All papers

The paper introduces a benchmark framework evaluating LLM-generated quantum software unit tests against independent ground-truth checkers.

Implements a formal framework in Lean 4 to verify hybrid quantum and classical programs.

The authors propose a post-training method enabling autonomous language model agents to organize experiments and carry out machine learning engineering tasks.

Introduces an evolutionary optimization method using multi-turn reflective mutators to improve production data agents and pipelines.

Presents ObliVul, a framework combining safety obligation modeling and counterfactual validation to filter false static analysis vulnerability alerts.

Uses diffusion language models to perform multi-target few-shot infilling across shared code context guided by demonstration examples.

This paper presents a case study of an interpreter co-creating a local computer-assisted interpreting tool using large language models.

Presents a multi-agent LLM framework that automatically tunes specific internal algorithm components while preserving overall neural network structures.

The tutorial examines machine learning engineering agents from a data management view, highlighting challenges in pipeline execution, data discovery, and debugging.

This paper presents a three-stage generation and repair framework to resolve API constraints and tensor shape errors in deep learning programs.

The paper proposes a code summarization method that encodes abstract syntax tree paths using self-supervised learning to reduce training overhead.

The authors combine zero-shot test suite generation with agentic refinement to improve test coverage and mutation scores.

This case study evaluates a non-programmer using an AI development tool to build a database application in a non-English language.

The paper evaluates pre-trained code models under a standardized experimental setup to compare the impacts of their pre-training tasks fairly.

The paper examines how prompting conditions and automated agent reviews affect code complexity placement across two software architectures.

This systematic review analyzes security vulnerabilities, benchmark shortcomings, and developer oversight challenges in code generated by language models.

This paper trains language models using reinforcement learning to construct executable financial trading formulas grounded in research literature.

This paper presents a fine-tuning method to update language models for code comprehension as codebases change over time.

Presents an end-to-end distributed system orchestrating coding agents to execute computational research experiments alongside human scientists.

Evaluates whether dependency graphs improve automated mapping between source code files and system architectures.

The authors present an agent loop that closes functional test coverage in hardware design using independent simulation scoreboards.

Develops a domain harness and policy learning pipeline to improve language model agent execution in computer-aided design environments.

Evaluates static analysis security testing tools on real-world Rust vulnerabilities and presents an improved framework for vulnerability detection.

Uses large language models and dynamic feedback to generate code variants and test static application security analysis tools.

The paper examines how different repository instruction categories in agent files affect token efficiency and automated code generation quality.

This case study explores strategies for using automated coding agents to transform external software dependencies into local reimplementations.

The paper analyzes 33,000 GitHub pull requests to examine why AI coding agent contributions succeed or fail.

Proposes Agentless, an agent-free approach to software development issues that compares favorably against autonomous software engineering agents.

The paper proposes an executable routing contract that separates ranking from context deletion for coding agents.

Examines operational challenges of maintaining and deploying fleets of software variants generated by large language models.

Studies how chain-of-thought reasoning affects social bias and code quality during LLM code generation.

The paper studies how outdated code comments systematically mislead language models during automated code completion.

This paper presents a user study evaluating how programmers interact with and perceive AI code generation tools in daily workflows.

The paper introduces an interactive web-based tool for analyzing and visualizing execution trajectories of software engineering agents.

The paper cleans code smells from training datasets to evaluate and improve the quality of LLM-generated code.

The paper proposes injecting reverse-engineered architectural and domain knowledge into coding agents to prevent conceptual and structural code errors.

This paper introduces a multi-agent system that evaluates code extensibility and detects SOLID design violations through automated code extension.

This paper combines evolutionary computation with language models to optimize register-transfer level hardware code for correctness and efficiency.

The paper presents a multi-agent framework that dynamically generates execution context to enable partial code execution.

This paper compares the quality and coverage of unit test cases generated by ChatGPT against an automated generation tool.

The paper presents an oversampling method using a large language model to improve malicious JavaScript code detection.

This paper explores how consensus planning methods affect the code generation capabilities of language models.

The paper presents a multi-agent framework that improves code generation by planning programming blueprints and repairing generated code.

Proposes AutoReview, a multi-agent system combining retrieval-augmented generation and code slicing for security-focused code review.

This review surveys research on using language models for code verification and assessing the safety of model-generated code.

The paper presents a tool that uses language models and static analysis to generate valid visual flowcharts from legacy codebases.

This paper evaluates the ability of language models to generate non-functional requirements from functional software specifications.

This paper presents an empirical study evaluating open-source language models for automated unit test generation in software development.

This paper presents the design and real-world developer evaluation of an automated code generation assistant for Ansible automation scripts.

This paper presents an agentic framework to compile large multi-modal requirement documents into runnable software systems using test-driven workflows.

This paper investigates using large language models to generate structured commit messages for software security patches.

Presents the proceedings and overview of the 2nd International Workshop on Multi-Agent Systems for Automated Software Engineering.

The authors introduce a training loop where multimodal models use text and visual abilities alongside program execution to generate their own training data.

Summary pending (next check).

Summary pending (next check).

The paper introduces a method to reduce redundant external knowledge retrieval by estimating what a language model already knows.

This position paper proposes multi-agent reinforcement learning and mechanism design to reform academic peer review processes.

Summary pending (next check).

The study introduces a per-symbol static memory attribution method to analyze SRAM usage between FreeRTOS-C and Embassy-Rust on microcontrollers.

The paper presents an automated framework that revises open-source contribution guidelines by extracting and integrating evidence from code repositories.