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ICSE 2021
Sat 22 - Sun 30 May 2021

ICSE is the premier forum for presenting and discussing the most recent and significant technical research contributions in the field of Software Engineering. We invite high quality submissions of technical research papers describing original and unpublished results of software engineering research. We welcome submissions addressing topics across the full spectrum of Software Engineering.

Technical track acceptance sent out

ICSE 2021 received 615 submissions. Of these, 13 were desk rejected for double-blind or formatting violations. The remaining 602 papers went through a thorough review process, with at least three reviewers, one meta-reviewer, and an area chair per paper. Following an online discussion, the program committee decided to accept 138 papers, including 30 conditional ones. We will announce the acceptance rate after finalizing all conditional decisions.

We are extremely grateful to the PC: They wrote over 1800 reviews, 550 meta-reviews, placed over 10,000, comments, made over 3500 edits to improve your reviews – all while also teaching, advising your students, and taking care of their families during a pandemic.

Call for Papers

ICSE is the premier forum for presenting and discussing the most recent and significant technical research contributions in the field of Software Engineering. We invite high quality submissions of technical research papers describing original and unpublished results of software engineering research. We welcome submissions addressing topics across the full spectrum of Software Engineering including but not limited to:

  • AI and software engineering
    • Search-based software engineering
    • Machine learning with and for SE
    • Recommender systems
    • Autonomic systems and self adaptation
    • Program synthesis
    • Program repair  
  • Testing and analysis
    • Software testing
    • Program analysis
    • Validation and Verification
    • Fault localization
    • Formal methods
    • Programming languages  
  • Empirical software engineering
    • Mining software repositories
    • Apps and app store Analysis
    • Software ecosystems
    • Qualitative research methods  
  • Software evolution
    • Evolution and maintenance
    • Debugging
    • Program comprehension
    • API design and evolution
    • Configuration management
    • Release engineering and DevOps
    • Software reuse
    • Refactoring
    • Reverse engineering
    • Software visualization  
  • Social aspects of software engineering
    • Human aspects of software engineering
    • Human-computer interaction
    • Distributed and collaborative software engineering
    • Agile methods and software processes
    • Software economics
    • Crowd-based software engineering
    • Ethics in software engineering  
  • Requirements, modeling, and design
    • Requirements Engineering
    • Modeling and Model-Driven Engineering
    • Software Architecture and Design
    • Tools and Environments
    • Variability and product lines
    • Parallel, Distributed, and Concurrent Systems
    • Software services  
  • Dependability
    • Software Security
    • Privacy
    • Reliability and Safety
    • Performance
    • Green and sustainable technologies
    • Embedded / cyber-physical systems
    • Mobile applications

Review Criteria

Each paper submitted to the Technical Track will be evaluated based on the following criteria:

  • Soundness: The extent to which the paper’s contributions are supported by rigorous application of appropriate research methods
  • Significance: The extent to which the paper’s contributions are important with respect to open software engineering challenges
  • Novelty: The extent to which the contribution is sufficiently original and is clearly explained with respect to the state-of-the-art
  • Verifiability: The extent to which the paper includes sufficient information to support independent verification or replication of the paper’s claimed contributions
  • Presentation: The extent to which the paper’s quality of writing meets the high standards of ICSE, including clear descriptions and explanations, adequate use of the English language, absence of major ambiguity, clearly readable figures and tables, and adherence to the formatting instructions provided below

Reviewers will carefully consider all of these criteria during the review process, and as a result, high-quality papers may vary considerably in their type of contribution. For example, one paper could provide an extensive replication of prior work while another could describe a highly novel approach supported by non-trivial experimentation or empirical analysis.

How to Submit

  • All submissions must conform to the ICSE 2021 formatting and submission instructions and must not exceed 10 pages for the main text, inclusive of all figures, tables, appendices, etc. Two more pages containing only references are permitted. All submissions must be in PDF. The page limit is strict, and it will not be possible to purchase additional pages at any point in the process (including after the paper is accepted).
  • Submissions must conform to the IEEE formatting instructions IEEE Conference Proceedings Formatting Guidelines (title in 24pt font and full text in 10pt type, LaTeX users must use \documentclass[10pt,conference]{IEEEtran} without including the compsoc or compsocconf options).
  • By submitting to the ICSE Technical Track, authors acknowledge that they are aware of and agree to be bound by the ACM Policy and Procedures on Plagiarism and the IEEE Plagiarism FAQ. In particular, papers submitted to ICSE 2021 must not have been published elsewhere and must not be under review or submitted for review elsewhere whilst under consideration for ICSE 2021. Contravention of this concurrent submission policy will be deemed a serious breach of scientific ethics, and appropriate action will be taken in all such cases. To check for double submission and plagiarism issues, the chairs reserve the right to (1) share the list of submissions with the PC Chairs of other conferences with overlapping review periods and (2) use external plagiarism detection software, under contract to the ACM or IEEE, to detect violations of these policies.
  • The ICSE 2021 Technical Track will employ a double-blind review process. Thus, no submission may reveal its authors’ identities. The authors must make every effort to honor the double-blind review process. In particular, the authors’ names must be omitted from the submission and references to their prior work should be in the third person. Further advice, guidance, and explanation about the double-blind review process can be found in the Q&A page.
  • By submitting to the ICSE Technical Track, authors acknowledge that they conform to the authorship policy of the ACM, and the authorship policy of the IEEE

Submissions to the Technical Track that meet the above requirements can be made via the Technical Track submission site (https://icse2021.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-blind reviewing.

Any submission that does not comply with these requirements may be desk rejected by the Technical Track PC Chairs without further review. ICSE 2021 is conducting a pilot with a new open source tool, the SIGSOFT Submission Checker to check conformance to the formatting and double blind guidelines. You can help us by trying out the tool yourself. If you see valid warnings you’ll know how to update your paper; if you identify places for improvement of the tool, please file an issue (or offer a pull request).

Supplementary Material

Authors are requested to adhere to the ICSE 2021 Open Science policies to the best of their abilities. To that end supplementary material can be uploaded via the HotCRP site or anonymously linked from the paper submission. Although PC members are not obligated to look at this material, we strongly encourage submitters to use supplementary material to provide access to anonymized code or data, whenever possible. Please carefully review any supplementary material to ensure it conforms to the double-blind policy (described above). For example, code and data repositories may be exported to remove version control history, scrubbed of names in comments and metadata, and anonymously uploaded to a sharing site to support review. One resource that may be helpful in accomplishing this task is this blog post.

Upon acceptance, authors have the possibility to separately submit their supplementary material to the ICSE 2021 Artifact Evaluation track, for recognition of artifacts that are reusable, available, replicated or reproduced.

Author Response Period

ICSE 2021 will offer a three day author response period. In this period the authors will have the opportunity to inspect the reviews, and answer to specific questions raised by the program committee. This period is scheduled after all reviews have been completed, and serves to inform the subsequent decision making process. Authors will be able to see the full reviews, including the reviewer scores as part of the author response process.

Withdrawing a Paper

Authors can withdraw their paper at any moment until the final decision has been made, through the paper submission system. Resubmitting a paper to another venue before the final decision has been made without withdrawing from ICSE 2021 first is considered a violation of the concurrent submission policy, and will lead to automatic rejection from ICSE 2021 as well as any other venue adhering to this policy.

Important Dates

  • Technical Track Submissions Deadline: 28 August 2020
  • Technical Track Author Response Period: 18–20 November 2020
  • Technical Track Acceptance Notification: 17 December 2020
  • Technical Track Camera Ready: 12 February 2021

Conference Attendance Expectation

If a submission is accepted, at least one author of the paper is required to register for ICSE 2021 and present the paper. The presentation is expected to be delivered in person, unless this is impossible due to travel limitations (related to, e.g., health, visa, or COVID-19 prevention).

Double Blind Frequently Asked Questions

The ICSE 2021 Technical track will adopt a double-blind review process. Further advice, guidance and explanation about the double-blind review process can be found in the Q&A page. This FAQ’s is based on guidelines for double-blind reviewing from ASE 2020 and ICSE 2020*.

Accepted Papers

Title
"Ignorance and Prejudice" in Software Fairness
Technical Track
A Case Study of Onboarding in Software Teams: Tasks and Strategies
Technical Track
A Context-based Automated Approach for Method Name Consistency Checking and Suggestion
Technical Track
A Differential Testing Approach for Evaluating Abstract Syntax Tree Mapping Algorithms
Technical Track
AID: An Automated Inclusivity-Bug Detector
Technical Track
ATVHunter: Reliable Version Detection of Third-Party Libraries for Vulnerability Identification in Android Apps
Technical Track
AUTOTRAINER: An Automatic DNN Training Problem Detection and Repair System
Technical Track
Abacus: Precise Side-Channel Analysis
Technical Track
An Empirical Analysis of UI-based Flaky Tests
Technical Track
An Empirical Assessment of Global COVID-19 Contact Tracing Applications
Technical Track
An Empirical Study of Refactorings and Technical Debt in Machine Learning Systems
Technical Track
An Empirical Study on Deployment Faults of Deep Learning Based Mobile Applications
Technical Track
An Evolutionary Study of Software Configuration Design and Implementation in Cloud Systems
Technical Track
App's Auto-Login Function Security Testing via Android OS-Level Virtualization
Technical Track
Are Machine Learning Cloud APIs Used Correctly?
Technical Track
AutoCCAG: An Automated Approach to Constrained Covering Array Generation
Technical Track
Automated Query Reformulation for Efficient Search Based on Query Logs from Stack Overflow
Technical Track
Automatic Extraction of Opinion-based Q&A from Online Developer Chats
Technical Track
Automatic Solution Summarization for Crash Bugs
Technical Track
Automatic Unit Test Generation for Machine Learning Libraries: How Far Are We?
Technical Track
Automatic Web Testing using Curiosity-Driven Reinforcement Learning
Technical Track
Automatically Matching Bug Reports With Related App Reviews
Technical Track
Bounded Exhaustive Search of Alloy Specification Repairs
Technical Track
CENTRIS: A Precise and Scalable Approach for Identifying Modified Open-Source Software Reuse
Technical Track
CHAMP: Characterizing Undesired App Behaviors from User Comments based on Market Policies
Technical Track
CURE: Code-Aware Neural Machine Translation for Automatic Program Repair
Technical Track
Can Program Synthesis be Used to Learn Merge Conflict Resolutions? An Empirical Analysis
Technical Track
Code Prediction by Feeding Trees to Transformers
Technical Track
CodeShovel: Constructing Method-Level Source Code Histories
Technical Track
Containing Malicious Package Updates in npm with a Lightweight Permission System
Technical Track
Data-Driven Synthesis of a Provably Sound Side Channel Analysis
Technical Track
Data-Oriented Differential Testing of Object-Relational Mapping Systems
Technical Track
DeepBackdoor: Black-box Backdoor Attack on Deep Learning Models through Neural Payload Injection
Technical Track
DeepLV: Suggesting Log Levels Using Ordinal Based Neural Networks
Technical Track
DeepLocalize: Fault Localization for Deep Neural Networks
Technical Track
DepOwl: Detecting Dependency Bugs to Prevent Compatibility Failures
Technical Track
Distribution-Aware Testing of Neural Networks Using Generative Models
Technical Track
Do you really code? Designing and Evaluating Screening Questions for Online Surveys with Programmers
Technical Track
Does mutation testing improve testing practices?
Technical Track
Domain-Specific Fixes for Flaky Tests with Wrong Assumptions on Underdetermined Specifications
Technical Track
Don't Do That! Hunting Down Visual Design Smells in Complex UIs against Design Guidelines
Technical Track
Early Life Cycle Software Defect Prediction. Why? How?
Technical Track
Pre-print
Efficient Compiler Autotuning via Bayesian Optimization
Technical Track
Enabling Software Resilience in GPGPU Applications via Partial Thread Protection
Technical Track
Enhancing Genetic Improvement of Software with Regression Test Selection
Technical Track
Evaluating SZZ Implementations Through a Developer-informed Oracle
Technical Track
Evaluating Unit Testing Practices in R Packages
Technical Track
Pre-print
EvoSpex: An Evolutionary Algorithm for Learning Postconditions
Technical Track
Extracting Concise Bug-Fixing Patches from Human-Written Patches in Version Control Systems
Technical Track
Extracting Rationale for Software Development Decisions—A Study of Python Email Archives
Technical Track
FLACK: Counterexample-Guided Fault Localization for Alloy Models
Technical Track
Fast Outage Analysis of Large-scale Production Clouds with Service Correlation Mining
Technical Track
Fast Parametric Model Checking through Model Fragmentation
Technical Track
Fast and Precise On-the-fly Patch Validation for All
Technical Track
Fault Localization with Code Coverage Representation Learning
Technical Track
Fine with ``1234''? An Analysis of SMS One-Time Password Randomness in Android Apps
Technical Track
FlakeFlagger: Predicting Flakiness Without Rerunning Tests
Technical Track
Fuzzing Symbolic Expressions
Technical Track
GUIGAN: Learning to Generate GUI Designs Using Generative Adversarial Networks
Technical Track
GenTree: Using Decision Trees to Learn Interactions for Configurable Software
Technical Track
Graph-based Fuzz Testing for Deep Learning Inference Engines
Technical Track
Growing A Test Corpus with Bonsai Fuzzing
Technical Track
Hero: On the Chaos When PATH Meets Modules
Technical Track
How Developers Optimize Virtual Reality Applications: A Study of Optimization Commits in Open Source Unity Projects
Technical Track
How Gamification Affects Software Developers: Cautionary Evidence from a Natural Experiment on GitHub
Technical Track
Pre-print
How to identify Boundary Conditions with Contrasty Metric?
Technical Track
IMGDroid: Detecting Image Loading Defects in Android Applications
Technical Track
IdBench: Evaluating Semantic Representations of Identifier Names in Source Code
Technical Track
Identifying Key Features from App User Reviews
Technical Track
If It’s Not Secure, It Should Not Compile: Preventing DOM-Based XSS in Large-Scale Web Development with API Hardening
Technical Track
Improving Fault Localization by Integrating Value and Predicate Based Causal Inference Techniques
Technical Track
InferCode: Self-Supervised Learning of Code Representations by Predicting Subtrees
Technical Track
Input Algebras
Technical Track
Interface Compliance of Inline Assembly: Automatically Check, Patch and Refine
Technical Track
Interpretation-enabled Software Reuse Detection Based on a Multi-Level Birthmark Model
Technical Track
IoT Bugs and Development Challenges
Technical Track
It Takes Two to Tango: Combining Visual and Textual Information for Detecting Duplicate Video-Based Bug Reports
Technical Track
JEST: N+1-version Differential Testing of Both JavaScript Engines and Specification
Technical Track
JUSTGen: Effective Test Generation for Unspecified JNI Behaviors on JVMs
Technical Track
Layout and Image Recognition Driving Cross-Platform Automated Mobile Testing
Technical Track
Leaving My Fingerprints: Motivations and Challenges of Contributing to OSS for Social Good
Technical Track
Measuring Discrimination to Boost Comparative Testing for Multiple Deep Learning Models
Technical Track
MuDelta: Delta-Oriented Mutation Testing at Commit Time
Technical Track
On Indirectly Dependent Documentation in the Context of Code Evolution: A Study
Technical Track
On the Naming of Methods: A Survey of Professional Developers
Technical Track
Onboarding vs. Diversity, Productivity and Quality -- Empirical Study of the OpenStack Ecosystem
Technical Track
Operation is the hardest teacher: estimating DNN accuracy looking for mispredictions
Technical Track
Playing Planning Poker in Crowds: Human Computation of Software Effort Estimates
Technical Track
Prioritize Crowdsourced Test Reports via Deep Screenshot Understanding
Technical Track
Prioritizing Test Inputs for Deep Neural Networks via Mutation Analysis
Technical Track
Program Comprehension and Code Complexity Metrics: An fMRI Study
Technical Track
PyART: Python API Recommendation in Real-Time
Technical Track
PyCG: Practical Call Graph Generation in Python
Technical Track
RAICC: Revealing Atypical Inter-Component Communication in Android Apps
Technical Track
Reducing DNN Properties to Enable Falsification with Adversarial Attacks
Technical Track
Relating Reading, Visualization, and Coding for New Programmers: A Neuroimaging Study
Technical Track
Representation of Developer Expertise in Open Source Software
Technical Track
Pre-print
Resource-Guided Configuration Space Reduction for Deep Learning Models
Technical Track
Restoring Execution Environments of Jupyter Notebooks
Technical Track
RobOT: Robustness-Oriented Testing for Deep Learning Systems
Technical Track
SOAR: A Synthesis Approach for Data Science API Refactoring
Technical Track
Same File, Different Changes: The Potential of Meta-Maintenance on GitHub
Technical Track
Scalable Quantitative Verification For Deep Neural Networks
Technical Track
Seamless Variability Management With the Virtual Platform
Technical Track
Self-Checking Deep Neural Networks in Deployment
Technical Track
Semantic Patches for Adaptation of JavaScript Programs to Evolving Libraries
Technical Track
Semantic Web Accessibility Testing via Hierarchical Visual Analysis
Technical Track
Semi-supervised Log-based Anomaly Detection via Probabilistic Label Estimation
Technical Track
Shipwright: A Human-in-the-Loop System for Dockerfile Repair
Technical Track
Siri, Write the Next Method
Technical Track
Smart Contract Security: a Practitioners’ Perspective
Technical Track
Studying Test Annotation Maintenance in the Wild
Technical Track
Studying the Usage of Text-To-Text Transfer Transformer to Support Code-Related Tasks
Technical Track
Supporting Quality Assurance with Automated Process-Centric Quality Constraints Checking
Technical Track
Sustainable Solving: Reducing The Memory Footprint of IFDS-Based Data Flow Analyses Using Intelligent Garbage Collection
Technical Track
Synthesizing Object State Transformers for Dynamic Software Updates
Technical Track
Technical Leverage in a Software Ecosystem: Development Opportunities and Security Risks
Technical Track
Testing Machine Translation via Referential Transparency
Technical Track
The Mind Is a Powerful Place: How Showing Code Comprehensibility Metrics Influences Code Understanding
Technical Track
The Shifting Sands of Motivation: Revisiting What Drives Contributors in Open Source
Technical Track
Too Quiet in the Library: An Empirical Study of Security Updates in Android Apps’ Native Code
Technical Track
Towards Automating Code Review Activities
Technical Track
Trace-Checking CPS Properties: Bridging the Cyber-Physical Gap
Technical Track
Traceability Transformed: Generating moreAccurate Links with Pre-Trained BERT Models
Technical Track
TransRegex: Multi-modal Regular Expression Synthesis by Generate-and-Repair
Technical Track
Understanding Bounding Functions in Safety-Critical UAV Software
Technical Track
Unrealizable Cores for Reactive Systems Specifications
Technical Track
Using Domain-specific Corpora for Improved Handling of Ambiguity in Requirements
Technical Track
Verifying Determinism in Sequential Programs
Technical Track
We’ll Fix It in Post: What Do Bug Fixes in Video Game Update Notes Tell Us?
Technical Track
What Makes a Great Maintainer of Open Source Projects?
Technical Track
What helped, and what did not? An Evaluation of the Strategies to Improve Continuous Integration
Technical Track
White-Box Analysis over Machine Learning: Modeling Performance of Configurable Systems
Technical Track
White-Box Performance-Influence Models
Technical Track
Why Security Defects Go Unnoticed during Code Reviews? A Case-Control Study of the Chromium OS Project
Technical Track
Why don’t Developers Detect Improper Input Validation?'; DROP TABLE Papers; --
Technical Track
“Do this! Do that!, And nothing will happen” Do specifications lead to securely stored passwords?
Technical Track
“How Was Your Weekend?” Software Development Teams Working From Home During COVID-19
Technical Track

ABOUT PROCEEDINGS

  • Your paper must be formatted according to the instructions at https://www.ieee.org/conferences/publishing/templates.html. Validation of the paper formatting will be part of the submission process.

  • The given page limits are strict. It is not possible to buy extra pages.

  • The list of authors (names, emails, affiliations, order) is not allowed to be changed after notification. If a correction is needed (e.g., because the author name was misspelled), the track/event chairs need to approve the change. The authors should also notify the track/event chairs if the author list in HotCRP data about the paper is not identical to the author list in initial-submission pdf (not relevant for tracks with double-blind process).

  • In the next days, you will be contacted by the publisher (IEEE CPS) with the instructions and link where to submit the camera-ready version of your paper (please do not submit it to HotCRP and wait for the instructions). As part of the process, you will also need to submit your IEEE copyright (before you complete your final paper submission). So please read the instructions carefully immediately after it arrives.

  • At least one of the paper’s authors must register to the conference no later than January 31, 2021 specifying the unique Paper ID, assigned to each paper by IEEE CPS publishing vendor and sent to you when inviting you to submit the camera ready version of your paper.

ABOUT OPEN POLICIES

As you know, ICSE 2021 has embraced open science practices towards higher transparency of the scientific process and availability of research artifacts. With ICSE 2021 going virtual, more than ever we need to ensure access to such a widely distributed audience.

In case you have not done so at submission time already, we ask you to inspect the open science policies (https://conf.researchr.org/track/icse-2021/icse-2021-open-science-policies) while preparing the camera ready. In particular, make sure to:

  1. Provide a supporting statement on the data/software/analysis scripts/…/artifact availability (or lack thereof, which should be accompanied by reasons for non-disclosure) in a section named Data Availability after the Conclusion section.

  2. Self-archive your camera ready (NOT the typeset proof by IEEE).

The policies (https://conf.researchr.org/track/icse-2021/icse-2021-open-science-policies) contain instructions and tutorials for all the above. In case of questions, do not hesitate to contact the open science chair Daniel Graziotin (daniel.graziotin@iste.uni-stuttgart.de).

ABOUT PRESENTATION AND TIMING

  • ICSE main conference will be held from Tuesday May 25 to Friday May 28, 2021.

  • You presentation will be 10 minutes long. The presentation will be recorded, not live (you are required to provide a 10 minutes video presentation).

  • Q&A will last for 10 minutes. Q&A will be live.

  • After every session there will be breakout rooms for every paper to continue discussion with interested attendees.

  • Program will have a uniform daily schedule of 12 hours (10:00-22:00 CEST) and will be mirrored in the other 12 hours. Authors are encouraged to be present in the Q&A of the live/main conference (10:00-22:00 CEST). They can be or not present in the mirrored conference (22:00-10:00 CEST) although we encourage to do so if the time is not too inconvenient for their time zone. It may be that for some regions (Japan, Australia, Hawaii) we allow authors to be present in the mirror rather than in the main conference if the time of their session is too uncomfortable. This will be further clarified to authors.

ABOUT REQUIRED INFORMATION TO UPLOAD

  • You are required to provide a compulsory 10 minutes video, shown as part of the conference program. Optionally, you can provide another video of 20 minutes maximum for those people who prefer watching a video over reading a paper.

  • Based on the Open Science Initiative of ICSE 2021, you are required to self-archive your camera ready. So, you will be required to provide us the link that will appear in ICSE 2021 website.

  • Further instructions and details on all these matters will be specified as the process advances.

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Results (897)

MicrosoftUnited States
Deakin University, AustraliaAustralia
Faculty of Engineering, University of Porto, PortugalPortugal
University of Luxembourg
Queens University
KU LeuvenBelgium
Software Institute, USI Università della Svizzera italiana
Politecnico di MilanoItaly
University of Rio Cuarto and CONICET, Argentina
FacebookUnited Kingdom
University of California, Irvine
Samsung ElectronicsSouth Korea
Samsung ElectronicsSouth Korea
Adyen N.V.Netherlands
University of York, UK
Simon Fraser UniversityCanada
University of California, Irvine
Rochester Institute of Technology, USAUnited States
Xerox CorporationUnited States
Lancaster UniversityUnited Kingdom
Technical University of Munich, SiemensGermany
Delft University of Technology, NetherlandsNetherlands
Saarland University
Deakin University
Facebook, Inc.United States
Jane StreetUnited States
University of Nebraska-Lincoln
University of British Columbia
University of Adelaide
National University of Singapore
Microsoft ResearchUnited States
The Pennsylvania State University
CEA LIST, University Paris-Saclay, France
University College London, UK
University of Luxembourg
Università della Svizzera Italiana
Carleton UniversityCanada
University College LondonUnited Kingdom
Northeastern University
Northeastern University
Facebook, Inc.United States
Aston UniversityUnited Kingdom
Chalmers | University of Gothenburg
University of British Columbia
LMU Munich, GermanyGermany
University of Alberta, CanadaCanada
JOHANNES KEPLER UNIVERSITY LINZ
Lancaster UniversityUnited Kingdom
University of AucklandNew Zealand
Heinz Nixdorf Institut, Paderborn University and Fraunhofer IEM
University of Stuttgart, Institute of Software Engineering, Empirical Software Engineering GroupGermany
FacebookUnited Kingdom
Tweag I/O, Paris, France
Federal University of PernambucoBrazil
Sapienza University of Rome
Chalmers University of Technology, Sweden
Wayne State University
University of British Columbia
MicrosoftUnited States
University of Zurich
Leibniz Institute for Neurobiology
University of Luxembourg and University of Ottawa
EECS, University Of Ottawa
University of Rio Cuarto and CONICET, Argentina
Ericsson / Blekinge Institute of TechnologySweden
Singapore Management University, Singapore
IBM ResearchUnited States
Oregon State University
MicrosoftUnited States
Monash University, AustraliaAustralia
C
Institute of Software at Chinese Academy of Sciences, China
Institute of Software, Chinese Academy of SciencesChina
Drexel UniversityUnited States
Chalmers | University of Gothenburg
University of York, UK
CSIRO Data61Australia
Department of Computer Science and Engineering, The Hong Kong University of Science and Technology
Software Institute, Nanjing University
Peking University, China
Nanjing University
Politecnico di MilanoItaly
Federal University of Pernambuco
Tilburg University & ​Jheronimus Academy of Data ScienceNetherlands
Samsung ElectronicsSouth Korea
I&V Dept of Kirin Solution Dept, HS, Huawei
MicrosoftUnited States
Athens University of Economics and Business
City University of Hong Kong, Hong KongChina
Facebook, USAUnited States
College of William & Mary
Institute of Software, Chinese Academy of Sciences;University of Chinese Academy of Sciences
Monash University
Zhejiang University
College of Intelligence and Computing, Tianjin University
Department of Computer Science and Technology, Nanjing University
College of Intelligence and Computing, Tianjin University
Nanyang Technological University
School of Information Science and Technology, Nantong University
Microsoft Research, China
Department of Computer Science, South China Normal University
Wuhan University
Peking University, ChinaChina
Nanjing University
Institute of Software, Chinese Academy of Sciences;University of Chinese Academy of Sciences
Zhejiang University
Department of Computer Science and Engineering, The Hong Kong University of Science and Technology
Indian Institute of Technology TirupatiIndia
Kyoto Institute of TechnologyJapan
MicrosoftUnited States
University of British Columbia
Independent Researcher
University of CambridgeUnited Kingdom
Iowa State UniversityUnited States
Universidade Federal do AmazonasBrazil
Sapienza University of Rome
Chalmers University of TechnologySweden
Eindhoven University of TechnologyNetherlands
D
Federal University of Pernambuco
Utrecht UniversityNetherlands
Virginia Commonwealth University
University of VictoriaCanada
University of TwenteNetherlands
University at Buffalo
Bowling Green State University
Sapienza University of Rome
Zhejiang UniversityChina
Nanjing University
The University of Tennessee
Federal University of Pará
IT University of Copenhagen, Denmark
University of North TexasUnited States
University of Virginia
IBM Research, USA
National University of Singapore
National University of Defense Technology
Information and Network Center,Tianjin University
MicrosoftUnited States
MicrosoftUnited States
FacebookUnited Kingdom
FacebookUnited Kingdom
University of Virginia
E
Polytechnique Montreal
JOHANNES KEPLER UNIVERSITY LINZ
University of Virginia
Oregon State University
University of Michigan
IBM ResearchUnited States
University of VictoriaCanada
IBMUnited States
Université de Lille, CNRS, Inria, Centrale Lille, UMR 9189 –CRIStALFrance
University of Luxembourg
F
Nanyang Technological University
Xi'an Jiaotong University
College of Computer Science and Technology, Zhejiang University
Nanjing University
Drexel UniversityUnited States
University of York, UK
VecScan AB (Vector Sweden)Sweden
University of MinnesotaUnited States
State Key Laboratory for Novel Software Technology, Nanjing University
Lancaster UniversityUnited Kingdom
Carnegie Mellon University
Microsoft Research
Arizona State University
Universitat Politècnica de CatalunyaSpain
Chalmers | University of GothenburgSweden
University of Passau
University of Zurich
Dept. of Software Engineering Instituto Tecnológico de Buenos Aires
University of ZurichSwitzerland
Institute of Software Technology, University of Stuttgart / University of Applied Sciences ReutlingenGermany
Wuhan University
G
GoogleUnited States
Microsoft Azure
University of California, Berkeley
Microsoft Research, China
University of Buenos Aires and CONICET, ArgentinaArgentina
University of California, Irvine
MicrosoftUnited States
Stellenbosch University
FacebookUnited States
University of York, UK
Northern Arizona University, USAUnited States
Microsoft Research, USAUnited States
Rochester Institute of TechnologyUnited States
CISPA Helmholtz Center for Information Security
Facebook & Delft University of TechnologyNetherlands
University of Texas at Dallas
University of Stuttgart
Adyen N.V.Netherlands
Chalmers | University of GothenburgSweden
FacebookUnited States
University of British Columbia
Monash University
Johannes Kepler University Linz, AustriaAustria
Universidad Autonoma de Madrid
Università di Napoli Federico II
Oregon State University
University College London
Peking University
H
University of Hamburg, Germany
Monash University, AustraliaAustralia
Deakin University, AustraliaAustralia
Florida State UniversityUnited States
University of British Columbia
University of Bristol
Samsung ElectronicsSouth Korea
Zhejiang UniversityChina
Nanjing University of Science & Technology
Microsoft Research AsiaChina
University of EssexUnited Kingdom
Tianjin University
State Key Laboratory for Novel Software Technology Nanjing University
University College London
School of Computing, Queen's University
University of Michigan - Dearborn
Nara Institute of Science and TechnologyJapan
Microsoft Research, China
Fudan UniversityChina
National University of Defense Technology
State Key Laboratory for Novel Software Technology, Nanjing University
University of Wisconsin--Madison
Tools for Software Engineers, Microsoft
Carnegie Mellon University, USAUnited States
University of Chicago
University of British Columbia
Chalmers and the University of GothenburgSweden
Microsoft ResearchUnited States
Faculty of Information Technology, Monash UniversityAustralia
MicrosoftUnited States
University of Michigan
Beijing Institute of Technology
Chongqing University of Posts and Telecommunications
Institute of Software, Chinese Academy of Sciences;University of Chinese Academy of Sciences
Beijing University of Posts and Telecommunications
Texas A&M University
University of Michigan
Faculty of Information Technology, Monash UniversityAustralia
Monash UniversityAustralia
IBM ResearchUnited States
I
Chalmers | University of GothenburgSweden
University of L'AquilaItaly
Nara Institute of Science and TechnologyJapan
J
MicrosoftUnited States
University of South Carolina
Samsung ElectronicsSouth Korea
Case Western Reserve University
Xi'an Jiaotong University
Carnegie Mellon University
National University of Defense Technology
School of Software, Dalian University of TechnologyChina
College of Intelligence and Computing, Tianjin University
Singapore Management University
University of Notre Dame
Purdue University
Beijing Institute of Technology
Nanjing University
Peking University
William & Mary
George Mason UniversityUnited States
University of California, Berkeley
Samsung ElectronicsSouth Korea
University of Washington
K
Kyushu UniversityJapan
Indian Institute of Science, Bangalore
MicrosoftUnited States
Microsoft Research, Beijing, China
Volkswagen AGGermany
Samsung ElectronicsSouth Korea
University of Michigan
University of Tennessee Knoxville
University of Hawai‘i at Mānoa
JOHANNES KEPLER UNIVERSITY LINZ
University of MichiganUnited States
City University of New York (CUNY) Hunter CollegeUnited States
Concordia University
Sungkyunkwan University