ICSE 2024
Fri 12 - Sun 21 April 2024 Lisbon, Portugal
Thu 18 Apr 2024 11:00 - 11:15 at Pequeno Auditório - LLM, NN and other AI technologies 3 Chair(s): Tushar Sharma

Large-scale cloud systems play a pivotal role in modern IT infrastructure. However, incidents occurring within these systems can lead to service disruptions and adversely affect user experience. To swiftly resolve such incidents, on-call engineers depend on crafting domain-specific language (DSL) queries to analyze telemetry data. However, writing these queries can be challenging and time-consuming. This paper presents a thorough empirical study on the utilization of queries of XQL, a DSL employed for incident management in a large-scale cloud management system at CompanyX. The findings obtained underscore the importance and viability of XQL queries recommendation to enhance incident management.

Building upon these valuable insights, we introduce Xpert, an end-to-end machine learning framework that automates XQL recommendation process. By leveraging historical incident data and large language models, Xpert generates customized XQL queries tailored to new incidents. Furthermore, Xpert incorporates a novel performance metric called Xcore, enabling a thorough evaluation of query quality from three comprehensive perspectives. We conduct extensive evaluations of Xpert, demonstrating its effectiveness in offline settings. Notably, we deploy Xpert in the real production environment of a large-scale incident management system in CompanyX, validating its efficiency in supporting incident management. To the best of our knowledge, this paper represents the first empirical study of its kind, and Xpert stands as a pioneering XQL recommendation framework designed for incident management.

Thu 18 Apr

Displayed time zone: Lisbon change

11:00 - 12:30
11:00
15m
Talk
Xpert: Empowering Incident Management with Query Recommendations via Large Language Models
Research Track
Yuxuan Jiang University of Michigan Ann-Arbor, Chaoyun Zhang Microsoft, Shilin He Microsoft Research, Zhihao Yang Peking University, Minghua Ma Microsoft Research, Si Qin Microsoft Research, Yu Kang Microsoft Research, Yingnong Dang Microsoft Azure, Saravan Rajmohan Microsoft 365, Qingwei Lin Microsoft, Dongmei Zhang Microsoft Research
11:15
15m
Talk
Tensor-Aware Energy Accounting
Research Track
Timur Babakol SUNY Binghamton, USA, Yu David Liu SUNY Binghamton
DOI Pre-print
11:30
15m
Talk
LLM4PLC: Harnessing Large Language Models for Verifiable Programming of PLCs in Industrial Control Systems
Software Engineering in Practice
Mohamad Fakih University of California, Irvine, Rahul Dharmaji University of California, Irvine, Yasamin Moghaddas University of California, Irvine, Gustavo Quiros Siemens Technology, Tosin Ogundare Siemens Technology, Mohammad Al Faruque UCI
11:45
15m
Talk
Resolving Code Review Comments with Machine Learning
Software Engineering in Practice
Alexander Frömmgen Google, Jacob Austin Google, Peter Choy Google, Nimesh Ghelani Google, Lera Kharatyan Google, Gabriela Surita Google, Elena Khrapko Google, Pascal Lamblin Google, Pierre-Antoine Manzagol Google, Marcus Revaj Google, Maxim Tabachnyk Google, Danny Tarlow Google, Kevin Villela Google, Dan Zheng Google DeepMind, Satish Chandra Google, Inc, Petros Maniatis Google DeepMind
12:00
15m
Talk
LLMs Still Can't Avoid Instanceof: An investigation Into GPT-3.5, GPT-4 and Bard's Capacity to Handle Object-Oriented Programming Assignments
Software Engineering Education and Training
Bruno Pereira Cipriano Lusófona University, COPELABS, Pedro Alves Lusófona University, COPELABS
12:15
7m
Talk
Leveraging Large Language Models to Improve REST API Testing
New Ideas and Emerging Results
Myeongsoo Kim Georgia Institute of Technology, Tyler Stennett Georgia Institute of Technology, Dhruv Shah Georgia Institute of Technology, Saurabh Sinha IBM Research, Alessandro Orso Georgia Institute of Technology
Pre-print
12:22
7m
Talk
LogExpert: Log-based Recommended Resolutions Generation using Large Language Model
New Ideas and Emerging Results
JiaboWang Beijing University of Posts and Telecommunications, guojun chu Beijing University of Posts and Telecommunications, Jingyu Wang , Haifeng Sun Beijing University of Posts and Telecommunications, Qi Qi , Yuanyi Wang Beijing University of Posts and Telecommunications, Ji Qi China Mobile (Suzhou) Software Technology Co., Ltd., Jianxin Liao Beijing University of Posts and Telecommunications
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