Papers with software development
NLP+Code: Code Intelligence in Language Models (2025.emnlp-tutorials)
Copied to clipboard
| Challenge: | Language models have shown impressive abilities in a range of natural language processing tasks. |
| Approach: | This tutorial will provide an overview of the latest advances in natural language processing . it will provide preliminaries of training foundation models on code and their common practices . |
| Outcome: | This tutorial aims to provide an overview of recent advances in code modeling . it provides preliminaries of training foundation models on code and their common practices . |
CodeFlowBench: A Multi-turn, Iterative Benchmark for Complex Code Generation (2026.acl-long)
Copied to clipboard
| Challenge: | Modern software development demands code that is maintainable, testable, and scalable by organizing the implementation into modular components with iterative reuse of existing codes. |
| Approach: | They propose a benchmark to evaluate LLMs' ability to perform codeflow by reusing existing functions over multiple turns. |
| Outcome: | The proposed benchmarks show that LLMs perform significantly worse in multi-turn codeflow scenarios and that their performance inversely correlates with dependency complexity. |
Exploring Dynamic Selection of Branch Expansion Orders for Code Generation (2021.acl-long)
Copied to clipboard
| Challenge: | Existing code generation models model abstract syntax tree (AST) but not suitable for all multi-branch nodes. |
| Approach: | They propose to equip a Seq2Tree model with a branch selector to determine optimal expansion orders for multi-branch nodes. |
| Outcome: | The proposed model can determine optimal expansion orders of branches for multi-branch nodes. |
Towards Low-Resource Automatic Program Repair with Meta-Learning and Pretrained Language Models (2023.emnlp-main)
Copied to clipboard
| Challenge: | Recent advances in deep learning (DL) based APR models have demonstrated promising results by learning from large-scale bug-fix examples in a data-driven manner. |
| Approach: | They propose a meta-learning framework integrated with code pretrained language models to generate fixes for low-resource bugs with limited training samples. |
| Outcome: | The proposed framework learns better error-specific knowledge from high-resource bugs through efficient first-order meta-learning optimization, which allows for a faster adaptation to the target low-resourced bugs. |
DCE-LLM: Dead Code Elimination with Large Language Models (2025.naacl-long)
Copied to clipboard
| Challenge: | Dead code can obscure logical errors and be exploited for obfuscation in malware. |
| Approach: | They propose a framework for automated dead code elimination using a codeBERT model with an attribution-based line selector. |
| Outcome: | Experimental results show that DCE-LLM outperforms existing tools for dead code elimination . dead code can obscure logical errors and be exploited for obfuscation in malware . |
Revisiting the Impact of Pursuing Modularity for Code Generation (2024.findings-emnlp)
Copied to clipboard
| Challenge: | a recent study examines the impact of modularity on code generation in large language models . modularity is not a core factor for improving performance of code generation models, argues a new study . |
| Approach: | They introduce a new metric to measure the impact of modularity in code generation . they find modularity is not a core factor for improving performance of LLMs . |
| Outcome: | The proposed metric shows that modularity is not a core factor for improving performance . coding assistants are becoming increasingly essential for programmers . |
BLOCSUM: Block Scope-based Source Code Summarization via Shared Block Representation (2023.findings-acl)
Copied to clipboard
| Challenge: | Abstract Syntax Tree (AST) and sequence of code tokens are useful for code summarization. |
| Approach: | They propose a shared block position embedding to represent various code blocks . they also develop variant ASTs to learn rich information such as block and global dependencies . |
| Outcome: | The proposed method improves on two real-world datasets, including ablation studies and a human evaluation. |
Jointly Learning to Repair Code and Generate Commit Message (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing work performs code repair and commit message generation independently. |
| Approach: | They propose a cascaded method to repair program codes and generate commit messages in a unified framework. |
| Outcome: | The proposed model significantly outperforms baselines on a buggy-fixed-commit dataset. |
HintPilot: LLM-based Compiler Hint Synthesis for Code Optimization (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing methods to optimize source code rely on invasive transformations that can introduce semantic errors and miss fine-grained compiler-level optimization opportunities. |
| Approach: | They propose a method that bridges LLM-based reasoning with traditional compilers by synthesizing compiler hints. |
| Outcome: | HintPilot achieves 6.88x speedup over -Ofast while preserving program correctness. |