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SE Tasks

Table of contents

  1. Code Completion
  2. Code comment generation
  3. Code search
  4. Code summarization
  5. Program synthesis
  6. Program Repair
  7. Code clone detection
  8. Bug report analysis
  9. Code Review
  10. Test generation
  11. Vulnerability detection
  12. Code Generation

Code Completion

  • A systematic evaluation of large language models of code, 2022;
  • An empirical study on the usage of transformer models for code completion, 2021, link
  • Automatic detection and analysis of technical debts in peer-review documentation of r packages, 2022, link
  • CCTest: Testing and repairing code completion systems, 2023, link
  • CodeFill: Multi-token code completion by jointly learning from structure and naming sequences, 2022, link
  • Evaluating and improving transformers pre-trained on ASTs for code completion, 2023, link
  • Examining zero-shot vulnerability repair with large language models, 2022, link
  • From Copilot to Pilot: Towards AI supported software development, 2023, link
  • Making the most of small Software Engineering datasets with modern machine learning, 2022, link
  • Multi-task learning based pre-trained language model for code completion, 2020, link
  • Natural Language Generation and Understanding of Big Code for AI-Assisted Programming: A Review, 2023;
  • Piloting Copilot and Codex: Hot temperature, cold prompts, or black magic?, 2023, link
  • RepoBench: Benchmarking repository-level code auto-completion systems, 2023, link
  • Toward less hidden cost of code completion with acceptance and ranking models, 2021, link

Code comment generation

  • An Empirical Study on Code Comment Completion, 2021, link
  • Large Language Models are Few-shot Summarizers: Multi-intent Comment Generation via In-context Learning, 2023, link
  • Cross-Modal Contrastive Learning for Code Search, 2022, link
  • Do Pre-trained Language Models Indeed Understand Software Engineering Tasks?, 2022, link
  • Generation-Augmented Query Expansion for Code Retrieval, 2022, link
  • On Contrastive Learning of Semantic Similarity for Code-to-Code Search, 2023, link
  • On the Effectiveness of Transfer Learning for Code Search, 2023, link

Code summarization

  • Assemble Foundation Models for Automatic Code Summarization, 2022, link
  • Constructing Effective In-Context Demonstration for Code Intelligence Tasks: An Empirical Study, 2023, link
  • Exploring Distributional Shifts in Large Language Models for Code Analysis, 2023, link
  • Extending Source Code Pre-trained Language Models to Summarise Decompiled Binaries, 2023, link
  • Improving Few-shot Prompts with Relevant Static Analysis Products, 2023, link
  • Multilingual Adapter-based Knowledge Aggregation on Code Summarization for Low-Resource Languages, 2023
  • On the Transferability of Pre-trained Language Models for Low-Resource Programming Languages, 2022, link
  • Studying the Usage of Text-to-Text Transfer Transformer to Support Code-Related Tasks, 2021, link
  • Using Transfer Learning for Code-Related Tasks, 2023, link

Program synthesis

  • Evaluating ChatGPT and GPT-4 for Visual Programming, 2023
  • Exploring the Robustness of Large Language Models for Solving Programming Problems, 2023
  • Jigsaw: Large Language Models Meet Program Synthesis, 2022, link
  • Less is More: Summary of Long Instructions is Better for Program Synthesis, 2022
  • Natural Language Commanding via Program Synthesis, 2023, link

Program Repair

  • Toward less hidden cost of code completion with acceptance and ranking models, 2021, link
  • A Chain of AI-based Solutions for Resolving FQNs and Fixing Syntax Errors in Partial Code, 2023
  • a new era in software security: towards self-healing software via large language models and formal verification, 2023, link
  • a prompt pattern catalog to enhance prompt engineering with chatgpt, 2023, link
  • a study on prompt design, advantages and limitations of chatgpt for deep learning program repair, 2023, link
  • Addressing Compiler Errors: Stack Overflow or Large Language Models?, 2023
  • an analysis of the automatic bug fixing performance of chatgpt, 2023, link
  • automated repair of programs from large language models, 2023, link
  • boosting automated patch correctness prediction via pre-trained language model, 2023, link
  • circle: continual repair across programming languages, 2022, link
  • constructing effective in-context demonstration for code intelligence tasks: an empirical study, 2023, link
  • conversational automated program repair, 2023, link
  • Enhancing Automated Program Repair through Fine-tuning and Prompt Engineering, 2023
  • evaluating representation learning of code changes for predicting patch correctness in program repair, 2020, link
  • how effective are neural networks for fixing security vulnerabilities, 2023, link
  • inferfix: end-to-end program repair with llms, 2023, link
  • invalidator: automated patch correctness assessment via semantic and syntactic reasoning, 2023, link
  • is chatgpt the ultimate programming assistant – how far is it?, 2023, link
  • keep the conversation going: fixing 162 out of 337 bugs for $0.42 each using chatgpt, 2023, link
  • neural program repair with program dependence analysis and effective filter mechanism, 2023, link
  • practical program repair in the era of large pre-trained language models, 2022, link
  • the best of both worlds: combining learned embeddings with engineered features for accurate prediction of correct patches, 2023, link
  • towards javascript program repair with generative pre-trained transformer (gpt-2), 2022, link
  • using transfer learning for code-related tasks, 2023

Code clone detection

  • an exploratory study on code attention in bert, 2022, link
  • Towards Understanding the Capability of Large Language Models on Code Clone Detection: A Survey, 2023
  • Utilization of Pre-trained Language Model for Adapter-based Knowledge Transfer in Software Engineering, 2023

Bug report analysis

  • fast changeset-based bug localization with bert, 2022, link
  • Few-shot learning for sentence pair classification and its applications in software engineering, 2023
  • large language models are few-shot testers: exploring llm-based general bug reproduction, 2022, link

Code Review

  • A Multi-Step Learning Approach to Assist Code Review, 2023
  • aspect-based api review classification: how far can pre-trained transformer model go?, 2022, link
  • auger: automatically generating review comments with pre-training models, 2022, link
  • Automated Summarization of Stack Overflow Posts, 2023, link
  • coditt5: pretraining for source code and natural language editing, 2022, link
  • using pre-trained models to boost code review automation, 2022, link

Test generation

  • adaptive test generation using a large language model, 2023, link
  • algo: synthesizing algorithmic programs with generated oracle verifiers, 2023, link
  • Can Large Language Models Write Good Property-Based Tests?, 2023
  • chatunitest: a chatgpt-based automated unit test generation tool, 2023, link
  • exploring the effectiveness of large language models in generating unit tests, 2023, link
  • no more manual tests? evaluating and improving chatgpt for unit test generation, 2023, link

Vulnerability detection

  • csgvd: a deep learning approach combining sequence and graph embedding for source code vulnerability detection, 2023, link
  • Detecting Phishing Sites Using ChatGPT, 2023
  • diversevul: a new vulnerable source code dataset for deep learning-based vulnerability detection, 2023, link
  • low-level source code vulnerability detection using advanced BERT language model, 2022, link
  • transformer-based language models for software vulnerability detection, 2022, link
  • transformer-based vulnerability detection in code at edittime: zero-shot, few-shot, or fine-tuning?, 2023, link
  • When GPT Meets Program Analysis: Towards Intelligent Detection of Smart Contract Logic Vulnerabilities in GPTScan, 2023

Code Generation

  • A Lightweight Framework for High-Quality Code Generation, 2023
  • A Syntax-Guided Multi-Task Learning Approach for Turducken-Style Code Generation, 2023, link
  • A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark Datasets, 2023
  • AI for Low-Code for AI, 2023, link
  • Aligning Offline Metrics and Human Judgments of Value of AI-Pair Programmers, 2022, link
  • An Extensive Study on Pre-trained Models for Program Understanding and Generation, 2022, link
  • ANPL: Compiling Natural Programs with Interactive Decomposition, 2023, link
  • Capturing Failures of Large Language Models via Human Cognitive Biases, 2022, link
  • CERT: Continual Pre-training on Sketches for Library-Oriented Code Generation, 2022, link
  • ClassEval: A Manually-Crafted Benchmark for Evaluating LLMs on Class-level Code Generation, 2023
  • Code Generation Tools (Almost) for Free? A Study of Few-shot, Pre-trained Language Models on Code, 2022, link
  • CodeCompose: A Large-scale Industrial Deployment of AI-assisted Code Authoring, 2023, link
  • CodeGeex: A Pre-trained Model for Code Generation with Multilingual Evaluations on Humaneval-X, 2023, link
  • CodeIE: Large Code Generation Models Are Better Few-shot Information Extractors, 2023, link
  • CodeEval: A Benchmark of Pragmatic Code Generation with Generative Pre-trained Models, 2023, link
  • Comparing Software Developers with ChatGPT: An Empirical Investigation, 2023, link
  • Copilot for Xcode: Exploring AI-Assisted Programming by Prompting Cloud-based Large Language Models, 2023
  • Demystifying GPT Self-Repair for Code Generation, 2023
  • DS-1000: A Natural and Reliable Benchmark for Data Science Code Generation, 2022, link
  • Enabling Programming Thinking in Large Language Models Toward Code Generation, 2023
  • Evaluating AIGC Detectors on Code Content, 2023, link
  • Evaluating Large Language Models Trained on Code, 2021, link
  • Evaluating the Code Quality of AI-Assisted Code Generation Tools: An Empirical Study on GitHub Copilot, Amazon CodeWhisperer, and ChatGPT, 2023, link
  • Extending the Frontier of ChatGPT: Code Generation and Debugging, 2023
  • GRACE: Generation using Associated Code Edits, 2023, link
  • Improving ChatGPT Prompt for Code Generation, 2023, link
  • Improving Code Generation by Training with Natural Language Feedback, 2023, link
  • Interactive Code Generation via Test-Driven User-Intent Formalization, 2022, link
  • Is Model Attention Aligned with Human Attention? An Empirical Study on Large Language Models for Code Generation, 2023, link
  • Is this Snippet Written by ChatGPT? An Empirical Study with, 2023