All papers

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The paper studies how outdated code comments systematically mislead language models during automated code completion.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

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

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.

The paper evaluates twenty-one language models used as judges across three benchmarks, finding widespread discrepancies between agreement metrics and underlying validity.

The paper proposes a metamorphic testing method with mutation operators to evaluate how retrieval-augmented generation systems handle changing document corpora.

The authors introduce CharXiv, a benchmark of realistic scientific charts designed to test descriptive and reasoning capabilities in multimodal language models.

This survey provides an algorithmic overview and failure mode analysis of reinforcement learning techniques applied to language models.

This paper introduces specification templates to define operational boundaries, authority, and oversight for autonomous software agents.

This paper presents a mid-training method, corpus, and benchmark to help language models understand and reason about time series data.

The paper reviews retrieval-augmented generation paradigms and examines frameworks across retrieval, generation, and modular components.

The paper presents an 8-bit weight and activation post-training quantization technique that migrates activation outliers to weights.

The paper introduces a post-training mixed-precision quantization method using GPU-compatible 2D weight tiles for efficient inference.

The paper presents an open-source multimodal language model combining a small language backbone with vision encoders for efficient inference.

Proposes an agent memory architecture that uses biologically inspired adaptive decay functions to selectively retain and forget context.

Introduces a mixture-of-prompts distillation method to transfer knowledge from hard prompts to soft prompts, improving generalization on unseen classes.

Uses coarse CTC decoding to filter relevant hotwords into LLM prompts for improved contextual speech recognition on rare words.

The paper introduces a zero-shot LLM pipeline to generate counterfactual examples for stress-testing NLP models.