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

“…). In particular, some recent studies have explored how to leverage large language models of code (LLMCs…”
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What Makes Good In-Context Demonstrations for Code Intelligence Tasks with LLMs? by Gao, Shuzheng, Wen, Xin-Cheng, Gao, Cuiyun, Wang, Wenxuan, Zhang, Hongyu, Lyu, Michael R.

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

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

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

“… This paper presents an empirical study on the prevalence and severity of API misuse on Stack Overflow…”
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Nuances are the Key: Unlocking ChatGPT to Find Failure-Inducing Tests with Differential Prompting by Li, Tsz-On, Zong, Wenxi, Wang, Yibo, Tian, Haoye, Wang, Ying, Cheung, Shing-Chi, Kramer, Jeff

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

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

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

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

“… Prior studies provide recommendations on logging locations, but such recommendations are only for limited situations (e.g., exception logging…”
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A Large-Scale Empirical Study on Vulnerability Distribution within Projects and the Lessons Learned by Liu, Bingchang, Meng, Guozhu, Zou, Wei, Gong, Qi, Li, Feng, Lin, Min, Sun, Dandan, Huo, Wei, Zhang, Chao

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

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

“… Traditional APR tools typically focus on specific bug types and fixes through the use of templates, heuristics, and formal specifications…”
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How Many of All Bugs Do We Find? A Study of Static Bug Detectors by Habib, Andrew, Pradel, Michael

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

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

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

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

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

“…Formal methods and tools have a long history of successful applications in the design of safety-critical railway products…”
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Vulnerability Detection with Code Language Models: How Far are We? by Ding, Yangruibo, Fu, Yanjun, Ibrahim, Omniyyah, Sitawarin, Chawin, Chen, Xinyun, Alomair, Basel, Wagner, David, Ray, Baishakhi, Chen, Yizheng

“…In the context of the rising interest in code language models (code LMs) and vulnerability detection, we study the effectiveness of code LMs for detecting vulnerabilities…”
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