Write a Blog >>
ICSE 2022
Sun 8 May - Mon 27 June 2022 Location to be announced
Supporters
Sponsor
Sponsor
Platinum
Platinum
Gold
Silver
Silver
Silver

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. In the technical track, we invite high quality submissions of technical research papers describing original and unpublished results of software engineering research.

Please note the following important changes for 2022:

  1. Point out the significance of your research contributions (see review criteria).

  2. Follow our open science policy – share data and justify if you do not.

  3. Give your submission a unique title that is different from preprints and talks.

Research of Interest

ICSE welcomes submissions addressing topics across the full spectrum of Software Engineering, being inclusive of quantitative, qualitative, and mixed-methods research. Topics of interest include:

  • AI and software engineering, including

    • Search-based software engineering
    • Machine learning with and for SE
    • Recommender systems
    • Autonomic systems and self adaptation
    • Program synthesis
    • Program repair
    • Software fairness
  • Testing and analysis, including

    • Software testing
    • Program analysis
    • Debugging and Fault localization
    • Programming languages
    • Performance
    • Mobile applications
  • Software analytics, including

    • Mining software repositories
    • Apps and app store analysis
    • Software ecosystems
    • Configuration management
    • Software visualization
  • Software evolution, including

    • Evolution and maintenance
    • API design and evolution
    • Release engineering and DevOps
    • Software reuse
    • Refactoring
    • Program comprehension
    • Reverse engineering
  • Social aspects of software engineering, including

    • 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
    • Green and sustainable technologies
  • Requirements, modeling, and design, including

    • Requirements Engineering
    • Privacy and Security by Design
    • Modeling and Model-Driven Engineering
    • Software Architecture and Design
    • Variability and product lines
    • Software services
  • Dependability, including

    • Formal methods
    • Validation and Verification
    • Reliability and Safety
    • Privacy and Security
    • Embedded and cyber-physical systems

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 and/or innovations address its research questions and are supported by rigorous application of appropriate research methods

  • Significance: The extent to which the paper’s contributions can impact the field of software engineering, and under which assumptions (if any)

  • Novelty: The extent to which the contributions are sufficiently original with respect to the state-of-the-art

  • Verifiability and Transparency: The extent to which the paper includes sufficient information to understand how an innovation works; to understand how data was obtained, analyzed, and interpreted; and how the paper supports 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, as well as 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 authors should take great care in clearly addressing them all. The paper should clearly explain the claimed contributions, and how they are sound, significant, novel, and verifiable, as described above.

For more information on how the ICSE PC will interpret and use these criteria in the paper evaluation process, see the ICSE 2022 Review Process and Guidelines.

How to Submit

All authors should use the official “ACM Primary Article Template”, as can be obtained from the ACM Proceedings Template page. LaTeX users should use the sigconf option, as well as the review (to produce line numbers for easy reference by the reviewers) and anonymous (omitting author names) options. To that end, the following LaTeX code can be placed at the start of the LaTeX document:

\documentclass[sigconf,review,anonymous]{acmart}

\acmConference[ICSE 2022]{The 44th International Conference on Software Engineering}{May 21–29, 2022}{Pittsburgh, PA, USA}

  • All submissions 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. Accepted papers will be allowed one extra page for the main text of the camera-ready version.

  • Submissions must strictly conform to the ACM formatting instructions. Alterations of spacing, font size, and other changes that deviate from the instructions may result in desk rejection without further review.

  • 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 2022 must not have been published elsewhere and must not be under review or submitted for review elsewhere whilst under consideration for ICSE 2022. 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 2022 Technical Track 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.
    • Authors are encouraged to title their submission differently than preprints of the authors on ArXiV or similar sites. During review, authors should not publicly use the submission title.

    Further advice, guidance, and explanation about the double-anonymous 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://icse2022.hotcrp.com) by the submission deadline. Any submission that does not comply with these requirements may be desk rejected without further review.

We encourage the authors to upload their paper info early (and can submit the PDF later) to properly enter conflicts for double-anonymous reviewing. Authors are encouraged to try out the experimental SIGSOFT Submission Checker to detect violations to the formatting and double anonymous guidelines. (Mind that the tool is based on heuristics. Therefore it may miss violations, and it can raise false alarms. The requirements listed in this call for papers take precedence over the results of the tool when deciding whether a paper meets the submission guidelines.)

Open Science Policy

The research track of ICSE 2022 is governed by the ICSE 2022 Open Science policies. In summary, the steering principle is that all research results should be accessible to the public and, if possible, empirical studies should be reproducible. In particular, we actively support the adoption of open data and open source principles and encourage all contributing authors to disclose (anonymized and curated) data to increase reproducibility and replicability. Note that sharing research data is not mandatory for submission or acceptance. However, sharing is expected to be the default, and non-sharing needs to be justified. We recognize that reproducibility or replicability is not a goal in qualitative research and that, similar to industrial studies, qualitative studies often face challenges in sharing research data. For guidelines on how to report qualitative research to ensure the assessment of the reliability and credibility of research results, see the Q&A page.

Upon submission to the research track, authors are asked

  • to make their data available to the program committee (via upload of supplemental material or a link to an anonymous repository) – and provide instructions on how to access this data in the paper; or

  • to include in the paper an explanation as to why this is not possible or desirable; and

  • to indicate if they intend to make their data publicly available upon acceptance.

Supplementary material can be uploaded via the HotCRP site or anonymously linked from the paper submission. Although PC members are not required to look at this material, we strongly encourage authors to use supplementary material to provide access to anonymized data, whenever possible. Authors are asked to carefully review any supplementary material to ensure it conforms to the double-anonymous 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 2022 Artifact Evaluation track, for recognition of artifacts that are reusable, available, replicated or reproduced.

Mentoring for Prospective Authors

We are organizing an “Ask me Anything” (AMA) Session on Best practices for a successful ICSE paper in June for prospective authors to learn from the 2021 ICSE PC co-chairs, Arie van Deursen and Tao Xie.

This event takes place on two dates, one for each hemisphere:

To participate in the event, please register here. Deadline for registration is June 24,

Author Response Period

ICSE 2022 will offer a three day author response period. In this period the authors will have the opportunity to inspect the reviews, and to answer 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 the paper to another venue before the final decision has been made without withdrawing from ICSE 2022 first is considered a violation of the concurrent submission policy, and will lead to automatic rejection from ICSE 2022 as well as any other venue adhering to this policy.

Important Dates

  • Technical Track Abstract Submissions (Required) Deadline: August 27, 2021

  • Technical Track Submissions Deadline: September 3, 2021

  • Technical Track Author Response Period: November 10–13, 2021

  • Technical Track Acceptance Notification: December 3, 2021

  • Technical Track Camera Ready: February 11, 2022

Conference Attendance Expectation

If a submission is accepted, at least one author of the paper is required to register for ICSE 2022 and present the paper. [We will add more info on this as soon as the ICSE 2022 format is finalized.]

Accepted Papers

Title
Adaptive Performance Anomaly Detection for Online Service Systems via Pattern Sketching
Technical Track
Adaptive Test Selection for Deep Neural Networks
Technical Track
μAFL: Non-intrusive Feedback-driven Fuzzing for Microcontroller Firmware
Technical Track
A Grounded Theory Based Approach to Characterize Software Attack Surfaces
Technical Track
A Grounded Theory of Coordination in Remote-First and Hybrid Software Teams
Technical Track
Analyzing User Perspectives on Mobile App Privacy at Scale
Technical Track
An Exploratory Study of Deep Learning Supply Chain
Technical Track
An Exploratory Study of Productivity in Software Teams
Technical Track
Aper: Evolution-Aware Runtime Permission Misuse Detection for Android Apps
Technical Track
ARCLIN: Automated API Mention Resolution for Unformatted Texts
Technical Track
A Scalable t-wise Coverage Estimator
Technical Track
AST-Trans: Code Summarization with Efficient Tree-Structured Attention
Technical Track
A Universal Data Augmentation Approach for Fault Localization
Technical Track
Automated Assertion Generation via Information Retrieval and Its Integration with Deep Learning
Technical Track
Automated Detection of Password Leakage from Public GitHub Repositories
Technical Track
Automated Handling of Anaphoric Ambiguity in Requirements: A Multi-solution Study
Technical Track
Automated Patching for Unreproducible Builds
Technical Track
Automated Testing of Software that Uses Machine Learning APIs
Technical Track
Automatic Detection of Performance Bugs in Database Systems using Equivalent Queries
Technical Track
AutoTransform: Automated Code Transformation to Support Modern Code Review Process
Technical Track
BeDivFuzz: Integrating Behavioral Diversity into Generator-based Fuzzing
Technical Track
Big Data = Big Insights? Operationalizing Brooks’ Law in a Massive GitHub Data Set
Technical Track
Bots for Pull Requests: The Good, the Bad, and the Promising
Technical Track
Bridging Pre-trained Models and Downstream Tasks for Source Code Understanding
Technical Track
BugListener: Identifying and Synthesizing Bug Reports from Collaborative Live Chats
Technical Track
BuildSheriff: Change-Aware Test Failure Triage for Continuous Integration Builds
Technical Track
Causality-Based Neural Network Repair
Technical Track
Causality in Configurable Software Systems
Technical Track
Change Is the Only Constant: Dynamic Updates for Workflows
Technical Track
DOI
Characterizing and Detecting Bugs in WeChat Mini-Programs
Technical Track
CLEAR: Contrastive Learning for API Recommendation
Technical Track
CodeFill: Multi-token Code Completion by Jointly Learning from Structure and Naming Sequences
Technical Track
Code Search based on Context-aware Code Translation
Technical Track
Collaboration Challenges in Building ML-Enabled Systems: Communication, Documentation, Engineering, and Process
Technical Track
Combinatorial Testing of RESTful APIs
Technical Track
CONFETTI: Amplifying Concolic Guidance for Fuzzers
Technical Track
Conflict-aware Inference of Python Compatible Runtime Environments with Domain Knowledge Graph
Technical Track
Controlled Concurrency Testing via Periodical Scheduling
Technical Track
DOI
Control Parameters Considered Harmful: Detecting Range Specification Bugs in Drone Configuration Modules via Learning-Guided Search
Technical Track
Cross-Domain Deep Code Search with Few-Shot Learning
Technical Track
Data-Driven Loop Bound Learning for Termination Analysis
Technical Track
DEAR: A Novel Deep Learning-based Approach for Automated Program Repair
Technical Track
Decomposing Convolutional Neural Networks into Reusable and Replaceable Modules
Technical Track
Pre-print
Decomposing Software Verification into Off-the-Shelf Components: An Application to CEGAR
Technical Track
DeepAnalyze: Learning to Localize Crashes at Scale
Technical Track
Pre-print
DeepDiagnosis: Automatically Diagnosing Faults and Recommending Actionable Fixes in Deep Learning Programs
Technical Track
DeepFD: Automated Fault Diagnosis and Localization for Deep Learning Programs
Technical Track
DeepStability: A Study of Unstable Numerical Methods and Their Solutions in Deep Learning
Technical Track
DeepState: Selecting Test Suites to Enhance the Robustness of Recurrent Neural Networks
Technical Track
DeepSTL - From English Requirements to Signal Temporal Logic
Technical Track
DeepTraLog: Trace-Log Combined Microservice Anomaly Detection through Graph-based Deep Learning
Technical Track
DeFault: Mutual Information-based Crash Triage for Massive Crashes
Technical Track
Demystifying Android Non-SDK APIs: Measurement and Understanding
Technical Track
Demystifying the Dependency Challenge in Kernel Fuzzing
Technical Track
Demystifying the Vulnerability Propagation and Its Evolution via Dependency Trees in the NPM Ecosystem
Technical Track
Pre-print
DescribeCtx: Context-Aware Description Synthesis for Sensitive Behaviors in Mobile Apps
Technical Track
Detecting False Alarms from Automatic Static Analysis Tools: How Far are We?
Technical Track
"Did You Miss My Comment or What?" Understanding Toxicity in Open Source Discussions
Technical Track
Difuzer: Uncovering Suspicious Hidden Sensitive Operations in Android Apps
Technical Track
DOI Pre-print
Discovering Repetitive Code Changes in Python ML Systems
Technical Track
Diversity-Driven Automated Formal Verification
Technical Track
Pre-print
Domain-Specific Analysis of Mobile App Reviews Using Keyword-Assisted Topic Models
Technical Track
DrAsync: Identifying and Visualizing Anti-Patterns in Asynchronous JavaScript
Technical Track
Dynamic Update for Synthesized GR(1) Controllers
Technical Track
EAGLE: Creating Equivalent Graphs to Test Deep Learning Libraries
Technical Track
Efficient Online Testing for DNN-Enabled Systems using Surrogate-Assisted and Many-Objective Optimization
Technical Track
Eflect: Porting Energy-Aware Applications to Shared Environments
Technical Track
EREBA: Black-box Energy Testing of Adaptive Neural Networks
Technical Track
Evaluating and Improving Neural Program-Smoothing-based Fuzzing
Technical Track
ExAIS: Executable AI Semantics
Technical Track
Explanation-Guided Fairness Testing through Genetic Algorithm
Technical Track
Exploiting Input Sanitization for Regex Denial of Service
Technical Track
FADATest: Fast and Adaptive Performance Regression Testing of Dynamic Binary Translation Systems
Technical Track
Fairness-aware Configuration of Machine Learning Libraries
Technical Track
FairNeuron: Improving Deep Neural Network Fairness with Adversary Games on Selective Neurons
Technical Track
Fast and Precise Application Code Analysis using a Partial Library
Technical Track
Fast Changeset-based Bug Localization with BERT
Technical Track
Pre-print
Fault Localization via Efficient Probabilistic Modeling of Program Semantics
Technical Track
FIRA: Fine-Grained Graph-Based Code Change Representation for Automated Commit Message Generation
Technical Track
FlakiMe: Laboratory-Controlled Test Flakiness Impact Assessment
Technical Track
Free Lunch for Testing: Fuzzing Deep-Learning Libraries from Open Source
Technical Track
Fuzzing Class Specifications
Technical Track
Garbage Collection Makes Rust Easier to Use: A Randomized Controlled Trial of the Bronze Garbage Collector
Technical Track
Generating and Visualizing Trace Link Explanations
Technical Track
GIFdroid: Automated Replay of Visual Bug Reports for Android Apps
Technical Track
GitHub Sponsors: Exploring a New Way to Contribute to Open Source
Technical Track
GraphFuzz: Library API Fuzzing with Lifetime-aware Dataflow Graphs
Technical Track
Green AI: Do Deep Learning Frameworks Have Different Costs?
Technical Track
Guidelines for Assessing the Accuracy of Log Message Template Identification Techniques
Technical Track
Hashing It Out: A Survey of Programmers’ Cannabis Usage, Perception, and Motivation
Technical Track
DOI Pre-print
Hiding Critical Program Components via Ambiguous Translation
Technical Track
History-Driven Test Program Synthesis for JVM Testing
Technical Track
If a Human Can See It, So Should Your System: Reliability Requirements for Machine Vision Components
Technical Track
Imperative versus Declarative Collection Processing: An RCT on the Understandability of Traditional Loops versus the Stream API in Java
Technical Track
Improving Fault Localization and Program Repair with Deep Semantic Features and Transferred Knowledge
Technical Track
Improving Machine Translation Systems via Isotopic Replacement
Technical Track
Inference and Test Generation Using Program Invariants in Chemical Reaction Networks
Technical Track
Inferring And Applying Type Changes
Technical Track
Jigsaw: Large Language Models meet Program Synthesis
Technical Track
JuCify: A Step Towards Android Code Unification for Enhanced Static Analysis
Technical Track
Pre-print
Knowledge-Based Environment Dependency Inference for Python Programs
Technical Track
Large-scale Security Measurements on the Android Firmware Ecosystem
Technical Track
Learning and Programming Challenges of Rust: A Mixed-Methods Study
Technical Track
Learning Probabilistic Models for Static Analysis Alarms
Technical Track
Learning to Recommend Method Names with Global Context
Technical Track
Learning to Reduce False Positives in Analytic Bug Detectors
Technical Track
Less is More: Supporting Developers in Vulnerability Detection during Code Review
Technical Track
Lessons from Eight Years of Operational Data from a Continuous Integration Service: A Case Study of CircleCI
Technical Track
Linear-time Temporal Logic guided Greybox Fuzzing
Technical Track
Log-based Anomaly Detection with Deep Learning: How Far Are We
Technical Track
Manas: Mining Software Repositories to Assist AutoML
Technical Track
Modeling Review History for Reviewer Recommendation: A Hypergraph Approach
Technical Track
Modx: Binary Level Partial Imported Third-Party Library Detection through Program Modularization and Semantic Matching
Technical Track
MOREST: Model-based RESTful API Testing with Execution Feedback
Technical Track
Muffin: Testing Deep Learning Libraries via Neural Architecture Fuzzing
Technical Track
Multi-Intention-Aware Configuration Selection for Performance Tuning
Technical Track
Multilingual training for Software Engineering
Technical Track
MVD: Memory-related Vulnerability Detection Based on Flow-Sensitive Graph Neural Networks
Technical Track
Nalin: Learning from Runtime Behavior to Find Name-Value Inconsistencies
Technical Track
Natural Attack for Pre-trained Models of Code
Technical Track
DOI Pre-print
Nessie: Automatically Testing JavaScript APIs with Asynchronous Callbacks
Technical Track
Neural Program Repair using Execution-based Backpropagation
Technical Track
NeuronFair: Interpretable White-Box Fairness Testing through Biased Neuron Identification
Technical Track
DOI Pre-print
NPEX: Repairing Java Null Pointer Exceptions without Tests
Technical Track
Nufix: Escape From NuGet Dependency Maze
Technical Track
OJXPerf: Featherlight Object Replica Detection for Java Programs
Technical Track
On Debugging the Performance of Configurable Software Systems: Developer Needs and Tailored Tool Support
Technical Track
One Fuzzing Strategy to Rule Them All
Technical Track
Online Summarizing Alerts through Semantic and Behavior Information
Technical Track
On the Benefits and Limits of Incremental Build of Software Configurations: An Exploratory Study
Technical Track
On the Evaluation of Neural Code Summarization
Technical Track
On the Importance of Building High-quality Training Datasets for Neural Code Search
Technical Track
On the Reliability of Coverage-Based Fuzzer Benchmarking
Technical Track
Path Transitions Tell More: Optimizing Fuzzing Schedules via Runtime Program States
Technical Track
PerfSig: Extracting Performance Bug Signatures via Multi-modality Causal Analysis
Technical Track
Practical Automated Detection of Malicious npm Packages
Technical Track
Practitioners’ Expectations on Automated Code Comment Generation
Technical Track
PReach: A Heuristic for Probabilistic Reachability to Identify Hard to Reach Statements
Technical Track
Precise Divide-By-Zero Detection with Affirmative Evidence
Technical Track
Preempting Flaky Tests via Non-Idempotent-Outcome Tests
Technical Track
Promal: Precise Window Transition Graphs for Android via Synergy of Program Analysis and Machine Learning
Technical Track
PropR: Property-Based Automatic Program Repair
Technical Track
DOI Pre-print
PUS: A Fast and Highly Efficient Solver for Inclusion-based Pointer Analysis
Technical Track
Push-Button Synthesis of Watch Companions for Android Apps
Technical Track
Quantifying Permissiveness of Access Control Policies
Technical Track
R2Z2: Detecting Rendering Regressions in Web Browsers through Differential Fuzz Testing
Technical Track
Recommending Good First Issues in GitHub OSS Projects
Technical Track
Refty: Refinement Types for Valid Deep Learning Models
Technical Track
ReMoS: Reducing Defect Inheritance in Transfer Learning via Relevant Model Slicing
Technical Track
Repairing Brain-Computer Interfaces with Fault-based Data Acquisition
Technical Track
Repairing Order-Dependent Flaky Tests via Test Generation
Technical Track
Retrieving Data Constraint Implementations Using Fine-Grained Code Patterns
Technical Track
RoPGen: Towards Robust Code Authorship Attribution via Automatic Coding Style Transformation
Technical Track
Rotten Apples Spoil the Bunch: An Anatomy of Google Play Malware
Technical Track
SapientML: Synthesizing Machine Learning Pipelines by Learning from Human-Written Solutions
Technical Track
Search-based Diverse Sampling from Real-world Software Product Lines
Technical Track
Semantic Image Fuzzing of AI Perception Systems
Technical Track
ShellFusion: Answer Generation for Shell Programming Tasks via Knowledge Fusion
Technical Track
SnR: Constraint-Based Type Inference for Incomplete Java Code Snippets
Technical Track
Social Science Theories in Software Engineering Research
Technical Track
SPT-Code: Sequence-to-Sequence Pre-Training for Learning Representation of Source Code
Technical Track
Static Inference Meets Deep Learning: A Hybrid Type Inference Approach for Python
Technical Track
Static Stack-Preserving Intra-Procedural Slicing of WebAssembly Binaries
Technical Track
Striking a Balance: Pruning False-Positives from Static Call Graphs
Technical Track
SugarC: Scalable Desugaring of Real-World Preprocessor Usage into Pure C
Technical Track
SymTuner: Maximizing the Power of Symbolic Execution by Adaptively Tuning External Parameters
Technical Track
SZZ for Vulnerability: Automatic Identification of Version Ranges Affected by CVE Vulnerabilities
Technical Track
Testing Time Limits in Screener Questions for Online Surveys with Programmers
Technical Track
The Art and Practice of Data Science Pipelines: A Comprehensive Study of Data Science Pipelines In Theory, In-The-Small, and In-The-Large
Technical Track
Pre-print
The Extent of Orphan Vulnerabilities from Code Reuse in Open Source Software
Technical Track
"This Is Damn Slick!" Estimating the Impact of Tweets on Open Source Project Popularity and New Contributors
Technical Track
TOGA: A Neural Method for Test Oracle Generation
Technical Track
Towards Automatically Repairing Compatibility Issues in Published Android Apps
Technical Track
Towards Bidirectional Live Programming for Incomplete Programs
Technical Track
Towards Boosting Patch Execution On-the-Fly
Technical Track
Towards Language-independent Brown Build Detection
Technical Track
Towards Practical Robustness Analysis for DNNs based on PAC-Model Learning
Technical Track
Towards Training Reproducible Deep Learning Models
Technical Track
Training Data Debugging for the Fairness of Machine Learning Software
Technical Track
Trust Enhancement Issues in Program Repair
Technical Track
TypeZilla: Practical Deep Similarity Learning-Based Type Inference for Python
Technical Track
Unleashing the Power of Compiler Intermediate Representation to Enhance Neural Program Embeddings
Technical Track
Use of Test Doubles in Android Testing: An In-Depth Investigation
Technical Track
Using Deep Learning to Generate Complete Log Statements
Technical Track
Pre-print
Using Pre-Trained Models to Boost Code Review Automation
Technical Track
Pre-print
Using Reinforcement Learning for Load Testing of Video Games
Technical Track
Pre-print
Utility-Based Prioritization of Mutants to Guide Testing Efforts
Technical Track
Utilizing Parallelism in Smart Contracts on Decentralized Blockchains by Taming Application-Inherent Conflicts
Technical Track
DOI Pre-print
VarCLR: Variable Semantic Representation Pre-training via Contrastive Learning
Technical Track
Verification of ORM-based Controllers by Summary Inference
Technical Track
VulCNN: An Image-inspired Scalable Vulnerability Detection System
Technical Track
What Do They Capture? - A Structural Analysis of Pre-Trained Language Models for Source Code
Technical Track
What Makes a Good Commit Message?
Technical Track
What Makes Effective Leadership in Agile Software Development Teams?
Technical Track
What the Fork? Finding Hidden Code Clones in npm
Technical Track
Where is Your App Frustrating Users?
Technical Track
WindRanger: A Directed Greybox Fuzzer driven by DeviationBasic Blocks
Technical Track