ICSE 2024
Fri 12 - Sun 21 April 2024 Lisbon, Portugal
Wed 17 Apr 2024 12:15 - 12:30 at Amália Rodrigues - Evolution & AI Chair(s): Oscar Chaparro

As software engineering advances and the code demand rises, the prevalence of code clones has increased. This phenomenon poses risks like vulnerability propagation, underscoring the growing importance of code clone detection techniques. While numerous code clone detection methods have been proposed, they often fall short in real-world code environments. They either struggle to effectively identify code clones or demand substantial time and computational resources to handle complex clones. This paper introduces a code clone detection method namely \emph{Toma} using tokens and machine learning. Specifically, we extract token type sequences and employ six similarity calculation methods to generate feature vectors. These vectors are then input into a trained machine learning model for classification. To evaluate the effectiveness and scalability of our tool \emph{Toma}, we conducted experiments on the widely used BigCloneBench dataset. Results show that our tool outperforms token-based code clone detectors and most tree-based clone detectors, demonstrating high effectiveness and significant time savings.

Wed 17 Apr

Displayed time zone: Lisbon change

11:00 - 12:30
Evolution & AIResearch Track at Amália Rodrigues
Chair(s): Oscar Chaparro William & Mary
11:00
15m
Talk
Prism: Decomposing Program Semantics for Code Clone Detection through Compilation
Research Track
Haoran Li Nankai university, wangsiqian Nankai university, Weihong Quan Nankai university, Xiaoli Gong Nankai University, Huayou Su NUDT, Jin Zhang Hunan Normal University
11:15
15m
Talk
Evaluating Code Summarization Techniques: A New Metric and an Empirical Characterization
Research Track
Antonio Mastropaolo Università della Svizzera italiana, Matteo Ciniselli Università della Svizzera Italiana, Massimiliano Di Penta University of Sannio, Italy, Gabriele Bavota Software Institute @ Università della Svizzera Italiana
11:30
15m
Talk
Are Prompt Engineering and TODO Comments Friends or Foes? An Evaluation on GitHub Copilot
Research Track
David OBrien Iowa State University, Sumon Biswas Carnegie Mellon University, Sayem Mohammad Imtiaz Iowa State University, Rabe Abdalkareem Omar Al-Mukhtar University, Emad Shihab Concordia University, Hridesh Rajan Iowa State University
11:45
15m
Talk
Automatic Semantic Augmentation of Language Model Prompts (for Code Summarization)
Research Track
Toufique Ahmed University of California at Davis, Kunal Suresh Pai UC Davis, Prem Devanbu University of California at Davis, Earl T. Barr University College London
DOI Pre-print
12:00
15m
Talk
DSFM: Enhancing Functional Code Clone Detection with Deep Subtree Interactions
Research Track
Zhiwei Xu Tsinghua University, Shaohua Qiang Tsinghua University, Dinghong Song Tsinghua University, Min Zhou Tsinghua University, Hai Wan Tsinghua University, Xibin Zhao Tsinghua University, Ping Luo Tsinghua University, Hongyu Zhang Chongqing University
12:15
15m
Talk
Machine Learning is All You Need: A Simple Token-based Approach for Effective Code Clone Detection
Research Track
Siyue Feng Huazhong University of Science and Technology, Wenqi Suo Huazhong University of Science and Technology, Yueming Wu Nanyang Technological University, Deqing Zou Huazhong University of Science and Technology, Yang Liu Nanyang Technological University, Hai Jin Huazhong University of Science and Technology
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