ICSE 2022 199 papers accepted. Updated on 2023-09-08. You can find the lastest information here. Message from the ICSE 2022 General Chair. Message from the ICSE 2022 Program Chairs. Diversity-Driven Automated Formal Verification. $\mu AFL$: Non-intrusive Feedback-driven Fuzzing for Microcontroller Firmware. SPT-Code: Sequence-to-Sequence Pre-Training for Learning Source Code Representations. A Grounded Theory Based Approach to Characterize Software Attack Surfaces. A Grounded Theory of Coordination in Remote-First and Hybrid Software Teams. A Scalable t-wise Coverage Estimator. A Universal Data Augmentation Approach for Fault Localization. Adaptive Performance Anomaly Detection for Online Service Systems via Pattern Sketching. Adaptive Test Selection for Deep Neural Networks. An Exploratory Study of Deep learning Supply Chain. An Exploratory Study of Productivity Perceptions in Software Teams. Analyzing User Perspectives on Mobile App Privacy at Scale. APER: Evolution-Aware Runtime Permission Misuse Detection for Android Apps. ARCLIN: Automated API Mention Resolution for Unformatted Texts. AST-Trans: Code Summarization with Efficient Tree-Structured Attention. Automated Assertion Generation via Information Retrieval and Its Integration with Deep learning. Automated Detection of Password Leakage from Public GitHub Repositories. Automated Handling of Anaphoric Ambiguity in Requirements: A Multi-solution Study. Automated Patching for Unreproducible Builds. Automated Testing of Software that Uses Machine Learning APIs. Automatic Detection of Performance Bugs in Database Systems using Equivalent Queries. AutoTransform: Automated Code Transformation to Support Modern Code Review Process. BEDIVFUZZ: Integrating Behavioral Diversity into Generator-based Fuzzing. Big Data = Big Insights? Operationalising Brooks' Law in a Massive GitHub Data Set. Bots for Pull Requests: The Good, the Bad, and the Promising. Bridging Pre-trained Models and Downstream Tasks for Source Code Understanding. BugListener: Identifying and Synthesizing Bug Reports from Collaborative Live Chats. Buildsheriff: Change-Aware Test Failure Triage for Continuous Integration Builds. Causality in Configurable Software Systems. Causality-Based Neural Network Repair. Change Is the Only Constant: Dynamic Updates for Workflows. Characterizing and Detecting Bugs in WeChat Mini-Programs. CLEAR: Contrastive Learning for API Recommendation. Code Search based on Context-aware Code Translation. CodeFill: Multi-token Code Completion by Jointly learning from Structure and Naming Sequences. Collaboration Challenges in Building ML-Enabled Systems: Communication, Documentation, Engineering, and Process. Combinatorial Testing of RESTful APIs. CONFETTI: Amplifying Concolic Guidance for Fuzzers. Conflict-aware Inference of Python Compatible Runtime Environments with Domain Knowledge Graph. Control Parameters Considered Harmful: Detecting Range Specification Bugs in Drone Configuration Modules via Learning-Guided Search. Controlled Concurrency Testing via Periodical Scheduling. Cross-Domain Deep Code Search with Meta Learning. Data-Driven Loop Bound Learning for Termination Analysis. DEAR: A Novel Deep Learning-based Approach for Automated Program Repair. Decomposing Convolutional Neural Networks into Reusable and Replaceable Modules. Decomposing Software Verification into Off-the-Shelf Components: An Application to CEGAR. DeepAnalyze: Learning to Localize Crashes at Scale. DeepDiagnosis: Automatically Diagnosing Faults and Recommending Actionable Fixes in Deep Learning Programs. DeepFD: Automated Fault Diagnosis and Localization for Deep Learning Programs. DeepStability: A Study of Unstable Numerical Methods and Their Solutions in Deep Learning. DeepState: Selecting Test Suites to Enhance the Robustness of Recurrent Neural Networks. DeepSTL - From English Requirements to Signal Temporal Logic. DeepTraLog: Trace-Log Combined Microservice Anomaly Detection through Graph-based Deep Learning. Default: Mutual Information-based Crash Triage for Massive Crashes. Demystifying Android Non-SDK APls: Measurement and Understanding. Demystifying the Dependency Challenge in Kernel Fuzzing. Demystifying the Vulnerability Propagation and Its Evolution via Dependency Trees in the NPM Ecosystem. DescribeCtx: Context-Aware Description Synthesis for Sensitive Behaviors in Mobile Apps. Detecting False Alarms from Automatic Static Analysis Tools: How Far are We? "Did You Miss My Comment or What?" Understanding Toxicity in Open Source Discussions. Difuzer: Uncovering Suspicious Hidden Sensitive Operations in Android Apps. Discovering Repetitive Code Changes in Python ML Systems. Domain-Specific Analysis of Mobile App Reviews Using Keyword-Assisted Topic Models. DrAsync: Identifying and Visualizing Anti-Patterns in Asynchronous JavaScript. Dynamic Update for Synthesized GR(1) Controllers. EAGLE: Creating Equivalent Graphs to Test Deep Learning Libraries. Efficient Online Testing for DNN-Enabled Systems using Surrogate-Assisted and Many-Objective Optimization. Eflect: Porting Energy-Aware Applications to Shared Environments. EREBA: Black-box Energy Testing of Adaptive Neural Networks. Evaluating and Improving Neural Program-Smoothing-based Fuzzing. ExAIS: Executable AI Semantics. Explanation-Guided Fairness Testing through Genetic Algorithm. Exploiting Input Sanitization for Regex Denial of Service. FADATest: Fast and Adaptive Performance Regression Testing of Dynamic Binary Translation Systems. Fairness-aware Configuration of Machine Learning Libraries. Fairneuron: Improving Deep Neural Network Fairness with Adversary Games on Selective Neurons. Fast and Precise Application Code Analysis using a Partial Library. Fast Changeset-based Bug Localization with BERT. Fault Localization via Efficient Probabilistic Modeling of Program Semantics. FIRA: Fine-Grained Graph-Based Code Change Representation for Automated Commit Message Generation. FlakiMe: Laboratory-Controlled Test Flakiness Impact Assessment. Free Lunch for Testing: Fuzzing Deep-Learning Libraries from Open Source. Fuzzing Class Specifications. Garbage Collection Makes Rust Easier to Use: A Randomized Controlled Trial of the Bronze Garbage Collector. Generating and Visualizing Trace Link Explanations. GIFdroid: Automated Replay of Visual Bug Reports for Android Apps. GitHub Sponsors: Exploring a New Way to Contribute to Open Source. GraphFuzz: Library API Fuzzing with Lifetime-aware Dataflow Graphs. Green AI: Do Deep Learning Frameworks Have Different Costs? Guidelines for Assessing the Accuracy of Log Message Template Identification Techniques. Hashing It Out: A Survey of Programmers' Cannabis Usage, Perception, and Motivation. Hiding Critical Program Components via Ambiguous Translation. History-Driven Test Program Synthesis for JVM Testing. If a Human Can See It, So Should Your System: Reliability Requirements for Machine Vision Components. Imperative versus Declarative Collection Processing: An RCT on the Understandability of Traditional Loops versus the Stream API in Java. Improving Fault Localization and Program Repair with Deep Semantic Features and Transferred Knowledge. Improving Machine Translation Systems via Isotopic Replacement. Inference and Test Generation Using Program Invariants in Chemical Reaction Networks. Inferring and Applying Type Changes. Jigsaw: Large Language Models meet Program Synthesis. JuCify: A Step Towards Android Code Unification for Enhanced Static Analysis. Knowledge-Based Environment Dependency Inference for Python Programs. Large-scale Security Measurements on the Android Firmware Ecosystem. Learning and Programming Challenges of Rust: A Mixed-Methods Study. Learning Probabilistic Models for Static Analysis Alarms. Learning to Recommend Method Names with Global Context. Learning to Reduce False Positives in Analytic Bug Detectors. Less is More: Supporting Developers in Vulnerability Detection during Code Review. Lessons from Eight Years of Operational Data from a Continuous Integration Service: An Exploratory Case Study of CircleCI. Linear-time Temporal Logic guided Greybox Fuzzing. Log-based Anomaly Detection with Deep Learning: How Far Are We? Manas: Mining Software Repositories to Assist AutoML. Modeling Review History for Reviewer Recommendation: A Hypergraph Approach. ModX: Binary Level Partially Imported Third-Party Library Detection via Program Modularization and Semantic Matching. Morest: Model-based RESTful API Testing with Execution Feedback. Muffin: Testing Deep Learning Libraries via Neural Architecture Fuzzing. Multi-Intention-Aware Configuration Selection for Performance Tuning. Multilingual training for Software Engineering. MVD: Memory-Related Vulnerability Detection Based on Flow-Sensitive Graph Neural Networks. Nalin: learning from Runtime Behavior to Find Name-Value Inconsistencies in Jupyter Notebooks. Natural Attack for Pre-trained Models of Code. Nessie: Automatically Testing JavaScript APIs with Asynchronous Callbacks. Neural Program Repair with Execution-based Backpropagation. NeuronFair: Interpretable White-Box Fairness Testing through Biased Neuron Identification. NPEX: Repairing Java Null Pointer Exceptions without Tests. Nufix: Escape From NuGet Dependency Maze. OJXPERF: Featherlight Object Replica Detection for Java Programs. On Debugging the Performance of Configurable Software Systems: Developer Needs and Tailored Tool Support. On the Benefits and Limits of Incremental Build of Software Configurations: An Exploratory Study. On the Evaluation of Neural Code Summarization. On the Importance of Building High-quality Training Datasets for Neural Code Search. On the Reliability of Coverage-Based Fuzzer Benchmarking. One Fuzzing Strategy to Rule Them All. Online Summarizing Alerts through Semantic and Behavior Information. Path Transitions Tell More: Optimizing Fuzzing Schedules via Runtime Program States. PerfSig: Extracting Performance Bug Signatures via Multi-modality Causal Analysis. Practical Automated Detection of Malicious npm Packages. Practitioners' Expectations on Automated Code Comment Generation. PREACH: A Heuristic for Probabilistic Reachability to Identify Hard to Reach Statements. Precise Divide-By-Zero Detection with Affirmative Evidence. Preempting Flaky Tests via Non-Idempotent-Outcome Tests. Prioritizing Mutants to Guide Mutation Testing. PROMAL: Precise Window Transition Graphs for Android via Synergy of Program Analysis and Machine Learning. PROPR: Property-Based Automatic Program Repair. PUS: A Fast and Highly Efficient Solver for Inclusion-based Pointer Analysis. Push-Button Synthesis of Watch Companions for Android Apps. Quantifying Permissiveness of Access Control Policies. R2Z2: Detecting Rendering Regressions in Web Browsers through Differential Fuzz Testing. Recommending Good First Issues in GitHub OSS Projects. REFTY: Refinement Types for Valid Deep Learning Models. ReMoS: Reducing Defect Inheritance in Transfer Learning via Relevant Model Slicing. Repairing Brain-Computer Interfaces with Fault-Based Data Acquisition. Repairing Order-Dependent Flaky Tests via Test Generation. Retrieving Data Constraint Implementations Using Fine-Grained Code Patterns. RoPGen: Towards Robust Code Authorship Attribution via Automatic Coding Style Transformation. Rotten Apples Spoil the Bunch: An Anatomy of Google Play Malware. SAPIENTML: Synthesizing Machine Learning Pipelines by Learning from Human-Written Solutions. Search-based Diverse Sampling from Real-world Software Product Lines. Semantic Image Fuzzing of AI Perception Systems. ShellFusion: Answer Generation for Shell Programming Tasks via Knowledge Fusion. SnR: Constraint-Based Type Inference for Incomplete Java Code Snippets. Social Science Theories in Software Engineering Research. Static Inference Meets Deep learning: A Hybrid Type Inference Approach for Python. Static Stack-Preserving Intra-Procedural Slicing of WebAssembly Binaries. Striking a Balance: Pruning False-Positives from Static Call Graphs. SugarC: Scalable Desugaring of Real-World Preprocessor Usage into Pure C. SYMTUNER: Maximizing the Power of Symbolic Execution by Adaptively Tuning External Parameters. Testing Time Limits in Screener Questions for Online Surveys with Programmers. The Art and Practice of Data Science Pipelines: A Comprehensive Study of Data Science Pipelines In Theory, In-The-Small, and In-The-Large. The Extent of Orphan Vulnerabilities from Code Reuse in Open Source Software. "This Is Damn Slick!" Estimating the Impact of Tweets on Open Source Project Popularity and New Contributors. TOGA: A Neural Method for Test Oracle Generation. Towards Automatically Repairing Compatibility Issues in Published Android Apps. Towards Bidirectional Live Programming for Incomplete Programs. Towards Boosting Patch Execution On-the-Fly. Towards language-independent Brown Build Detection. Towards Practical Robustness Analysis for DNNs based on PAC-Model Learning. Towards Training Reproducible Deep Learning Models. Training Data Debugging for the Fairness of Machine Learning Software. Trust Enhancement Issues in Program Repair. Type4Py: Practical Deep Similarity Learning-Based Type Inference for Python. Unleashing the Power of Compiler Intermediate Representation to Enhance Neural Program Embeddings. Use of Test Doubles in Android Testing: An In-Depth Investigation. Using Deep Learning to Generate Complete Log Statements. Using Pre-Trained Models to Boost Code Review Automation. Using Reinforcement Learning for Load Testing of Video Games. Utilizing Parallelism in Smart Contracts on Decentralized Blockchains by Taming Application-Inherent Conflicts. VarCLR: Variable Semantic Representation Pre-training via Contrastive Learning. Verification of ORM-based Controllers by Summary Inference. V-SZZ: Automatic Identification of Version Ranges Affected by CVE Vulnerabilities. VulCNN: An Image-inspired Scalable Vulnerability Detection System. What Do They Capture? - A Structural Analysis of Pre-Trained Language Models for Source Code. What Makes a Good Commit Message? What Makes Effective Leadership in Agile Software Development Teams? What the Fork? Finding Hidden Code Clones in npm. Where is Your App Frustrating Users? Windranger: A Directed Greybox Fuzzer driven by Deviation Basic Blocks.