This program is tentative and subject to change.

Wed 17 Jul 2024 16:00 - 16:18 at Baobá 8 - Fault Diagnosis and Root Cause Analysis 1

Fault Localization (FL), in which a developer seeks to identify which part of the code is malfunctioning and needs to be fixed, is a recurring challenge in debugging. To reduce developer burden, many automated FL techniques have been proposed. However, prior work has noted that existing techniques fail to provide rationales for the suggested locations, hindering developer adoption of these techniques. With this in mind, we propose AutoFL, a Large Language Model (LLM)-based FL technique that generates an explanation of the bug along with a suggested fault location. AutoFL prompts an LLM to use function calls to navigate a repository, so that it can effectively localize faults over a large software repository and overcome the limit of the LLM context length. Extensive experiments on 798 real-world bugs in Java and Python reveal AutoFL improves method-level acc@1 by up to 233.3% over baselines. Furthermore, developers were interviewed on their impression of AutoFL-generated explanations, showing that developers generally liked the natural language explanations of AutoFL, and that they preferred reading a few, high-quality explanations instead of many.

This program is tentative and subject to change.

Wed 17 Jul

Displayed time zone: Brasilia, Distrito Federal, Brazil change

16:00 - 18:00
Fault Diagnosis and Root Cause Analysis 1Research Papers / Industry Papers at Baobá 8
16:00
18m
Talk
A Quantitative and Qualitative Evaluation of LLM-based Explainable Fault Localization
Research Papers
Sungmin Kang Korea Advanced Institute of Science and Technology, Gabin An Korea Advanced Institute of Science and Technology, Shin Yoo Korea Advanced Institute of Science and Technology
Pre-print
16:18
18m
Talk
BARO: Robust Root Cause Analysis for Microservices via Multivariate Bayesian Online Change Point Detection
Research Papers
Luan Pham RMIT University, Huong Ha RMIT University, Hongyu Zhang Chongqing University
Pre-print
16:36
18m
Talk
Fault Diagnosis for Test Alarms in Microservices Through Multi-source Data
Industry Papers
Shenglin Zhang Nankai University, Jun Zhu Nankai University, Bowen Hao Nankai University, Yongqian Sun Nankai University, Xiaohui Nie CNIC, CAS, Jingwen Zhu Nankai University, Xilin Liu Huawei Cloud, Xiaoqian Li Huawei Cloud, Yuchi Ma Huawei Cloud Computing Technologies CO., LTD., Dan Pei Tsinghua University
16:54
18m
Talk
Costs and Benefits of Machine Learning Software Defect Prediction: Industrial Case Study
Industry Papers
Szymon Stradowski Wroclaw University of Science and Technology & NOKIA, Lech Madeyski Wroclaw University of Science and Technology
17:12
18m
Talk
Chain-of-Event: Interpretable Root Cause Analysis for Microservices through Automatically Learning Weighted Event Causal Graph
Industry Papers
Yao Zhenhe Tsinghua University, Changhua Pei Computer Network Information Center at Chinese Academy of Sciences, Wenxiao Chen Tsinghua University, Hanzhang Wang Walmart Global Tech, Liangfei Su eBay, USA, Huai Jiang eBay, USA, Zhe Xie Tsinghua University, Xiaohui Nie CNIC, CAS, Dan Pei Tsinghua University
17:30
18m
Talk
ChangeRCA: Finding Root Causes from Software Changes in Large Online Systems
Research Papers
Guangba  Yu Sun Yat-sen University, Pengfei Chen Sun Yat-sen University, Zilong He Sun Yat-sen University, Qiuyu Yan Tencent, Yu Luo Tencent, Fangyuan Li Tencent, Zibin Zheng Sun Yat-sen University
DOI Pre-print