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Thu 1 May 2025 11:45 - 12:00 at 212 - AI for Analysis 3 Chair(s): Gias Uddin
Fri 2 May 2025 17:15 - 17:30 at 215 - SE for AI with Quality 3 Chair(s): Sumon Biswas

We present SEMANTIC CODE FINDER, a framework for semantic code search that delivers high-level search performance and supports multiple programming languages. Leveraging code summaries, it enables meaningful semantic code search by extracting the core content of code methods and using this information for search queries. Evaluated on large-scale codebases, SEMANTIC CODE FINDER demonstrates its effectiveness in outperforming existing open-source code search tools, achieving higher recall and precision rates. It delivers superior search performance across Java, Python, and C++. Notably, SEMANTIC CODE FINDER outperforms CodeMatcher, a previously successful semantic code search tool, by approximately 41% in terms of MRR. Moreover, it shows consistent performance across Java, Python, and C++ languages, highlighting its robustness and effectiveness. Currently, it is being used as a code search service for a significant amount of source code within Samsung Electronics, meeting the needs of its developers.

Thu 1 May

Displayed time zone: Eastern Time (US & Canada) change

11:00 - 12:30
AI for Analysis 3SE In Practice (SEIP) / Research Track at 212
Chair(s): Gias Uddin York University, Canada
11:00
15m
Talk
COCA: Generative Root Cause Analysis for Distributed Systems with Code Knowledge
Research Track
Yichen LI The Chinese University of Hong Kong, Yulun Wu The Chinese University of Hong Kong, Jinyang Liu Chinese University of Hong Kong, Zhihan Jiang The Chinese University of Hong Kong, Zhuangbin Chen Sun Yat-sen University, Guangba  Yu The Chinese University of Hong Kong, Michael Lyu The Chinese University of Hong Kong
11:15
15m
Talk
Enhancing Code Generation via Bidirectional Comment-Level Mutual Grounding
Research Track
Yifeng Di Purdue University, Tianyi Zhang Purdue University
11:30
15m
Talk
HumanEvo: An Evolution-aware Benchmark for More Realistic Evaluation of Repository-level Code Generation
Research Track
Dewu Zheng Sun Yat-sen University, Yanlin Wang Sun Yat-sen University, Ensheng Shi Xi’an Jiaotong University, Ruikai Zhang Huawei Cloud Computing Technologies, Yuchi Ma Huawei Cloud Computing Technologies, Hongyu Zhang Chongqing University, Zibin Zheng Sun Yat-sen University
11:45
15m
Talk
SEMANTIC CODE FINDER: An Efficient Semantic Search Framework for Large-Scale Codebases
SE In Practice (SEIP)
daeha ryu Innovation Center, Samsung Electronics, Seokjun Ko Samsung Electronics Co., Eunbi Jang Innovation Center, Samsung Electronics, jinyoung park Innovation Center, Samsung Electronics, myunggwan kim Innovation Center, Samsung Electronics, changseo park Innovation Center, Samsung Electronics
12:00
15m
Talk
Time to Retrain? Detecting Concept Drifts in Machine Learning Systems
SE In Practice (SEIP)
Tri Minh-Triet Pham Concordia University, Karthikeyan Premkumar Ericsson, Mohamed Naili Ericsson, Jinqiu Yang Concordia University
12:15
15m
Talk
UML Sequence Diagram Generation: A Multi-Model, Multi-Domain Evaluation
SE In Practice (SEIP)
Chi Xiao Ericsson AB, Daniel Ståhl Ericsson AB, Jan Bosch Chalmers University of Technology

Fri 2 May

Displayed time zone: Eastern Time (US & Canada) change

16:00 - 17:30
SE for AI with Quality 3Research Track / SE In Practice (SEIP) at 215
Chair(s): Sumon Biswas Case Western Reserve University
16:00
15m
Talk
Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable ClassificationSE for AIArtifact-Available
Research Track
Sigma Jahan Dalhousie University, Mehil Shah Dalhousie University, Parvez Mahbub Dalhousie University, Masud Rahman Dalhousie University
Pre-print
16:15
15m
Talk
Lightweight Concolic Testing via Path-Condition Synthesis for Deep Learning LibrariesSE for AIArtifact-FunctionalArtifact-AvailableArtifact-Reusable
Research Track
16:30
15m
Talk
Mock Deep Testing: Toward Separate Development of Data and Models for Deep LearningSE for AI
Research Track
Ruchira Manke Tulane University, USA, Mohammad Wardat Oakland University, USA, Foutse Khomh Polytechnique Montréal, Hridesh Rajan Tulane University
16:45
15m
Talk
RUG: Turbo LLM for Rust Unit Test GenerationSE for AI
Research Track
Xiang Cheng Georgia Institute of Technology, Fan Sang Georgia Institute of Technology, Yizhuo Zhai Georgia Institute of Technology, Xiaokuan Zhang George Mason University, Taesoo Kim Georgia Institute of Technology
Pre-print Media Attached File Attached
17:00
15m
Talk
Test Input Validation for Vision-based DL Systems: An Active Learning ApproachArtifact-AvailableArtifact-FunctionalArtifact-ReusableSE for AI
SE In Practice (SEIP)
Delaram Ghobari University of Ottawa, Mohammad Hossein Amini University of Ottawa, Dai Quoc Tran SmartInsideAI Company Ltd. and Sungkyunkwan University, Seunghee Park SmartInsideAI Company Ltd. and Sungkyunkwan University, Shiva Nejati University of Ottawa, Mehrdad Sabetzadeh University of Ottawa
Pre-print
17:15
15m
Talk
SEMANTIC CODE FINDER: An Efficient Semantic Search Framework for Large-Scale Codebases
SE In Practice (SEIP)
daeha ryu Innovation Center, Samsung Electronics, Seokjun Ko Samsung Electronics Co., Eunbi Jang Innovation Center, Samsung Electronics, jinyoung park Innovation Center, Samsung Electronics, myunggwan kim Innovation Center, Samsung Electronics, changseo park Innovation Center, Samsung Electronics
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