This program is tentative and subject to change.
Code smells are anti-patterns that violate code understandability, re-usability, changeability, and maintainability. It is important to identify code smells and locate them in the code. For this purpose, automated detection of code smells is a sought-after feature for development tools; however, the design and evaluation of such tools depends on the quality of oracle datasets. The typical approach for creating an oracle dataset involves multiple developers independently inspecting and annotating code examples for their existing code smells. Since multiple inspectors cast votes about each code example, it is possible for the inspectors to disagree about the presence of smells. Such disagreements introduce ambiguity into how smells should be interpreted. Prior work has studied developer perceptions of code smells in traditional source code; however, smells in Infrastructure-as-Code (IaC) have not been investigated. To understand the real-world impact of disagreements among developers and their perceptions of IaC code smells, we conduct an empirical study on the oracle dataset of GLITCH—a state-of-the-art detection tool for security code smells in IaC. We analyze GLITCH’s oracle dataset for code smell issues, their types, and individual annotations of the inspectors. Furthermore, we investigate possible confounding factors associated with the incidences of developer misaligned perceptions of IaC code smells. Finally, we triangulate developer perceptions of code smells in traditional source code with our results on IaC. Our study reveals that unlike developer perceptions of smells in traditional source code, their perceptions of smells in IaC are more substantially impacted by subjective interpretation of smell types and their co-occurrence relationships. For instance, the interpretation of admins by default, empty passwords, and hard-coded secrets varies considerably among raters and are more susceptible to misidentification than other IaC code smells. Consequently, the manual identification of IaC code smells involves annotation disagreements among developers—46.3% of studied IaC code smell incidences have at least one dissenting vote among three inspectors. Meanwhile, only 1.6% of code smell incidences in traditional source code are affected by inspector bias stemming from these disagreements. Hence, relying solely on the majority voting, would not fully represent the breadth of interpretation of the IaC under scrutiny.
This program is tentative and subject to change.
Wed 19 NovDisplayed time zone: Seoul change
11:00 - 12:30 | |||
11:00 10mTalk | Automated Inline Comment Smell Detection and Repair with Large Language Models Research Papers Hatice Kübra Çağlar Bilkent University, Semih Çağlar Bilkent University, Eray Tüzün Bilkent University Pre-print | ||
11:10 10mTalk | What’s DAT Smell? Untangling and Weaving the Disjoint Assertion Tangle Test Smell Research Papers Monil Narang University of California, Irvine, Hang Du University of California at Irvine, James Jones University of California at Irvine Pre-print | ||
11:20 10mTalk | Your Build Scripts Stink: The State of Code Smells in Build Scripts Research Papers Mahzabin Tamanna North Carolina State University, Yash Chandrani North Carolina State University, Matthew Burrows North Carolina State University, Brandon Wroblewski North Carolina State University, Dominik Wermke North Carolina State University, Laurie Williams North Carolina State University | ||
11:30 10mTalk | Do Experts Agree About Smelly Infrastructure? Journal-First Track Sogol Masoumzadeh Mcgill University, Nuno Saavedra INESC-ID and IST, University of Lisbon, Rungroj Maipradit University of Waterloo, Lili Wei McGill University, João F. Ferreira INESC-ID and IST, University of Lisbon, Daniel Varro Linköping University / McGill University, Shane McIntosh University of Waterloo | ||
11:40 10mTalk | Wired for Reuse: Automating Context-Aware Code Adaptation in IDEs via LLM-Based Agent Research Papers Taiming Wang Beijing Institute of Technology, Yanjie Jiang Peking University, Chunhao Dong Beijing Institute of Technology, Yuxia Zhang Beijing Institute of Technology, Hui Liu Beijing Institute of Technology | ||
11:50 10mTalk | BinStruct: Binary Structure Recovery Combining Static Analysis and Semantics Research Papers Yiran Zhang , Zhengzi Xu Imperial Global Singapore, Zhe Lang Institute of Information Engineering, CAS, CHENGYUE LIU , Yuqiang Sun Nanyang Technological University, Wenbo Guo School of Cyber Science and Engineering, Sichuan University, Chengwei Liu Nanyang Technological University, Weisong Sun Nanyang Technological University, Yang Liu Nanyang Technological University | ||
12:00 10mTalk | SateLight: A Satellite Application Update Framework for Satellite Computing Research Papers Jinfeng Wen Beijing University of Posts and Telecommunications, Jianshu Zhao Beijing University of Posts and Telecommunications, Zixi Zhu Beijing University of Posts and Telecommunications, Xiaomin Zhang Beijing University of Posts and Telecommunications, Qi Liang Beijing University of Posts and Telecommunications, Ao Zhou Beijing University of Posts and Telecommunications, Shangguang Wang Beijing University of Posts and Telecommunications | ||
12:10 10mTalk | ComCat: Expertise-Guided Context Generation to Enhance Code Comprehension Journal-First Track Skyler Grandel Vanderbilt University, Scott Andersen National Autonomous University of Mexico, Yu Huang Vanderbilt University, Kevin Leach Vanderbilt University | ||
12:20 10mTalk | AdaptEval: A Benchmark for Evaluating Large Language Models on Code Snippet Adaptation Research Papers Tanghaoran Zhang National University of Defense Technology, Xinjun Mao National University of Defense Technology, Shangwen Wang National University of Defense Technology, Yuxin Zhao Key Laboratory of Software Engineering for Complex Systems, National University of Defense Technology, Yao Lu National University of Defense Technology, Jin Zhang Hunan Normal University, Zhang Zhang Key Laboratory of Software Engineering for Complex Systems, National University of Defense Technology, Kang Yang National University of Defense Technology, Yue Yu PengCheng Lab | ||