Internetware 2023
Fri 4 - Sun 6 August 2023 Hangzhou, China

Internetware 2023, the 14th Asia-Pacific Symposium on Internetware, provides a forum for researchers and practitioners to discuss the trending software technologies in the Internet era. Internetware 2023 will be held August 4-6 in Hangzhou, China.

Internetware 2023 Keynote

Keynote1: Toward a Better Software Future - Prof. Zhendong Su

Abstract: The key mission of computer science is to help people construct reliable, performant, and usable software. To this end, there have been substantial conceptual, technological, and practical advances for engineering high-quality software—we have better processes, languages, compilers, and development tools. On the other hand, the fundamental processes and toolchains are not significantly different from those in the early days of the field. In this talk, I will share some reflections on how we may advance the science and practice of engineering software. The talk highlights several areas and directions that I believe are critical and offer promising opportunities for significantly moving our field forward.

Zhendong Su Bio: Zhendong Su is a Professor in the Department of Computer Science at ETH Zurich. He had previously been a full professor in Computer Science and a Chancellor’s Fellow at UC Davis. He is passionate about fundamental and practical innovations for building software. His research spans programming languages and compilers, software engineering, computer security, deep learning, and education technologies. He served on the steering committees of ISSTA and ESEC/FSE, served as an Associate Editor for ACM TOSEM, co-chaired SAS 2009, program chaired ISSTA 2012, and program co-chaired SIGSOFT FSE 2016. He is a Member of the Academia Europaea, and a Fellow of the ACM and of the IEEE.

Keynote2: From Software Analytics to Cloud Intelligence – Reflection and Path Forward - Prof. Dongmei Zhang

Abstract: Software Analytics focuses on utilizing data-driven approaches to help improve the quality of software systems, the user experience of interacting with software systems, and the productivity of software development processes. Software Analytics is an important research area in the software engineering community for more than a decade. It has already made a broad impact in the software industry. As the computing paradigm was shifting towards cloud computing, we started to focus our Software Analytics research on cloud computing and created the research topic Cloud Intelligence. Cloud Intelligence targets to innovate AI/ML technologies to help design, build, and operate high-quality and high-efficiency cloud systems at scale. Due to the distributed nature, great complexity, and enormous scale of cloud systems, Cloud Intelligence presents unique challenges and opportunities to Software Analytics research. In this talk, I will first introduce the research landscape of software analytics and Cloud Intelligence. Then using a couple of projects as examples, I will talk about our research on Cloud Intelligence and its impact, as well as our experiences working with product teams on joint innovations across Microsoft. I will also discuss the research challenges and opportunities in Cloud Intelligence moving forward.

Dongmei Zhang Bio: Dr. Dongmei Zhang is a Distinguished Scientist in Microsoft. She worked in Microsoft Research Asia (MSRA) for 18 years. Now she is the Chief Scientist of Microsoft Software Technology Center Asia (STCA), leading the research areas of data intelligence, knowledge computing, information visualization, and software engineering. Dr. Zhang founded the Software Analytics Group in MSRA in 2009. Since then, she has been leading the group to research software analytics technologies. Her group collaborates closely with many product teams across Microsoft and has developed and deployed software analytics tools that have created significant business impact. In recent years, Dr. Zhang and her teams have expanded the research and impact into the business intelligence area, and helped Microsoft products establish technology leadership in the direction of Smart Data Discovery. Dr. Zhang holds a Ph.D. degree in Robotics from the Robotics Institute, Carnegie Mellon University.

Keynote3: Massive-Scale Smart City Deployments - Prof. Abdelsalam (Sumi) Helal

Abstract: Recent advances in IoT and pervasive and ubiquitous computing provide a glimpse into the future of our planet and reveal exciting visions of smart many things: smart cities, smart homes, smart cars, in addition to smart spaces such as malls, workplaces, hotels, schools, and much more. Driven by a technological revolution offering low power many things, wireless almost everything, and ubiquitous computation/intelligence everywhere, even at the edge of the network, we could envision and prototype impressive smart space systems that improve quality of life, enhance awareness of resources and the environment, and enrich users’ experience. But prototyping is one thing; actual large-scale deployments are another. The massive scale of sensors and IoT devices that will be deployed in highly populated smart cities of the future will be mind-bugling. Without a carefully thought ecosystem and a scalable Internetware infrastructure in place, it will be extremely difficult to manage or program such an expanding and massive deployments. In this talk, I will present our recent work on Cloud-Edge-Beneath (CEB) architecture and delineate the role of edge intelligence in achieving scalability. I will introduce the concept of sentience-efficiency (a new paradigm for realizing aggressive energy savings while minimizing demands on computational resources) and show how it is paramount to energy-efficiency. I will then present CEB’s bi-directional waterfall optimization framework and show how it supports sentience-efficiency. Addressing implementational issues, I will present an event-driven programming model specific to CEB and show how the way we program large-scale deployments impacts its scalability. I will present a validation study demonstrating CEB’s scaling behavior in face of smart city deployment expansions in terms of IoT devices and sensors (horizontal growth) as well as smart city applications proliferation and increased adoption (vertical growth). Finally, I will present our ongoing project in which edge intelligence can additionally exploits deep reinforcement learning towards the same goals set forth by CEB.

Abdelsalam (Sumi) Helal Bio: Sumi Helal is a Professor in the Computer & Information Science and Engineering Department at the University of Florida, USA, and Director of its Mobile and Pervasive Computing Laboratory. He co-founded and directed the Gator Tech Smart House, a real-world deployment project that aimed at identifying key barriers and opportunities to make the Smart Home concept a common place (creating the “Smart Home in a Box” concept). His active areas of research focus on architectural and programmability aspects of the Internet of Things (IoT), IoT architectures, IoT edge intelligence, and pervasive/ubiquitous systems and their human-centric applications, especially in the Digital Health area. Helal is also a technologist at heart who founded several successful ventures in the areas of IoT and Digital Health. His patents that came out of his research were licensed by the top multinational tech industry including Google, Apple, Samsung, Bosch, T-Mobile, Verizon, among others. Helal is a Fellow of the ACM, IEEE, AAAS, AAIA, and IET, and a member of Academia Europaea. He can be contacted at: helal@acm.org

Dates
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Sat 5 Aug

Displayed time zone: Beijing, Chongqing, Hong Kong, Urumqi change

09:00 - 09:15
09:00
15m
Day opening
Opening
Main Track

10:30 - 11:00
10:30
30m
Coffee break
Tea Break
Main Track

11:00 - 12:15
11:00
75m
Keynote
Keynote 2: Toward a Better Software Future
Main Track
Zhendong Su ETH Zurich
12:15 - 13:30
12:15
75m
Lunch
Lunch
Main Track

13:30 - 14:40
Session 1: Code IntelligenceMain Track at Main Conference Room
13:30
15m
Research paper
Structural-semantics Guided Program Simplification for Understanding Neural Code Intelligence Models
Main Track
Chaoxuan Shi , Tingwei Zhu Nanjing University, Tian Zhang Nanjing University, Jun Pang University of Luxembourg, Minxue Pan Nanjing University
13:45
15m
Research paper
Hybrid API Migration: A Marriage of Small API Mapping Models and Large Language Models
Main Track
Bingzhe Zhou , Xinying Wang , Shengbin Xu Nanjing University, Yuan Yao Nanjing University, Minxue Pan Nanjing University, Feng Xu Nanjing University, Xiaoxing Ma Nanjing University
14:00
15m
Research paper
Towards Better Multilingual Code Search through Cross-Lingual Contrastive Learning
Main Track
Xiangbing Huang , Yingwei Ma Nanjing University of Aeronautics and Astronautics, Haifang Zhou National University of Defense Technology, Zhijie Jiang National University of Defense Technology, Yuanliang Zhang , Teng Wang National University of Defense Technology, Shanshan Li National University of Defense Technology
14:15
15m
Research paper
PyBartRec: Python API Recommendation with Semantic Information
Main Track
14:40 - 15:50
Session 2: Debugging & Bug ManagementMain Track at Main Conference Room
14:40
15m
Research paper
SupConFL: Fault Localization with Supervised Contrastive Learning
Main Track
Wei Chen Southwest University, Wu Chen Southwest University, Jiamou Liu The University of Auckland, Kaiqi Zhao The University of Auckland, Mingyue Zhang Southwest University
14:55
15m
Research paper
Effective Recommendation of Cross-Project Correlated Issues based on Issue Metrics
Main Track
Hao Ren Department of Computer Science and Technology, Nanjing University, Mingliang Ma , Xiaowei Zhang , Yulu Cao , Changhai Nie Nanjing University, Yanhui Li Nanjing University
15:10
15m
Research paper
The Impact of the Bug Number on Effort-Aware Defect Prediction: An Empirical Study
Main Track
Peixin Yang , Lin Zhu , Wenhua Hu , Jacky Keung City University of Hong Kong, Liping Lu , Jianwen Xiang
15:25
15m
Research paper
Can Neural Networks Help Smart Contract Testing? An Empirical Study
Main Track
Jiadong Wu School of Software Engineering, Sun Yat-sen University, Yanlin Wang Sun Yat-sen University, Ruixin Wang Purdue University, Jiachi Chen Sun Yat-sen University, Zibin Zheng Sun Yat-sen University
15:50 - 16:10
15:50
20m
Coffee break
Tea Break 2
Main Track

16:10 - 17:20
Session 3: Software Ecosystem Main Track at Main Conference Room
16:10
15m
Research paper
Towards Better Dependency Scope Settings in Maven Projects
Main Track
Haolin Yang , Lin Chen Nanjing University, Yulu Cao , Yanhui Li , Yuming Zhou Nanjing University
16:25
15m
Research paper
An Empirical Study of the Apache Voting Process on Open Source Community Governance
Main Track
Jisheng Wang , Lingfeng Bao Zhejiang University, Chao Ni Zhejiang University
16:40
15m
Research paper
A Deep Dive into the Featured iOS Apps
Main Track
Liu Wang Beijing University of Posts and Telecommunications, Haoyu Wang Huazhong University of Science and Technology, Huiyi Wang Beijing University of Posts and Telecommunications, Li Li Beihang University, Yi Wang
16:55
15m
Research paper
A Fine-Grained Evaluation of Mutation Operators for Deep Learning Systems: A Selective Mutation Approach
Main Track
Yichun Wang , Zhiyi Zhang Nanjing University of Aeronautics and Astronautics, Yongming Yao , Zhiqiu Huang Nanjing University of Aeronautics and Astronautics
18:00 - 21:00
18:00
3h
Dinner
Banquet
Main Track

Sun 6 Aug

Displayed time zone: Beijing, Chongqing, Hong Kong, Urumqi change

10:30 - 11:00
10:30
30m
Coffee break
Tea Break 3
Main Track

11:00 - 12:10
Session 5: Vulnerability Detection & Management Main Track at Main Conference Room
11:00
15m
Research paper
Prompt Learning for Developing Software Exploits
Main Track
Xiaoming Ruan Wuhan University, Yaoxiang Yu Wuhan University, mawenhao , Bo Cai Wuhan University
11:15
15m
Research paper
FAEG: Feature-Driven Automatic Exploit Generation
Main Track
Peng Xu , Liangze Yin National University of Defense Technology, Jiantong Ma , Dong Yang , Wei Dong
11:30
15m
Research paper
Comparing the Performance of Different Code Representations for Learning-based Vulnerability Detection
Main Track
Yuting Zhang , Jiahao Zhu , Yixin Yang , Ming Wen Huazhong University of Science and Technology, Hai Jin Huazhong University of Science and Technology
11:45
15m
Research paper
VulD-Transformer: Source Code Vulnerability Detection via Transformer
Main Track
Xuejun Zhang Lanzhou Jiaotong University, Fenghe Zhang Lanzhou Jiaotong University, Bo Zhao , Bo Zhou Lanzhou Jiaotong University, Boyang Xiao
12:15 - 13:30
12:15
75m
Lunch
Lunch 2
Main Track

13:30 - 14:40
Session 6: Testing Main Track at Main Conference Room
13:30
15m
Research paper
Prioritizing Testing Instances to Enhance the Robustness of Object Detection Systems
Main Track
Shihao Weng Nanjing University, Yang Feng Nanjing University, Yining Yin Nanjing University, China, Jia Liu Nanjing University
13:45
15m
Research paper
Practical Accuracy Evaluation for Deep Learning Systems via Latent Representation Discrepancy
Main Track
Yining Yin Nanjing University, China, Yang Feng Nanjing University, Zixi Liu Nanjing University, Zhihong Zhao
14:00
15m
Research paper
An Empirical Study on AST-level mutation-based fuzzing techniques for JavaScript Engines
Main Track
Song Tang College of Intelligence and Computing, Tianjin University, Shuang Liu Tianjin University, Junjie Wang College of Intelligence and Computing, Tianjin University, Xiangwei Zhang College of Intelligence and Computing, Tianjin University
14:15
15m
Research paper
DRIFT: Fine-Grained Prediction of the Co-Evolution of Production and Test Code via Machine Learning
Main Track
Lei Liu , Sinan Wang , Yepang Liu Southern University of Science and Technology, Jinliang Deng , Sicen Liu
14:40 - 15:50
Session 7: Code Search & Generation Main Track at Main Conference Room
14:40
15m
Research paper
Seq2Seq or Seq2Tree: Generating Code Using Both Paradigms via Mutual Learning
Main Track
Yunfei Zhao Peking University, Yihong Dong Peking University, Ge Li Peking University
14:55
15m
Research paper
Measuring Efficient Code Generation with GEC
Main Track
Yue Pan , Chen Lyu Shandong Normal University
15:10
15m
Research paper
APICom: Automatic API Completion via Prompt Learning and Adversarial Training-based Data Augmentation
Main Track
yafeng gu , YihengShen , Xiang Chen Nantong University, ShaoYu Yang School of Information Science and Technology, Nantong University, Yiling Huang , Zhixiang Cao
Pre-print
15:25
15m
Research paper
MCodeSearcher Multi-View Contrastive Learning for Code Search
Main Track
Jia Li Peking University, Fang Liu Beihang University, Yunfei Zhao Peking University, Ge Li Peking University, Zhi Jin Peking University
15:50 - 16:10
15:50
20m
Coffee break
Tea Break 4
Main Track

16:10 - 17:20
Session 8: Software SystemsMain Track at Main Conference Room
16:10
15m
Research paper
MiTFM: A multi-view information fusion method based on transformer for Next Activity Prediction of Business Processes
Main Track
Jiaxing Wang Zhejiang University of Technology, Chengliang Lu Zhejiang University of Technology, Bin Cao Zhejiang University of Technology, Jing Fang Zhejiang University of Technology
16:25
15m
Research paper
EFTuner: A Bi-Objective Configuration Parameter Auto-Tuning Method Towards Energy-Efficient Big Data Processing
Main Track
Hui Dou Anhui University, Xing Wei Anhui University, Kang Wang Anhui University, Yiwen Zhang Anhui University, Pengfei Chen Sun Yat-Sen University, Yuee Huang Wannan Medical College
16:40
15m
Research paper
Conflict-free Replicated Priority Queue: Design, Verification and Evaluation
Main Track
16:55
15m
Research paper
Isabelle/Cloud: Delivering Isabelle/HOL as a Cloud IDE for Theorem Proving
Main Track
17:20 - 17:30
17:20
10m
Day closing
Closing
Main Track

Not scheduled yet

Not scheduled yet
Meeting
未来软件工程与系统软件论坛
Main Track

Accepted Papers

Title
A Deep Dive into the Featured iOS Apps
Main Track
A Fine-Grained Evaluation of Mutation Operators for Deep Learning Systems: A Selective Mutation Approach
Main Track
An Empirical Study of the Apache Voting Process on Open Source Community Governance
Main Track
An Empirical Study on AST-level mutation-based fuzzing techniques for JavaScript Engines
Main Track
APICom: Automatic API Completion via Prompt Learning and Adversarial Training-based Data Augmentation
Main Track
Pre-print
Can Neural Networks Help Smart Contract Testing? An Empirical Study
Main Track
Comparing the Performance of Different Code Representations for Learning-based Vulnerability Detection
Main Track
Conflict-free Replicated Priority Queue: Design, Verification and Evaluation
Main Track
DRIFT: Fine-Grained Prediction of the Co-Evolution of Production and Test Code via Machine Learning
Main Track
Effective Recommendation of Cross-Project Correlated Issues based on Issue Metrics
Main Track
EFTuner: A Bi-Objective Configuration Parameter Auto-Tuning Method Towards Energy-Efficient Big Data Processing
Main Track
FAEG: Feature-Driven Automatic Exploit Generation
Main Track
Fine-Grained Flow Control Agent on Path MTU for IoT Software
Main Track
Hybrid API Migration: A Marriage of Small API Mapping Models and Large Language Models
Main Track
Isabelle/Cloud: Delivering Isabelle/HOL as a Cloud IDE for Theorem Proving
Main Track
MCodeSearcher Multi-View Contrastive Learning for Code Search
Main Track
Measuring Efficient Code Generation with GEC
Main Track
MiTFM: A multi-view information fusion method based on transformer for Next Activity Prediction of Business Processes
Main Track
Practical Accuracy Evaluation for Deep Learning Systems via Latent Representation Discrepancy
Main Track
Prioritizing Testing Instances to Enhance the Robustness of Object Detection Systems
Main Track
Prompt Learning for Developing Software Exploits
Main Track
PyBartRec: Python API Recommendation with Semantic Information
Main Track
Seq2Seq or Seq2Tree: Generating Code Using Both Paradigms via Mutual Learning
Main Track
Structural-semantics Guided Program Simplification for Understanding Neural Code Intelligence Models
Main Track
SupConFL: Fault Localization with Supervised Contrastive Learning
Main Track
The Impact of the Bug Number on Effort-Aware Defect Prediction: An Empirical Study
Main Track
Towards Better Dependency Scope Settings in Maven Projects
Main Track
Towards Better Multilingual Code Search through Cross-Lingual Contrastive Learning
Main Track
UbiCap: A Capability-based Run-time Model for Heterogeneous Sensors Management in Ubiquitous Operating System
Main Track
VulD-Transformer: Source Code Vulnerability Detection via Transformer
Main Track

Call for Sponsors

Internetware provides a forum for researchers and practitioners to discuss the trending software technologies in the Internet era and is attended by approximately 300 attendees yearly. These include many students and an increasing number of industry practitioners. Internetware attracts promising emerging research in the internet-relevant era and provides a multifaceted showcase.

What’s in it for you?

  • Internetware attracts excellent research in software engineering, artificial intelligence and internet-relevant area.
  • Networking opportunities
  • Industry collaboration
  • Be up to date with the latest research and findings
  • Opportunity to recruit soon to be graduating top software engineering students

Please view the opportunities outlined in the prospectus or contact us to discuss a bespoke opportunity to suit your organization and your budget!

Contact Details:

Lingfeng Bao,
Internetware2023 Local arrangement co-chairs
lingfengbao@zju.edu.cn

Call for Papers

With the Internetware paradigm, the software is architected like the Internet, developed with the Internet, operated on the Internet, and provided as services via the Internet. In the open, dynamic, and constantly changing environment of the Internet, Internetware systems need to be autonomous, cooperative, situational, evolvable, emergent, and trustworthy. These requirements pose special challenges for software technologies to support the construction, deployment, and use of software applications based on the Internet that not only consists of computers, but also of things and people.

This symposium aims to provide an interactive forum where researchers and professionals from multiple disciplines and domains meet and exchange ideas to explore and address the challenges brought by Internetware.

Internetware 2023 will be held August 4-6 in Hangzhou, China. We solicit submissions describing original and unpublished results of theoretical, empirical, conceptual, and experimental software engineering research related to Internetware. Topics of interest include but are not limited to:

  • Novel software paradigm for Internetware
  • Modeling and implementation of Internetware
  • Requirements engineering for Internetware
  • Software analysis, verification and testing
  • Mining software repositories
  • Software dependability, trustworthiness and confidence
  • Software architecture and design
  • Crowd-based methods, techniques and tools for Internetware
  • Socio-technical models and techniques
  • Software ecosystem practices and experiences
  • Software models and techniques for Internet-based systems such as Cloud Computing, Service Computing, Social commputing, Mobile Internet, Internet of Things, and Cyber-Physical Systems
  • Software engineering for/with Big data
  • Software engineering for/with Artificial Intelligence

How to Submit

All submissions must not exceed 10 pages for all text, figures, tables, and references. All submissions must be in English and in PDF format. Submissions that do not comply with the above instructions will be desk rejected without review. Please use the ACM Master article template, as can be obtained from the ACM Proceedings Template pages.

Submissions to internetware 2023 conference that meet the above requirements can be made via the internetware 2023 submission site (https://internetware2023.hotcrp.com) by the submission deadline. We encourage the authors to upload their paper info early (and can submit the PDF later) to properly enter conflicts for double-anonymous reviewing.

Review and Evaluation Criteria

The Internetware 2023 conference will employ a double-anonymous review process. Thus, no submission may reveal its authors’ identities. The authors must make every effort to honor the double-anonymous review process. In particular:

  • Authors’ names must be omitted from the submission.
  • All references to the author’s prior work should be in the third person.
  • While authors have the right to upload preprints on ArXiV or similar sites, they should avoid specifying that the manuscript was submitted to Internetware 2023.
  • During review, authors should not publicly use the submission title.

Internetware 2023 will follow the ACM SIGSOFT rules on Conflicts of Interest and Confidentiality of Submissions, and all authors, reviewers, organizers are expected to uphold the ACM Code of Conduct.

Important Dates

The Internetware 2023 paper abstract and submission deadline has been extended for a week:

  • Abstract Deadline: April 10, 2023 April 17, 2023
  • Submission Deadline: April 15, 2023 April 22, 2023
  • Notification of Acceptance: May 29, 2023
  • Camera-Ready Version: TBD

Award

Best papers submitted to the technical research track will win Distinguished Paper Award and will be invited to be revised and extended for consideration in a special issue of the Empirical Software Engineering journal by Springer. All extended submissions will be evaluated following the guidelines of the corresponding journal. Only those satisfying all the criteria will be accepted for the journal publication.

Conference Attendance Expectation

If a submission is accepted, at least one author of the paper is required to register for Internetware 2023 and present the paper.

Publication

All authors of accepted papers of will be asked to complete an electronic ACM Copyright form and will receive further instructions for preparing their camera ready versions. All accepted contributions will be published in the Internetware 2023 electronic proceedings and in the ACM Digital Library.