Výsledky vyhledávání - General AND reference Cross-computing tools AND techniques Empirical studies

  1. 1

    An Empirical Study on Fine-Tuning Large Language Models of Code for Automated Program Repair Autor Huang, Kai, Meng, Xiangxin, Zhang, Jian, Liu, Yang, Wang, Wenjie, Li, Shuhao, Zhang, Yuqing

    ISSN: 2643-1572
    Vydáno: IEEE 11.09.2023
    “…). In particular, some recent studies have explored how to leverage large language models of code (LLMCs…”
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  2. 2

    What Makes Good In-Context Demonstrations for Code Intelligence Tasks with LLMs? Autor Gao, Shuzheng, Wen, Xin-Cheng, Gao, Cuiyun, Wang, Wenxuan, Zhang, Hongyu, Lyu, Michael R.

    ISSN: 2643-1572
    Vydáno: IEEE 11.09.2023
    “…Pre-trained models of source code have gained widespread popularity in many code intelligence tasks. Recently, with the scaling of the model and corpus size,…”
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  3. 3

    Lost in Translation: A Study of Bugs Introduced by Large Language Models While Translating Code Autor Pan, Rangeet, Ibrahimzada, Ali Reza, Krishna, Rahul, Sankar, Divya, Wassi, Lambert Pougeum, Merler, Michele, Sobolev, Boris, Pavuluri, Raju, Sinha, Saurabh, Jabbarvand, Reyhaneh

    ISSN: 1558-1225
    Vydáno: ACM 14.04.2024
    “… To that end, we present a large-scale empirical study to investigate the ability of general LLMs and code LLMs for code translation across pairs of different languages, including C, C++…”
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  4. 4

    Nuances are the Key: Unlocking ChatGPT to Find Failure-Inducing Tests with Differential Prompting Autor Li, Tsz-On, Zong, Wenxi, Wang, Yibo, Tian, Haoye, Wang, Ying, Cheung, Shing-Chi, Kramer, Jeff

    ISSN: 2643-1572
    Vydáno: IEEE 11.09.2023
    “… We are motivated to study how far this outstanding challenge can be solved by recent advances in large language models (LLMs) such as ChatGPT…”
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  5. 5

    An Empirical Study of Parameter-Efficient Fine-Tuning Methods for Pre-Trained Code Models Autor Liu, Jiaxing, Sha, Chaofeng, Peng, Xin

    ISSN: 2643-1572
    Vydáno: IEEE 11.09.2023
    “… Although these methods claim superiority over the prior techniques, they seldom make a comprehensive and fair comparison on multiple software engineering tasks…”
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  6. 6

    Grounded Theory in Software Engineering Research: A Critical Review and Guidelines Autor Stol, Klaas-Jan, Ralph, Paul, Fitzgerald, Brian

    ISSN: 1558-1225
    Vydáno: ACM 01.05.2016
    “…Grounded Theory (GT) has proved an extremely useful research approach in several fields including medical sociology, nursing, education and management theory…”
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  7. 7

    Gamma: Revisiting Template-Based Automated Program Repair Via Mask Prediction Autor Zhang, Quanjun, Fang, Chunrong, Zhang, Tongke, Yu, Bowen, Sun, Weisong, Chen, Zhenyu

    ISSN: 2643-1572
    Vydáno: IEEE 11.09.2023
    “…Automated program repair (APR) aims to fix software bugs without manual debugging efforts and plays a crucial role in software development and maintenance…”
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  8. 8

    Are code examples on an online Q&A forum reliable?: a study of API misuse on stack overflow Autor Zhang, Tianyi, Upadhyaya, Ganesha, Reinhardt, Anastasia, Rajan, Hridesh, Kim, Miryung

    ISBN: 9781450356381, 1450356389
    ISSN: 1558-1225
    Vydáno: New York, NY, USA ACM 27.05.2018
    “… This paper presents an empirical study on the prevalence and severity of API misuse on Stack Overflow…”
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  9. 9

    Assessing and Restoring Reproducibility of Jupyter Notebooks Autor Wang, Jiawei, KUO, Tzu-Yang, Li, Li, Zeller, Andreas

    ISSN: 2643-1572
    Vydáno: ACM 01.09.2020
    “…Jupyter notebooks-documents that contain live code, equations, visualizations, and narrative text-now are among the most popular means to compute, present,…”
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  10. 10

    Where Shall We Log? Studying and Suggesting Logging Locations in Code Blocks Autor Li, Zhenhao, Chen, Tse-Hsun, Shang, Weiyi

    ISSN: 2643-1572
    Vydáno: ACM 01.09.2020
    “… Prior studies provide recommendations on logging locations, but such recommendations are only for limited situations (e.g., exception logging…”
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  11. 11

    A Large-Scale Empirical Study on Vulnerability Distribution within Projects and the Lessons Learned Autor Liu, Bingchang, Meng, Guozhu, Zou, Wei, Gong, Qi, Li, Feng, Lin, Min, Sun, Dandan, Huo, Wei, Zhang, Chao

    ISSN: 1558-1225
    Vydáno: ACM 01.10.2020
    “… Previous research either focuses on analyzing bugs rather than vulnerabilities, or only studies general vulnerability distribution among projects rather than the distribution within each project…”
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  12. 12

    The Plastic Surgery Hypothesis in the Era of Large Language Models Autor Xia, Chunqiu Steven, Ding, Yifeng, Zhang, Lingming

    ISSN: 2643-1572
    Vydáno: IEEE 11.09.2023
    “… Traditional APR tools typically focus on specific bug types and fixes through the use of templates, heuristics, and formal specifications…”
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  13. 13

    When Less is Enough: Positive and Unlabeled Learning Model for Vulnerability Detection Autor Wen, Xin-Cheng, Wang, Xinchen, Gao, Cuiyun, Wang, Shaohua, Liu, Yang, Gu, Zhaoquan

    ISSN: 2643-1572
    Vydáno: IEEE 11.09.2023
    “… Prior studies have demonstrated that the non-vulnerable code (i.e., negative labels) tends to be unreliable in commonly-used datasets, while vulnerable code (i.e., positive labels) is more determined…”
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  14. 14

    On the Evaluation of Neural Code Translation: Taxonomy and Benchmark Autor Jiao, Mingsheng, Yu, Tingrui, Li, Xuan, Qiu, Guanjie, Gu, Xiaodong, Shen, Beijun

    ISSN: 2643-1572
    Vydáno: IEEE 11.09.2023
    “…s. Based on the empirical results, we develop a taxonomy that categorizes code translation tasks into four primary types according to their complexity…”
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  15. 15

    An Empirical Study on Automatically Detecting AI-Generated Source Code: How Far are We? Autor Suh, Hyunjae, Tafreshipour, Mahan, Li, Jiawei, Bhattiprolu, Adithya, Ahmed, Iftekhar

    ISSN: 1558-1225
    Vydáno: IEEE 26.04.2025
    “… This study first presents an empirical analysis to investigate the effectiveness of the existing AI detection tools in detecting AI-generated code…”
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  16. 16

    Secure coding practices in Java: challenges and vulnerabilities Autor Meng, Na, Nagy, Stefan, Yao, Danfeng (Daphne), Zhuang, Wenjie, Argoty, Gustavo Arango

    ISBN: 9781450356381, 1450356389
    ISSN: 1558-1225
    Vydáno: New York, NY, USA ACM 27.05.2018
    “… We conducted an empirical study on StackOverflow posts, aiming to understand developers' concerns on Java secure coding, their programming obstacles, and insecure coding practices…”
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  17. 17

    Exploiting Input Sanitization for Regex Denial of Service Autor Barlas, Efe, Du, Xin, Davis, James C.

    ISSN: 1558-1225
    Vydáno: ACM 01.05.2022
    “… In this paper, we conduct the first black-box study measuring the extent of ReDoS vulnerabilities in live web services…”
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  18. 18

    How Many of All Bugs Do We Find? A Study of Static Bug Detectors Autor Habib, Andrew, Pradel, Michael

    ISSN: 2643-1572
    Vydáno: ACM 03.09.2018
    “… To decide which of these bugs the tools detect, we use a novel methodology that combines an automatic analysis of warnings and bugs with a manual validation of each candidate…”
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  19. 19

    Comparing Formal Tools for System Design: a Judgment Study Autor Ferrari, Alessio, Mazzanti, Franco, Basile, Davide, ter Beek, Maurice H., Fantechi, Alessandro

    ISSN: 1558-1225
    Vydáno: ACM 01.10.2020
    “…Formal methods and tools have a long history of successful applications in the design of safety-critical railway products…”
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  20. 20

    How Android Developers Handle Evolution-induced API Compatibility Issues: A Large-scale Study Autor Xia, Hao, Zhang, Yuan, Zhou, Yingtian, Chen, Xiaoting, Wang, Yang, Zhang, Xiangyu, Cui, Shuaishuai, Hong, Geng, Zhang, Xiaohan, Yang, Min, Yang, Zhemin

    ISSN: 1558-1225
    Vydáno: ACM 01.10.2020
    “…As Android platform evolves in a fast pace, API-related compatibility issues become a significant challenge for developers. To handle an incompatible API…”
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