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- Requirements Engineering
- Code Generation
- Code Search
- Code Summarization
- Software Refactoring
- Code Clone Detection
- Software Defect Prediction
- Bug Finding
- Fault Localization
- Program Repair
- Bug Report Management
- Developer Collaboration
- Technical Debt
- A large-scale survey on the usability of AI programming assistants: Successes and challenges (ICSE, 2024)
- Reqgen: Keywords-driven software requirements generation (Mathematics, 2023)
- On-demand security requirements synthesis with relational generative adversarial networks (ICSE, 2023)
- Ai-based question answering assistance for analyzing natural-language requirements (ICSE, 2023)
- Prcbert: Prompt learning for requirement classification using bert-based pretrained language models (ASE, 2023)
- A cross-level requirement trace link update model based on bidirectional encoder representations from transformers (Mathematics, 2023)
- A study about the knowledge and use of requirements engineering standards in industry (TSE, 2022)
- Detecting privacy requirements from user stories with NLP transfer learning models (Information and Software Technology, 2022)
- A software requirements ecosystem: Linking forum, issue tracker, and faqs for requirements management (TSE, 2022)
- Automated handling of anaphoric ambiguity in requirements: A multi-solution study (ICSE, 2022)
- Detecting coreferent entities in natural language requirements (Requirements Engineering, 2022)
- Information retrieval versus deep learning approaches for generating traceability links in bilingual projects ( Empirical Software Engineering, 2022)
- An end-to-end deep learning system for requirements classification using recurrent neural networks (Information and Software Technology, 2022)
- Traceability transformed: Generating more accurate links with pre-trained Bert models (ICSE, 2021)
- A deep multitask learning approach for requirements discovery and annotation from open forum (ASE, 2021)
- Automating developer chat mining (ASE, 2021)
- Bidirectional language modeling: A systematic literature review (Science Programming, 2021)
- Classifying user requirements from online feedback in small dataset environments using deep learning (RE, 2021)
- Codebert: A pre-trained model for programming and natural languages (ACL, 2020)
- Caspar: Extracting and synthesizing user stories of problems from app reviews (ICSE, 2020)
- Automated extraction of requirement entities by leveraging lstm-crf and transfer learning (ICSME, 2020)
- Automating intention mining (TSE, 2020)
- Detection of hidden feature requests from massive chat messages via deep siamese network (ICSE, 2020)
- A deep context-wise method for coreference detection in natural language requirements (RE, 2020)
- Norbert: Transfer learning for requirements classification (RE, 2020)
- Predicting how to test requirements: An automated approach (RE, 2019)
- Extraction of system states from natural language requirements (RE, 2019)
- Automatic multi-class non-functional software requirements classification using neural networks (COMPSAC, 2019)
- BioBERT: a pre-trained biomedical language representation model for biomedical text mining (Bioinformatics, 2019)
- Using the aman-da method to generate security requirements: A case study in the maritime domain (RE, 2018)
- Semantically enhanced software traceability using deep learning techniques (ICSE, 2017)
- Easy approach to requirements syntax (ears) (RE, 2009)
- Generating natural language specifications from UML class diagrams (RE, 2008)
- Deriving requirements from process models via the problem frames approach (Inf.Softw.Technol., 2005)
- Generating requirements from systems models using patterns: a case study (RE, 2005)
- Goal-oriented requirements engineering: a roundtrip from research to practice [enginering read engineering] (RE, 2004)
- Automating software requirements generation from business process models (PRISE, 2004)
- Deriving tabular event-based specifications from goal-oriented requirements models (Requir.Eng., 2004)
- The automated extraction of requirements from UML models (RE, 2003)
- Deriving operational software specifications from system goals (SIGSOFT, 2002)
- Inferring declarative requirements specifications from operational scenarios (Software Eng., 1998)
- From organization models to system requirements: A'cooperating agents' approach (CoopIS, 1995)
- Sifting through the Chaff: On Utilizing Execution Feedback for Ranking the Generated Code Candidates (arXiv, 2024)
- Measuring Code Efficiency Optimization Capabilities with ACEOB (arXiv, 2024)
- E-code: Mastering Efficient Code Generation through Pretrained Models and Expert Encoder Group (arXiv, 2024)
- IRCoCo: Immediate Rewards-Guided Deep Reinforcement Learning for Code Completion (ESEC/FSE, 2024)
- Knowledge-Aware Code Generation with Large Language Models (ICPC, 2024)
- KareCoder: A New Knowledge-Enriched Code Generation System (ICSE, 2024)
- Enhancing Code Generation Performance of Smaller Models by Distilling the Reasoning Ability of LLMs (LREC-COLING, 2024)
- RepoMasterEval: Evaluating Code Completion via Real-World Repositories (arXiv, 2024)
- TACO: Topics in Algorithmic COde generation dataset (arXiv, 2023)
- Exploitgen: Template-augmentedexploit code generation based on codebert (Journal of Systems and Software, 2023)
- Skcoder: A sketch-based approach for automatic code generation (arXiv, 2023)
- Practitioners’ expectations on code completion (arXiv, 2023)
- Domain adaptive code completion via language models and decoupled domain databases (arXiv, 2023)
- Codemark: Imperceptible watermarking for code datasets against neural code completion models (arXiv, 2023)
- Learning deep semantics for test completion (arXiv, 2023)
- On the robustness of code generation techniques: An empirical study on GitHub copilot (arXiv, 2023)
- In ChatGPT we trust? measuring and characterizing the reliability of ChatGPT (arXiv, 2023)
- Codefill: Multi-token code completion by jointly learning from structure and naming sequences (ICSE, 2022)
- Incorporating domain knowledge through task augmentation for front-end javascript code generation (ESEC/FSE, 2022)
- Coderl: Mastering code generation through pretrained models and deep reinforcement learning (NeurIPS, 2022)
- Lyra: A benchmark for turducken-style code generation (IJCAI, 2022)
- In-ide code generation from natural language: Promise and challenges (TOSEM, 2022)
- Compilable neural code generation with compiler feedback (ACL, 2022)
- Synchromesh: Reliable code generation from pre-trained language models (ICLR 2022)
- Codexglue: A machine learning benchmark dataset for code understanding and generation (NeurIPS, 2021)
- Analysis of tree-structured architectures for code generation (ACL-IJCNLP, 2021)
- Embedding api dependency graph for neural code generation (Empirical Software Engineering, 2021)
- Exploring dynamic selection of branch expansion orders for code generation (ACL, 2021)
- Codet5: Identifier-aware unified pre-trained encoder-decoder models for code understanding and generation (ACL, 2021)
- Unified pre-training for program understanding and generation (ACL, 2021)
- Retrieval augmented code generation and summarization (ACL, 2021)
- Measuring coding challenge competence with APPS ( NeurIPS, 2021)
- Code generation from natural language with less prior knowledge and more monolingual data (ACL, 2021)
- Improving tree-structured decoder training for code generation via mutual learning (AAAI, 2021)
- Multi-task learning based pre-trained language model for code completion (ASE 2021)
- Leveraging code generation to improve code retrieval and summarization via dual learning (WWW, 2020)
- Pymt5: Multi-mode translation of natural language and python code with transformers (ACL, 2020)
- Treegen: A tree-based transformer architecture for code generation (AAAI, 2020)
- A grammar-based structural cnn decoder for code generation (AAAI, 2019)
- Coupling retrieval and meta-learning for context-dependent semantic parsing (ACL, 2019)
- Code generation as a dual task of code summarization (NeurIPS, 2019)
- Spoc: Searchbased pseudocode to code (NeurIPS, 2019)
- Learning programmatic idioms for scalable semantic parsing (ACL, 2019)
- Coarse-to-fine decoding for neural semantic parsing (ACL, 2018)
- TRANX: A transition-based neural abstract syntax parser for semantic parsing and code generation (ACL, 2018)
- Semantic parsing with syntax- and table-aware SQL generation (ACL, 2018)
- Mapping language to code in programmatic context (ACL, 2018)
- Spider: A large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-sql task (EMNLP, 2018)
- Dlpaper2code: Auto-generation of code from deep learning research papers (AAAI, 2018)
- A retrieve-and-edit framework for predicting structured outputs (NeurIPS, 2018)
- A syntactic neural model for general-purpose code generation (ACL, 2017)
- Abstract syntax networks for code generation and semantic parsing (ACL, 2017)
- Latent predictor networks for code generation (ACL, 2016)
- Language to logical form with neural attention (ACL, 2016)
- Nl-based query refinement and contextualized code search results: A user study (CSMR-WCRE 2014)
- Empirical evaluation of gated recurrent neural networks on sequence modeling (arXiv, 2014)
- SNIFF: A search engine for java using free-form queries (ETAPS, 2009)
- Source code retrieval for bug localization using latent dirichlet allocation (WCRE 2008)
- xcodeeval: A large scale multilingual multitask benchmark for code understanding, generation, translation and retrieval (CoRR, 2023)
- Xcos: Explainable code search based on query scoping and knowledge graph (TOSEM, 2023)
- On the importance of building high-quality training datasets for neural code search (ICSE, 2022)
- Accelerating code search with deep hashing and code classification (ACL, 2022)
- Unixcoder: Unified cross-modal pre-training for code representation (ACL 2022)
- Coderetriever: Unimodal and bimodal contrastive learning (arXiv, 2022)
- Codexglue: A machine learning benchmark dataset for code understanding and generation (NeurIPS 2021)
- Cascaded fast and slow models for efficient semantic code search (CoRR, 2021)
- Interactive cross-language code retrieval with auto-encoders (ASE, 2021)
- Cosqa: 20, 000+ web queries for code search and question answering (ACL/IJCNLP 2021)
- Cross-language code search using static and dynamic analyses (ESEC/FSE, 2021)
- Automated query reformulation for efficient search based on query logs from stack overflow (ICSE, 2021)
- Graphsearchnet: Enhancing gnns via capturing global dependency for semantic code search. (CoRR, 2021)
- Graphcodebert: Pre-training code representations with data flow (ICLR, 2021)
- Neural code search revisited: Enhancing code snippet retrieval through natural language intent (CoRR, 2020)
- Are the code snippets what we are searching for? A benchmark and an empirical study on code search with natural-language queries (SANER, 2020)
- Codebert: A pre-trained model for programming and natural languages (ACL, 2020)
- Aroma: code recommendation via structural code search (Proc. ACM Program. Lang., 2019)
- Cross-language clone detection by learning over abstract syntax trees (MSR, 2019)
- Mohammad Masudur Rahman. Supporting code search with context-aware, analytics-driven, effective query reformulation (ICSE, 2019)
- Codesearchnet challenge: Evaluating the state of semantic code search (CoRR, 2019)
- ROSF: leveraging information retrieval and supervised learning for recommending code snippets (IEEE Trans. Serv. Comput., 2019)
- Multi-modal attention network learning for semantic source code retrieval (ASE 2019)
- Neural code search evaluation dataset (CoRR, 2019)
- Neural query expansion for code search (MAPL@PLDI, 2019)
- Deep code search (ICSE 2018)
- Retrieval on source code: a neural code search (MAPL@PLDI 2018)
- Graph embedding based code search in software project (Internetware, 2018)
- Staqc: A systematically mined question-code dataset from stack overflow (WWW, 2018)
- Learning to mine aligned code and natural language pairs from stack overflow (MSR, 2018)
- Exploring API embedding for API usages and applications (ICSE, 2017)
- Query expansion via wordnet for effective code search (SANER, 2015)
- Codehow: Effective code search based on API understanding and extended boolean model (E) (ASE, 2015)
- Improving source code search with natural language phrasal representations of method signatures (ASE, 2011)
- Sourcerer: a search engine for open source code supporting structure-based search (OOPSLA, 2006)
- Bcgen: a comment generation method for bytecode (ASEJ, 2023)
- Snippet comment generation based on code context expansion (TOSEM, 2023)
- Re trans: Combined retrieval and transformer model for source code summarization (Entropy, 2022)
- ATOM: commit message generation based on abstract syntax tree and hybrid ranking (TSE, 2022)
- M2TS: multi-scale multi-modal approach based on transformer for source code summarization (ICPC, 2022)
- Ast-trans: Code summarization with efficient tree-structured attention (ICSE, 2022)
- Source code summarization with structural relative position guided transformer (SANER, 2022)
- Gt-simnet: Improving code automatic summarization via multi-modal similarity networks (J. Syst. Softw., 2022)
- Keyword-guided abstractive code summarization via incorporating structural and contextual information (Inf. Softw. Technol., 2022)
- Modeling hierarchical syntax structure with triplet position for source code summarization (ACL, 2022)
- MMF3: neural code summarization based on multi-modal fine-grained feature fusion (ESEM, 2022)
- Gypsum: learning hybrid representations for code summarization (ICPC, 2022)
- Reinforcementlearning-guided source code summarization using hierarchical attention (TSE, 2022)
- Boosting code summarization by embedding code structures (ACL, 2022)
- Api2com: On the improvement of automatically generated code comments using API documentations (ICPC, 2021)
- Retrieval-augmented generation for code summarization via hybrid GNN (ICLR, 2021)
- Haconvgnn: Hierarchical attention based convolutional graph neural network for code documentation generation in jupyter notebooks (ACL, 2021)
- Ensemble models for neural source code summarization of subroutines (ICSME, 2021)
- Cosqa: 20, 000+ web queries for code search and question answering (ACL/IJCNLP, 2021)
- Project-level encoding for neural source code summarization of subroutines (ICPC, 2021)
- Exploiting method names to improve code summarization: A deliberation multi-task learning approach (ICPC, 2021)
- Secnn: A semantic CNN parser for code comment generation (J. Syst. Softw., 2021)
- Improving code summarization with block-wise abstract syntax tree splitting (ICPC, 2021)
- CAST: enhancing code summarization with hierarchical splitting and reconstruction of abstract syntax trees (ACL, 2021)
- Cocogum: Contextual code summarization with multi-relational gnn on umls (Microsoft, Tech. Rep., 2020)
- Transˆ3: A transformer-based framework for unifying code summarization and code search (CoRR, 2020)
- Deep code comment generation with hybrid lexical and syntactical information (Empir. Softw. Eng., 2020)
- Towards automatically generating block comments for code snippets (Inf. Softw. Technol., 2020)
- Pymt5: Multi-mode translation of natural language and python code with transformers (ACL, 2020)
- Improved code summarization via a graph neural network (ICPC, 2020)
- A transformer-based approach for source code summarization (ACL, 2020)
- Fret: Functional reinforced transformer with BERT for code summarization (IEEE Access, 2020)
- Retrieval-based neural source code summarization (ICSE, 2020)
- Augmenting java method comments generation wit context information based on neural networks (J. Syst. Softw., 2019)
- Structured neural summarization (ICLR, 2019)
- A neural model for generating natural language summaries of program subroutines (ICSE, 2019)
- Boosting neural commit message generation with code semantic analysis (ASE, 2019)
- Automatic generation of pull request descriptions (ASE, 2019)
- Code generation as a dual task of code summarization (Advances in neural information processing systems, 2019)
- Commit message generation for source code changes (IJCAI, 2019)
- Deep code comment generation (ICPC, 2018)
- Improving automatic source code summarization via deep reinforcement learning (ASE, 2018)
- Summarizing source code with transferred API knowledge (IJCAI, 2018)
- A neural framework for retrieval and summarization of source code (ASE 2018)
- A parallel corpus of python functions and documentation strings for automated code documentation and code generation (IJCNLP, 2017)
- Automatically generating commit messages from diffs using neural machine translation (ASE, 2017)
- Towards automatic generation of short summaries of commits (ICPC, 2017)
- Summarizing source code using a neural attention model (ACL, 2016)
- A convolutional attention network for extreme summarization of source code (ICML, 2016)
- Just-in-time code duplicates extraction (Information and Software Technology, 2023)
- An automated approach to extracting local variables (ESEC/FSE, 2023)
- Deep learning based feature envy detection boosted by real-world examples (ESEC/FSE, 2023)
- Automated Software Entity Matching Between Successive Versions (ASE, 2023)
- Detecting and Refactoring Feature Envy Based on Graph Neural Network (ISSRE, 2022)
- Recommending move method refactoring opportunities using structural and semantic representations of code (ICSME, 2022)
- On the value of oversampling for deep learning in software defect prediction (TSE, 2022)
- How to improve deep learning for software analytics: (a case study with code smell detection) (MSR, 2022)
- RefactoringMiner 2.0 (TSE, 2022)
- Deep learning based code smell detection (TSE, 2021)
- A deep method renaming prediction and refinement approach for Java projects (QRS, 2021)
- Graph neural network to dilute outliers for refactoring monolith application (AAAI, 2021)
- RefDiff 2.0: A multi-language refactoring detection tool (TSE, 2021)
- Local and Global Feature Based Explainable Feature Envy Detection (COMPSAC, 2021)
- Recent advances in deep learning (International Journal of Machine Learning and Cybernetics, 2020)
- Recommendation of Move Method Refactoring Using Path-Based Representation of Code (ICSEW, 2020)
- Feature requests-based recommendation of software refactorings (Empirical Software Engineering, 2020)
- Mlcq: Industry-relevant code smell data set (EASE, 2020)
- Deep Learning Anti-patterns from Code Metrics History (ICSME, 2019)
- Code2vec: Learning distributed representations of code (POPL, 2019)
- Bert: Pre-training of deep bidirectional transformers for language understanding (arXiv, 2019)
- Learning to spot and refactor inconsistent method names (ICSE, 2019)
- On learning meaningful code changes via neural machine translation (ICSE, 2019)
- Deep learning based feature envy detection (ASE, 2018)
- A novel heuristic and tool to detect move method refactoring opportunities (Journal of Systems and Software, 2018)
- Semeval-2017 task 4: Sentiment analysis in twitter (arXiv, 2017)
- Deep learning (nature, 2015)
- Identifying renaming opportunities by expanding conducted rename refactorings (TSE, 2015)
- Distributed representations of sentences and documents (PMLR, 2014)
- Ref-Finder: A refactoring reconstruction tool based on logic query templates (FSE, 2010)
- CLCD-I: Cross-Language Clone Detection by Using Deep Learning with InferCode (Computers, 2023)
- Using a nearest-neighbour, bert-based approach for scalable clone detection (ICSME, 2022)
- Codebert for code clone detection: A replication study (IWSC, 2022)
- Modeling functional similarity in source code with graph-based siamese networks (TSE, 2022)
- Seed: Semantic graph based deep detection for type-4 clone (Reuse and Software Quality, 2022)
- A collaborative method for code clone detection using a deep learning model (Advances in Engineering Software, 2022)
- Learning Program Semantics with Code Representations: An Empirical Study (SANER, 2022)
- Bridging pre-trained models and downstream tasks for source code understanding (ICSE, 2022)
- Software system comparison with semantic source code embeddings (Empirical Software Engineering, 2022)
- Unified abstract syntax tree representation learning for cross-language program classification (ICPC, 2022)
- Assessing and Improving an Evaluation Dataset for Detecting Semantic Code Clones via Deep Learning (TOSEM, 2022)
- BigCloneBench Considered Harmful for Machine Learning (IWSC, 2022)
- FCCA: Hybrid Code Representation for Functional Clone Detection Using Attention Networks (IEEE Transactions on Reliability, 2021)
- Two-Pass Technique for Clone Detection and Type Classification Using Tree-Based Convolution Neural Network (Applied Sciences, 2021)
- Learn To Align: A Code Alignment Network For Code Clone Detection (APSEC, 2021)
- InferCode: Self-Supervised Learning of Code Representations by Predicting Subtrees (ICSE, 2021)
- Code Representation Based on Hybrid Graph Modelling (Neural Information Processing, 2021)
- Can Neural Clone Detection Generalize to Unseen Functionalities? (ASE, 2021)
- LVMapper: A Large-Variance Clone Detector Using Sequencing Alignment Approach (IEEE Access, 2020)
- Semantic Code Clone Detection Via Event Embedding Tree and GAT Network (QRS, 2020)
- A Deep Neural Network-Based Approach to Finding Similar Code Segments (IEICE Transactions on Information and Systems, 2020)
- SCDetector: software functional clone detection based on semantic tokens analysis (ASE, 2020)
- Sia-RAE: A Siamese Network based on Recursive AutoEncoder for Effective Clone Detection (APSEC, 2020)
- Detecting Code Clones with Graph Neural Network and Flow-Augmented Abstract Syntax Tree (SANER, 2020)
- Functional code clone detection with syntax and semantics fusion learning (ISSTA, 2020)
- Review Sharing via Deep Semi-Supervised Code Clone Detection (IEEE Access, 2020)
- A Deep Learning Approach for a Source Code Detection Model Using Self-Attention (Complexity, 2020)
- SemanticCloneBench: A Semantic Code Clone Benchmark using Crowd-Source Knowledge (IWSC, 2020)
- Neural detection of semantic code clones via tree-based convolution (ICPC, 2019)
- A novel neural source code representation based on abstract syntax tree (ICSE, 2019)
- Fast Code Clone Detection Based on Weighted Recursive Autoencoders (IEEE Access, 2019)
- From Local to Global Semantic Clone Detection (DSA, 2019)
- Find Me if You Can: Deep Software Clone Detection by Exploiting the Contest between the Plagiarist and the Detector (AAAI, 2019)
- Learning-Based Recursive Aggregation of Abstract Syntax Trees for Code Clone Detection (SANER, 2019)
- Go-clone: graph-embedding based clone detector for Golang (ISSTA, 2019)
- Vulnerable Code Clone Detection for Operating System Through Correlation-Induced Learning (IEEE Transactions on Industrial Informatics, 2019)
- Capturing source code semantics via tree-based convolution over API-enhanced AST (Proceedings of the 16th ACM International Conference on Computing Frontiers, 2019)
- TECCD: A Tree Embedding Approach for Code Clone Detection (ICSME, 2019)
- Cross-Language Clone Detection by Learning Over Abstract Syntax Trees (MSR, 2019)
- CLCDSA: Cross Language Code Clone Detection using Syntactical Features and API Documentation (ASE, 2019)
- Bilateral Dependency Neural Networks for Cross-Language Algorithm Classification (SANER, 2019)
- Towards Automating Precision Studies of Clone Detectors (ICSE, 2019)
- SeSaMe: A Data Set of Semantically Similar Java Methods (MSR, 2019)
- DeepSim: deep learning code functional similarity (ESEC/FSE, 2018)
- Oreo: detection of clones in the twilight zone (ESEC/FSE, 2018)
- CCDLC Detection Framework-Combining Clustering with Deep Learning Classification for Semantic Clones (ICMLA, 2018)
- Positive and unlabeled learning for detecting software functional clones with adversarial training (IJCAI, 2018)
- Deep learning similarities from different representations of source code (MSR, 2018)
- CCAligner: a token based large-gap clone detector (ICSE, 2018)
- LICCA: A tool for cross-language clone detection (SANER, 2018)
- Clone-Slicer: Detecting Domain Specific Binary Code Clones through Program Slicing (FEAST, 2018)
- A deep learning approach to program similarity (MASES, 2018)
- Clone-hunter: accelerated bound checks elimination via binary code clone detection (MAPL, 2018)
- On the Use of Machine Learning Techniques Towards the Design of Cloud Based Automatic Code Clone Validation Tools (SCAM, 2018)
- CCLearner: A Deep Learning-Based Clone Detection Approach (ICSME, 2017)
- Fast and Flexible Large-Scale Clone Detection with CloneWorks (ICSE-C, 2017)
- Supervised deep features for software functional clone detection by exploiting lexical and syntactical information in source code (IJCAI, 2017)
- VUDDY: A Scalable Approach for Vulnerable Code Clone Discovery (IEEE Symposium on Security and Privacy (SP), 2017)
- Neural Network-based Graph Embedding for Cross-Platform Binary Code Similarity Detection (CCS, 2017)
- Sourcerercc: Scaling code clone detection to big-code (ICSE, 2016)
- Deep learning code fragments for code clone detection (ASE, 2016)
- Semantic Clone Detection Using Machine Learning (ICMLA, 2016)
- Convolutional Neural Networks over Tree Structures for Programming Language Processing (AAAI, 2016)
- Mining revision histories to detect cross-language clones without intermediates (ASE, 2016)
- Scalable Graph-based Bug Search for Firmware Images (CCS, 2016)
- Towards a Big Data Curated Benchmark of Inter-project Code Clones (ICSME, 2014)
- Qualitas.class corpus: a compiled version of the qualitas corpus (ACM SIGSOFT Software Engineering Notes, 2013)
- NICAD: Accurate Detection of Near-Miss Intentional Clones Using Flexible Pretty-Printing and Code Normalization (ICPC, 2008)
- DECKARD: Scalable and Accurate Tree-Based Detection of Code Clones (ICSE, 2007)
- Signature Verification using a "Siamese" Time Delay Neural Network (Proceedings of the 6th International Conference on Neural Information Processing Systems, 1993)
- DeepLineDP: Towards a Deep Learning Approach for Line-Level Defect Prediction (TSE, 2023)
- Defect Prediction via Tree-Based Encoding with Hybrid Granularity for Software Sustainability (IEEE Transactions on Sustainable Computing, 2023)
- Effort-Aware Just-in-Time Bug Prediction for Mobile Apps Via Cross-Triplet Deep Feature Embedding (IEEE Transactions on Reliability, 2022)
- ACGDP: An Augmented Code Graph-Based System for Software Defect Prediction (IEEE Transactions on Reliability, 2022)
- Software defect prediction employing BiLSTM and BERT-based semantic feature (Soft Computing, 2022)
- MPT-embedding: An unsupervised representation learning of code for software defect prediction (Journal of Software: Evolution and Process, 2021)
- GCN2defect : Graph Convolutional Networks for SMOTETomek-based Software Defect Prediction (ISSRE, 2021)
- Software Defect Prediction Based on Gated Hierarchical LSTMs (IEEE Transactions on Reliability, 2021)
- Joint feature representation learning and progressive distribution matching for cross-project defect prediction (Information and Software Technology, 2021)
- Software defect prediction based on stacked sparse denoising autoencoders and enhanced extreme learning machine (IET Software, 2021)
- Just-in-time software defect prediction using deep temporal convolutional networks (Neural Computing and Applications, 2021)
- Software visualization and deep transfer learning for effective software defect prediction (ICSE, 2020)
- Within-project and cross-project just-in-time defect prediction based on denoising autoencoder and convolutional neural network (IET Software, 2020)
- Deep Semantic Feature Learning for Software Defect Prediction (TSE, 2020)
- Software defect prediction via LSTM (IET Software, 2020)
- PathPair2Vec: An AST path pair-based code representation method for defect prediction (Journal of Computer Languages, 2020)
- SLDeep: Statement-level software defect prediction using deep-learning model on static code features (Expert Systems with Applications, 2020)
- How Well Do Change Sequences Predict Defects? Sequence Learning from Software Changes (TSE, 2020)
- Defect Prediction With Semantics and Context Features of Codes Based on Graph Representation Learning (IEEE Transactions on Reliability, 2020)
- Cross-project defect prediction via transferable deep learning-generated and handcrafted features (SEKE, 2019)
- DeepJIT: An End-to-End Deep Learning Framework for Just-in-Time Defect Prediction (MSR, 2019)
- Improving defect prediction with deep forest (Information and Software Technology, 2019)
- LDFR: Learning deep feature representation for software defect prediction (Journal of Systems and Software, 2019)
- Iterated feature selection algorithms with layered recurrent neural network for software fault prediction (Expert Systems with Applications, 2019)
- Lessons Learned from Using a Deep Tree-Based Model for Software Defect Prediction in Practice (MSR, 2019)
- Cross-project Defect Prediction via ASTToken2Vec and BLSTM-based Neural Network (IJCNN, 2019)
- Learning Semantic Features for Software Defect Prediction by Code Comments Embedding (ICDM, 2018)
- Connecting software metrics across versions to predict defects (SANER, 2018)
- Software defect prediction using stacked denoising autoencoders and two-stage ensemble learning (Information and Software Technology, 2018)
- Convolutional Neural Networks over Control Flow Graphs for Software Defect Prediction (ICTAI, 2017)
- Software Defect Prediction via Convolutional Neural Network (QRS, 2017)
- Deep Learning for Just-in-Time Defect Prediction (QRS, 2015)
- Software Testing With Large Language Models: Survey, Landscape, and Vision (TSE, 2024)
- Large Language Model guided Protocol Fuzzing (NDSS Symposium, 2024)
- UPBEAT: Test Input Checks of Q# Quantum Libraries (ISSTA, 2024)
- A Generative and Mutational Approach for Synthesizing Bug-Exposing Test Cases to Guide Compiler Fuzzing (ESEC/FSE 2023)
- Large Language Models for Software Engineering: A Systematic Literature Review (TOSEM, 2023)
- Learning to Boost Disjunctive Static Bug-Finders (ICSE, 2023)
- The Hitchhiker's Guide to Program Analysis: A Journey with Large Language Models (arXiv, 2023)
- A survey on neural-symbolic learning systems (Neural Networks, 2023)
- Revisiting Neural Program Smoothing for Fuzzing (ESEC/FSE, 2023)
- CodaMosa: Escaping Coverage Plateaus in Test Generation with Pre-trained Large Language Models (ICSE, 2023)
- Efficient Mutation Testing via Pre-Trained Language Models (arXiv, 2023)
- ChatUniTest: A Framework for LLM-Based Test Generation (arXiv, 2023)
- A3Test: Assertion-Augmented Automated Test case generation (Information and Software Technology, 2023)
- Neural-Based Test Oracle Generation: A Large-Scale Evaluation and Lessons Learned (ESEC/FSE, 2023)
- Towards More Realistic Evaluation for Neural Test Oracle Generation (ISSTA, 2023)
- No More Manual Tests? Evaluating and Improving ChatGPT for Unit Test Generation (arXiv, 2023)
- Learning Seed-Adaptive Mutation Strategies for Greybox Fuzzing (ICSE, 2023)
- Large language models are zero-shot fuzzers: Fuzzing deep-learning libraries via large language models (ISSTA, 2023)
- Large Language Models are Edge-Case Fuzzers: Testing Deep Learning Libraries via FuzzGPT (arXiv, 2023)
- WhiteFox: White-Box Compiler Fuzzing Empowered by Large Language Models (arXiv, 2023)
- Fuzz4All: Universal Fuzzing with Large Language Models (arXiv, 2023)
- Learning Deep Semantics for Test Completion (ICSE, 2023)
- RegFuzz: A Linear Regression-Based Approach for Seed Scheduling in Directed Fuzzing (Electronics, 2023)
- Evaluating and Improving Hybrid Fuzzing (ICSE, 2023)
- Detecting JVM JIT Compiler Bugs via Exploring Two-Dimensional Input Spaces (ICSE, 2023)
- Fill in the Blank: Context-aware Automated Text Input Generation for Mobile GUI Testing (ICSE, 2023)
- Efficiency Matters: Speeding Up Automated Testing with GUI Rendering Inference (ICSE, 2023)
- Badge: Prioritizing UI Events with Hierarchical Multi-Armed Bandits for Automated UI Testing (ICSE, 2023)
- APICAD: Augmenting API Misuse Detection through Specifications from Code and Documents (ICSE, 2023)
- Enhancing REST API Testing with NLP Techniques (ISSTA, 2023)
- Adaptive REST API Testing with Reinforcement Learning (ASE, 2023)
- On the Structure of the Boolean Satisfiability Problem: A Survey (ACM Computing Surveys, 2023)
- Machine Learning Methods in Solving the Boolean Satisfiability Problem (Machine Intelligence Research, 2023)
- LeanDojo: Theorem Proving with Retrieval-Augmented Language Models (NeurIPS, 2023)
- Ranking LLM-Generated Loop Invariants for Program Verification (arXiv, 2023)
- Do bugs lead to unnaturalness of source code? (ESEC/FSE, 2022)
- Towards Data-and Knowledge-Driven Artificial Intelligence: A Survey on Neuro-Symbolic Computing (arXiv, 2022)
- Evaluating and improving neural program-smoothing-based fuzzing (ICSE, 2022)
- Generating accurate assert statements for unit test cases using pretrained transformers (AST, 2022)
- Call Me Maybe: Using NLP to Automatically Generate Unit Test Cases Respecting Temporal Constraints (ASE, 2022)
- TOGA: a neural method for test oracle generation (ICSE, 2022)
- Neuroevolution-Based Generation of Tests and Oracles for Games (ASE, 2022)
- Fuzzing: A Survey for Roadmap (ACM Computing Surveys, 2022)
- Effectively Generating Vulnerable Transaction Sequences in Smart Contracts with Reinforcement Learning-guided Fuzzing (ASE, 2022)
- Avgust: automating usage-based test generation from videos of app executions (ESEC/FSE, 2022)
- SymTuner: maximizing the power of symbolic execution by adaptively tuning external parameters (ICSE, 2022)
- HyperTree Proof Search for Neural Theorem Proving (NeurIPS, 2022)
- Autoformalization with Large Language Models (NeurIPS, 2022)
- Diversity-driven automated formal verification (ICSE, 2022)
- Self-Supervised Bug Detection and Repair (NeurIPS, 2021)
- A Survey on Machine Learning Techniques for Source Code Analysis (arXiv, 2021)
- Learning type annotation: is big data enough? (ESEC/FSE, 2021)
- Experience Report: Deep Learning-based System Log Analysis for Anomaly Detection (arXiv, 2021)
- The Art, Science, and Engineering of Fuzzing: A Survey (TSE, 2021)
- Reinforcement Learning-based Hierarchical Seed Scheduling for Greybox Fuzzing (UC Riverside, 2021)
- Automated conformance testing for JavaScript engines via deep compiler fuzzing (PLDI, 2021)
- Graph-Based Fuzz Testing for Deep Learning Inference Engines (ICSE, 2021)
- Automatic Web Testing Using Curiosity-Driven Reinforcement Learning (ICSE, 2021)
- FIGCPS: Effective Failure-inducing Input Generation for Cyber-Physical Systems with Deep Reinforcement Learning (ASE, 2021)
- Deep GUI: Black-box GUI Input Generation with Deep Learning (ASE, 2021)
- Learning to Explore Paths for Symbolic Execution (CCS, 2021)
- Synthesize solving strategy for symbolic execution (ISSTA, 2021)
- Boosting symbolic execution via constraint solving time prediction (experience paper) (ISSTA, 2021)
- Have You been Properly Notified? Automatic Compliance Analysis of Privacy Policy Text with GDPR Article 13 (WWW, 2021)
- Safe systems programming in Rust (Communications of the ACM, 2021)
- Learning graph-based heuristics for pointer analysis without handcrafting application-specific features (OOPSLA, 2020)
- Learning fast and precise numerical analysis (PLDI, 2020)
- MTFuzz: fuzzing with a multi-task neural network (ESEC/FSE, 2020)
- Unit Test Case Generation with Transformers and Focal Context (arXiv, 2020)
- On learning meaningful assert statements for unit test cases (ICSE, 2020)
- Reinforcement learning based curiosity-driven testing of Android applications (ISSTA, 2020)
- Efficient multiplex symbolic execution with adaptive search strategy (ASE, 2020)
- Making symbolic execution promising by learning aggressive state-pruning strategy (ESEC/FSE, 2020)
- API-misuse detection driven by fine-grained API-constraint knowledge graph (ASE, 2020)
- RTFM! Automatic Assumption Discovery and Verification Derivation from Library Document for API Misuse Detection (CCS, 2020)
- Montage: A Neural Network Language Model-Guided JavaScript Engine Fuzzer (USENIX, 2020)
- Zeror: speed up fuzzing with coverage-sensitive tracing and scheduling (ASE, 2020)
- FuzzGuard: Filtering out Unreachable Inputs in Directed Grey-box Fuzzing through Deep Learning (USENIX, 2020)
- Resource-Aware Program Analysis Via Online Abstraction Coarsening (ICSE, 2019)
- NL2Type: Inferring JavaScript Function Types from Natural Language Information (ICSE, 2019)
- NEUZZ: Efficient Fuzzing with Neural Program Smoothing (SP, 2019)
- Machine Learning Applied to Software Testing: A Systematic Mapping Study (IEEE Transactions on Reliability, 2019)
- Neufuzz: Efficient fuzzing with deep neural network (IEEE Access, 2019)
- Learning-Guided Network Fuzzing for Testing Cyber-Physical System Defences (ASE, 2019)
- Learning to Fuzz from Symbolic Execution with Application to Smart Contracts (CCS, 2019)
- A Survey of Symbolic Execution Techniques (ACM Computing Surveys, 2019)
- Concolic testing with adaptively changing search heuristics (ESEC/FSE, 2019)
- A Large-Scale Empirical Study on Code-Comment Inconsistencies (ICPC, 2019)
- Ares: Inferring Error Specifications through Static Analysis (ASE, 2019)
- DeepFuzz: Automatic Generation of Syntax Valid C Programs for Fuzz Testing (AAAI, 2019)
- Full-Speed Fuzzing: Reducing Fuzzing Overhead through Coverage-Guided Tracing (SP, 2019)
- Fuzzing: a survey (Cybersecurity, 2018)
- Compiler fuzzing through deep learning (ISSTA, 2018)
- Automatically generating search heuristics for concolic testing (ICSE, 2018)
- Learning to Accelerate Symbolic Execution via Code Transformation (ECOOP, 2018)
- GSP: an automatic programming technique with gravitational search algorithm (Applied Intelligence, 2018)
- Fuzzing for software security testing and quality assurance (2018)
- Angora: Efficient Fuzzing by Principled Search (SP, 2018)
- Automatically generating features for learning program analysis heuristics for C-like languages (OOPSLA, 2017)
- Machine-Learning-Guided Selectively Unsound Static Analysis (ICSE, 2017)
- Data-driven context-sensitivity for points-to analysis (OOPSLA, 2017)
- SemFuzz: Semantics-based Automatic Generation of Proof-of-Concept Exploits (CCS, 2017)
- Learn&Fuzz: Machine learning for input fuzzing (ASE, 2017)
- On the naturalness of software (Communications of the ACM, 2016)
- Automated Analysis of Privacy Requirements for Mobile Apps (AAAI, 2016)
- ICON: Inferring Temporal Constraints from Natural Language API Descriptions (ICSME, 2016)
- APISan: Sanitizing API Usages through Semantic Cross-Checking (USENIX, 2016)
- APEx: automated inference of error specifications for C APIs (ASE, 2016)
- In defense of soundiness: A manifesto (Communications of the ACM, 2015)
- Learning to Execute (arXiv, 2014)
- Enhancing symbolic execution with veritesting (ICSE, 2014)
- Regression testing minimization, selection and prioritization: a survey (ISSTA, 2013)
- Distributed Representations of Words and Phrases and their Compositionality (NIPS, 2013)
- AddressSanitizer: A Fast Address Sanity Checker (USENIX, 2012)
- Software Abstractions: logic, language, and analysis (2012)
- @tComment: Testing Javadoc Comments to Detect Comment-Code Inconsistencies (Software Testing, Verification and Validation, 2012)
- Expect the unexpected: error code mismatches between documentation and the real world (PASTE, 2010)
- Formal verification of a realistic compiler (Communications of the ACM, 2009)
- seL4: formal verification of an OS kernel (SOSP, 2009)
- ThreadSanitizer: data race detection in practice (WBIA, 2009)
- /icomment: bugs or bad comments?/ (SOSP, 2007)
- CP-Miner: finding copy-paste and related bugs in large-scale software code (TSE, 2006)
- PEP 8–style guide for python code (2001)
- Bugs as deviant behavior: a general approach to inferring errors in systems code (ACM SIGOPS Operating Systems Review, 2001)
- Java coding style guide (2000)
- A study of effective regression testing in practice (ISSRE, 1997)
- Structure and Interpretation of Computer Programs (1996)
- On the approximate realization of continuous mappings by neural networks (Neural Networks, 1989)
- Natural deduction as higher-order resolution (The Journal of Logic Programming, 1986)
- Lint, a C program checker (1977)
- Classes of Recursively Enumerable Sets and Their Decision Problems (Transactions of the American Mathematical Society, 1953)
- Gnet4fl: effective fault localization via graph convolutional neural network (ASE, 2023)
- Context-aware neural fault localization (TSE, 2023)
- Automatic bug localization using a combination of deep learning and model transformation through node classification (SQJ, 2023)
- Gmbfl: Optimizing mutation-based fault localization via graph representation. (ICSME), 2023)
- A light-weight data augmentation method for fault localization (IST, 2023)
- Mitigating the effect of class imbalance in fault localization using context-aware generative adversarial network (ICPC, 2023)
- Influential global and local contexts guided trace representation for fault localization (TSEM, 2023)
- Fault localization to detect co-change fixing locations (ESEC, 2022)
- Context-based cluster fault localization (ICPC, 2022)
- Graph neural network based two-phase fault localization approach (APSI, 2022)
- Fast changeset-based bug localization with bert (ICSE, 2022)
- Bcl-fl: A data augmentation approach with between-class learning for fault localization (SANER, 2022)
- Learning to construct better mutation faults (ICASE, 2022)
- Improving fault localization using model-domain synthesized failing test generation (ICSME, 2022)
- A universal data augmentation approach for fault localization (ICSE, 2022)
- Boosting coverage-based fault localization via graph-based representation learning. (ESEC, 2021)
- Agfl: a graph convolutional neural network-based method for fault localization (QRS, 2021)
- A study of effectiveness of deep learning in locating real faults (IST, 2021)
- Improving deep-learning-based fault localization with resampling (JSEP, 2021)
- Hierarchically localizing software faults using dnn (TR, 2020)
- Bugpecker: Locating faulty methods with deep learning on revision graphs (ASE, 2020)
- Learning a graph-based classifier for fault localization (SCIS, 2020)
- Deepfl: Integrating multiple fault diagnosis dimensions for deep fault localization (ISSTA, 2019)
- Cnn-fl: An effective approach for localizing faults using convolutional neural networks (SANER, 2019)
- Fault localization with code coverage representation learning (ICSE, 2019)
- Bears: An extensible java bug benchmark for automatic program repair studies (SANER, 2019)
- Deep learning-based fault localization with contextual information (TIS, 2017)
- Fault localization analysis based on deep neural network (MPE, 2016)
- Defects4j: A database of existing faults to enable controlled testing studies for java programs (ISSTA, 2014)
- The manybugs and introclass benchmarks for automated repair of c programs (TSE, 2015)
- Effective software fault localization using an rbf neural network (TR, 2012)
- Bp neural network-based effective fault localization (International Journal of Software Engineering and Knowledge Engineering, 2009)
- Supporting controlled experimentation with testing techniques:An infrastructure and its potential impact (ESE, 2005)
- ThinkRepair: Self-Directed Automated Program Repair (ISSTA, 2024)
- MarsCode Agent: AI-native Automated Bug Fixing (arXiv, 2024)
- Synshine: Improved fixing of syntax errors (TSE, 2023)
- Learning approximate execution semantics from traces for binary function similarity (TSE, 2023)
- Program repair with repeated learning (TSE, 2023)
- Neural transfer learning for repairing security vulnerabilities in C code (TSE, 2023)
- Seqtrans: Automatic vulnerability fix via sequence to sequence learning (TSE, 2023)
- Seq2parse: neurosymbolic parse error repair (PACMPL, 2022)
- Transrepair: Context-aware program repair for compilation errors. (ASE, 2022)
- DEAR: A novel deep learning-based approach for automated program repair (ICSE, 2022)
- M3V: multi-modal multi-view context embedding for repair operator prediction (CGO, 2022)
- Improving fault localization and program repair with deep semantic features and transferred knowledge (ICSE, 2022)
- Impact of defect instances for successful deep learning-based automatic program repair (ICSME, 2022)
- Deepdiagnosis: Automatically diagnosing faults and recommending actionable fixes in deep learning programs (ICSE, 2022)
- Bug-transformer: Automated program repair using attention-based deep neural network (CSC, 2022)
- Crex:Predicting patch correctness in automated repair of C programs through transfer learning of execution semantics (IST, 2022)
- CODIT: code editing with tree-based neural models (TSE, 2022)
- Neural program repair with execution-based backpropagation (ICSE, 2022)
- Automated classification of overfitting patches with statically extracted code features (TSE, 2022)
- Selfapr: Self-supervised program repair with test execution diagnostics (ASE, 2022)
- Less training, more repairing please: revisiting automated program repair via zero-shot learning (ESEC, 2022)
- An empirical study of deep transfer learning-based program repair for kotlin projects (ESEC, 2022)
- Predicting patch correctness based on the similarity of failing test cases (TOSEM, 2022)
- CIRCLE:continual repair across programming languages (ISSTA, 2022)
- Gui-guided test script repair for mobile apps (TSE, 2022)
- Automated patching for unreproducible builds (ICSE, 2022)
- Styler: learning formatting conventions to repair checkstyle violations (ESE, 2022)
- SPVF: security property assisted vulnerability fixing via attention-based models (ESE, 2022)
- Repairing security vulnerabilities using pre-trained programming language models (DSN-W, 2022)
- Bugbuilder: An automated approach to building bug repository (TSE, 2022)
- Vul4j: A dataset of reproducible java vulnerabilities geared towards the study of program repair techniques (MSR, 2022)
- Self-supervised bug detection and repair (NIPS, 2021)
- Samplefix: Learning to generate functionally diverse fixes (CCIS, 2021)
- Break-it-fix-it: Unsupervised learning for program repair (ICLM, 2021)
- Learning lenient parsing & typing via indirect supervision (ESE, 2021)
- A robustly optimized BERT pre-training approach with post-training (CCL, 2021)
- Varfix: balancing edit expressiveness and search effectiveness in automated program repair (ESEC, 2021)
- Sequencer:Sequence-to-sequence learning for end-to-end program repair (TSE, 2021)
- CURE: code-aware neural machine translation for automatic program repair (ICSE, 2021)
- Grammar-based patches generation for automated program repair. (ACL, 2021)
- Application of seq2seq models on code correction (Frontiers Artif. Intell, 2021)
- A bidirectional LSTM language model for code evaluation and repair (Symmetry, 2021)
- Tfix: Learning to fix coding errors with a text-to-text transformer (ICML, 2021)
- Grasp: Graph-to-sequence learning for automated program repair (QRS, 2021)
- Detecting and fixing nonidiomatic snippets in python source code with deep learning (ISA, 2021)
- Extracting concise bug-fixing patches from human-written patches in version control systems (ICSE, 2021)
- Crossvul: A cross-language vulnerability dataset with commit data (ESEC, 2021)
- C-3PR: A bot for fixing static analysis violations via pull requests (SANER, 2020)
- Dlfix: context-based code transformation learning for automated program repair (ICSE, 2020)
- GGF: A graph-based method for programming language syntax error correction (ICPC, 2020)
- Graph-based, self-supervised program repair from diagnostic feedback (ICLM, 2020)
- Patching as translation: the data and the metaphor (ASE, 2020)
- Applying deep learning algorithm to automatic bug localization and repair (SAC, 2020)
- Coconut: combining contextaware neural translation models using ensemble for program repair (ISSTA, 2020)
- Evaluating representation learning of code changes for predicting patch correctness in program repair (ASE, 2020)
- Hoppity: Learning graph transformations to detect and fix bugs in programs (ICLR, 2020)
- On learning meaningful code changes via neural machine translation (ICSE, 2019)
- Deepdelta: learning to repair compilation errors (ESEC, 2019)
- Deep reinforcement learning for syntactic error repair in student programs (AAAI, 2019)
- Progress on software crash research (SCIENTIA SINICA Informationis, 2019)
- Harnessing evolution for multi-hunk program repair (ICSE, 2019)
- Tbar: revisiting template-based automated program repair (ISSTA, 2019)
- Sorting and transforming program repair ingredients via deep learning code similarities (SANER, 2019)
- An empirical investigation into learning bug-fixing patches in the wild via neural machine translation. (ASE, 2019)
- An automatic semantic code repair service based on deep learning for programs with single error (SERVICES, 2019)
- History-driven build failure fixing: how far are we? (ISSTA, 2019)
- Neuro-symbolic program corrector for introductory programming assignments (ICSE, 2018)
- Compilation error repair: for the student programs, from the student programs (SEET, 2018)
- Syntax and sensibility: Using language models to detect and correct syntax errors (SANER, 2018)
- Bugs.jar: a large-scale, diverse dataset of real-world java bugs (MSR, 2018)
- Visual web test repair (ESEC, 2018)
- Hirebuild: an automatic approach to history-driven repair of build scripts (ICSE, 2018)
- Learning to repair software vulnerabilities with generative adversarial networks (NIPS, 2018)
- Blackbox, five years on: An evaluation of a largescale programming data collection project (ICER 2018)
- Do automated program repair techniques repair hard and important bugs? (ICSE, 2018)
- Deepfix: Fixing common C language errors by deep learning (AAAI, 2017)
- Automatically diagnosing and repairing error handling bugs in C (ESEC, 2017)
- Nopol: Automatic repair of conditional statement bugs in java programs (TSE, 2017)
- Seqgan: Sequence generative adversarial nets with policy gradient (AAAI, 2017)
- Automatic repair of real bugs in java: a large-scale experiment on the defects4j dataset (ESE, 2017)
- Vurle: Automatic vulnerability detection and repair by learning from examples (Computer Security -ESORICS, 2017)
- Automatic repair of real bugs in java: A large-scale experiment on the defects4j dataset (ESE,2017)
- Angelix: scalable multiline program patch synthesis via symbolic analysis (ICSE, 2016)
- Prutor: A system for tutoring cs1 and collecting student programs for analysis (ARXIV, 2016)
- Automatic patch generation by learning correct code (ACM SIGPLAN Notices, 2016)
- relifix: Automated repair of software regressions (ICSE, 2015)
- Blackbox: a large scale repository of novice programmers’ activity (SIGCSE, 2014)
- Defects4j: a database of existing faults to enable controlled testing studies for java programs. (ISSTA, 2014)
- Semfix: program repair via semantic analysis (ICSE, 2013)
- Genprog: A generic method for automatic software repair (TSE, 2012)
- A systematic study of automated program repair: Fixing 55 out of 105 bugs for $8 each (ICSE, 2012)
- Automatic error correction of java prnizaograms (FMICS 2010)
- A Practical Method for Lr and Ll Syntactic Error Diagnosis and Recovery (TOPLAS, 1987)
- Locally least-cost error recovery in early’s algorithm (TOPLAS, 1981)
- Practical syntactic error recovery (Communications of the ACM, 1975)
- A minimum distance error-correcting parser for context-free languages (SIAM, 1972)
- Duplicate bug report detection: How far are we? (TOSEM, 2023)
- Leveraging multi-level embeddings for knowledge-aware bug report reformulation. (JSS, 2023)
- Does deep learning improve the performance of duplicate bug report detection? (JSS, 2023)
- Deep learning and gradient-based extraction of bug report features related to bug fixing time (Front.Comput.Sci., 2023)
- Gen-FL: Quality prediction-based filter for automated issue title generation (JSS, 2023)
- bjXnet: an improved bug localization model based on code property graph and attention mechanism (Automated Software Engineering, 2023)
- Fast changeset-based bug localization with bert (ICSE, 2022)
- Automatic bug triaging via deep reinforcement learning (APPL.SCI, 2022)
- Modeling function-level interactions for file-level bug localization (ESE, 2022)
- Duplicate bug report detection by using sentence embedding and fine-tuning (ICSME, 2021)
- How to cherry pick the bug report for better summarization? (ESE, 2021)
- Automatically recommending components for issue reports using deep learning (ESE, 2021)
- Automatically matching bug reports with related app reviews (ICSE, 2021)
- Automating intention mining (TSE, 2020)
- Bugpecker: Locating faulty methods with deep learning on revision graphs (ASE, 2020)
- Duplicate bug report detection using dual-channel convolutional neural networks (ICPC, 2020)
- Hindbr: Heterogeneous information network based duplicate bug report prediction (ISSRE, 2020)
- Bugsum: Deep context understanding for bug report summarization (ICPC, 2020)
- Stay professional and efficient:automatically generate titles for your bug reports (ASE, 2020)
- Multi-dimension convolutional neural network for bug localization (TSC, 2020)
- A similarity integration method based information retrieval and word embedding in bug localization (QRS, 2020)
- Cooba: Cross-project bug localization via adversarial transfer learning (IJCAI, 2020)
- An empirical assessment of machine learning approaches for triaging reports of a Java static analysis tool (ICST, 2019)
- Deeptriage: Exploring the effectiveness of deep learning for bug triaging (CODS-COMAD, 2019)
- Bug report severity level prediction in open source software: A survey and research opportunities (IST, 2019)
- Improving bug localization with word embedding and enhanced convolutional neural networks (IST, 2019)
- Deep transfer bug localization (TSE, 2019)
- DeepLink: Recovering issue-commit links based on deep learning (JSS, 2019)
- Deeplink: A code knowledge graph based deep learning approach for issue-commit link recovery (SANER, 2019)
- Exploring word embedding techniques to improve sentiment analysis of software engineering texts (MSR, 2019)
- How practitioners perceive automated bug report management techniques (TSE, 2018)
- Unsupervised deep bug report summarization (ICPC, 2018)
- DWEN: deep word embedding network for duplicate bug report detection in software repositories (ICSE-COMPAION, 2018)
- Detecting duplicate bug reports with convolutional neural networks (APSEC, 2018)
- Bug localization by learning to rank and represent bug inducing changes (CIKM, 2018)
- An effective approach for routing the bug reports to the right fixers (APSI, 2018)
- Automatic approval prediction for software enhancement requests (Automated Software Engineering, 2018)
- Bridging semantic gaps between natural languages and APIs with word embedding (TSE, 2018)
- Superneurons:Dynamic GPU memory management for training deep neural networks (ACM SIGPLAN Not, 2018)
- Machine learning-based prototyping of graphical user interfaces for mobile apps (TSE, 2018)
- Improving automatic source code summarization via deep reinforcement learning (ASE, 2018)
- Towards accurate duplicate bug retrieval using deep learning techniques (ICSME, 2017)
- Parallel implementation of a bug report assignment recommender using deep learning (ICANN, 2017)
- Applying deep learning based automatic bug triager to industrial projects (ESEC, 2017)
- Learning to predict severity of software vulnerability using only vulnerability description (ICSME, 2017)
- Improving bug localization with an enhanced convolutional neural network (APSEC, 2017)
- Bug localization with combination of deep learning and information retrieval (ICPC, 2017)
- Applying deep learning based automatic bug triager to industrial projects (ESEC, 2017)
- Easy over hard: A case study on deep learning (ESEC, 2017)
- Exploring API embedding for API usages and applications (ICSE, 2017)
- VDNN: Virtualized deep neural networks for scalable, memory-efficient neural network design (MICRO, 2016)
- Combining deep learning with information retrieval to localize buggy files for bug reports (n) (ASE, 2015)
- Using knowledge units of programming languages to recommend reviewers for pull requests: An empirical study (ESE, 2023)
- Competencies for code review (Proceedings of the ACM on Human-Computer Interaction, 2023)
- Code recommendation for open source software developers (WWW, 2023)
- Dual analysis for helping developers to find collaborators based on co-changed files: An empirical study (CAPSE, 2023)
- A collaboration-aware approach to profiling developer expertise with cross-community data (QRS, 2022)
- Dev2vec: Representing domain expertise of developers in an embedding space. (IST, 2022)
- Context- and fairness-aware in-process crowdworker recommendation (TOSEM, 2022)
- Using large-scale heterogeneous graph representation learning for code review recommendations at microsoft (ICSE-SEIP, 2022)
- Modeling review history for reviewer recommendation: A hypergraph approach (ICSE, 2022)
- Recommending good first issues in github oss projects (ICSE, 2022)
- Supporting the task-driven skill identification in open source project issue tracking systems (SEN, 2022)
- Coopfinder: Finding collaborators based on co–changed files (VL/HCC, 2022)
- Mining the technical roles of github users (IST, 2021)
- Context-aware personalized crowdtesting task recommendation (TSE, 2021)
- Recommending participants for collaborative merge sessions (TSE, 2021)
- Representation of developer expertise in open source software (ICSE, 2020)
- Discovering software developer’s coding expertise through deep learning (IET SOFTWARE, 2020)
- Multi-objective code reviewer recommendations: balancing expertise, availability and collaborations (Automated Software Engineering, 2020)
- Does reviewer recommendation help developers (TSE, 2020)
- Best answerers prediction with topic based gat in q&a sites (INTERNETWARE, 2020)
- World of code: An infrastructure for mining the universe of open source vcs data (MSR, 2019)
- Developer recommendation for topcoder through a meta-learning based policy model (ESE, 2019)
- Cross-domain developer recommendation algorithm based on feature matching (CSC, 2019)
- Towards a theory of software development expertise. (ESEC, 2018)
- Towards quantifying the development value of code contributions (ESEC, 2018)
- Profiling developer expertise across software communities with heterogeneous information network analysis (INTERNETWARE, 2018)
- Personalized teammate recommendation for crowdsourced software developers (ASE, 2018)
- I know what you coded last summer: Mining candidate expertise from github repositories (CSCW, 2017)
- Github and stack overflow: Analyzing developer interests across multiple social collaborative platforms (Social Informatics, 2017)
- Recommending crowdsourced software developers in consideration of skill improvement (ASE, 2017)
- Who should comment on this pull request? analyzing attributes for more accurate commenter recommendation in pull-based development (IST, 2017)
- Cpdscorer: Modeling and evaluating developer programming ability across software communities (SEKE, 2016)
- leveraging expertise and authority for pull-request reviewer recommendation in github (CSI-SE, 2016)
- Automatically recommending peer reviewers in modern code review (TSE, 2016)
- Automatically recommending code reviewers based on their expertise: An empirical comparison (ASE, 2016)
- From developer networks to verified communities: a fine-grained approach (ICSE, 2015)
- Tbil: A tagging-based approach to identity linkage across software communities (APSEC, 2015)
- Distributed representations of sentences and documents (ICML, 2014)
- Degree-of-knowledge: Modeling a developer’s knowledge of code (TOSEM, 2014)
- Hydra: Large-scale social identity linkage via heterogeneous behavior modeling (SIGMOD, 2O14)
- Mining software repositories for accurate authorship (ICSM, 2013)
- Discovery of technical expertise from open source code repositories (WWW, 2013)
- Who’s who in gnome: Using lsa to merge software repository identities (ICSM, 2012)
- Who is going to mentor newcomers in open source projects? (FSE, 2012)
- Recommending people in developers’ collaboration network (WCRE, 2011)
- Developer fluency: Achieving true mastery in software projects (FSE, 2010)
- Expert recommendation with usage expertise (ICSM, 2009)
- Who should fix this bug? (ICSE, 2006)
- Expertise browser: A quantitative approach to identifying expertise (ICSE, 2002)
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