoto Ishida, Katsuro Inoue:
NOD4J: Near-omniscient debugging tool for Java using size-limited execution trace. Sci. Comput. Program. 206: 102630 (2021)

2010 – 2019

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2019
[c2]
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Kazumasa Shimari, Takashi Ishio, Tetsuya Kanda, Katsuro Inoue:
Near-Omniscient Debugging for Java Using Size-Limited Execution Trace. ICSME 2019: 398-401
[c1]
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  - conf/iwpc/MizouchiSII19
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Tsuyoshi Mizouchi, Kazumasa Shimari, Takashi Ishio, Katsuro Inoue:
PADLA: a dynamic log level adapter using online phase detection. ICPC 2019: 135-138

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