Applied & Human-Centered · Computer Science & AI
Software Engineering
The newest papers in Software Engineering that ReadSearch has read, newest first. Each links to its own page, with its authors, date, where it was published and the original.
1,448 papers, from 31 August 2026 to 5 November 2026: 1,444 arXiv preprints and 4 OpenAlex records.
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Newest papers, page 2
QDiff: Differential Testing of Quantum Software Stacks
Over the past few years, several quantum software stacks (QSS) have been developed in response to rapid hardware advances in quantum computing.Accepted but Wrong: Silent Failures of LLM Code Repairs after API Migrations
After a runtime or API migration, an LLM-repaired procedure can run cleanly yet return wrong results, and a release check on typical test inputs ships it.DARE: Difficulty Analysis for Repair Enhancement
LLM-based coding agents are primarily evaluated by their solve rates.A Hybrid Approach for Automated Security Pattern Recovery
Security-by-design emphasises integrating security into software design and development.Can LLM Agents Learn to Test? Maintaining Scoped Experience as Testing Evidence for Mobile GUI Testing
Repeated mobile GUI tests can reuse routes, locators, and recovery strategies from earlier executions.Retrieval Supersedes Scale: Efficient Log Level Prediction via Multiplex Socio-Technical Context
Developers insert logging statements in source code to record its execution, and the resulting logs are essential for detecting and troubleshooting system anomalies.When Does a Cross-Device Run Justify PASS? Obligation-Level Evidence Qualification under Partial Observability
A cross-device test can complete every command yet judge a business claim from evidence belonging to another device or attempt.SynMOE: A Syntax-Aware MOE-Guided Approach for PEFT-Based Code Intelligence Learning
Parameter-Efficient Fine-Tuning (PEFT) has emerged as an important approach for adapting Language Models (LMs) in downstream tasks.Reasoning-Trace Overflow in Code Generation: An Empirical Study of Explicit Reasoning Models
Explicit reasoning can help language models generate code, but it also introduces a practical risk: the model may spend so much of its output budget reasoning that it never produces usable code.SWEOps-Bench: Evaluating Agents for End-to-End Resolution of Software-Induced Cloud Incidents
As autonomous agents advance, the traditional boundary between software development and cloud operations gradually blurs, increasing the need for integrated incident detection, localization, and…Growing One's AI Tools: A Paradigm of Practice-Grown Tools
Professionals have long used software built for them by others.SBVUL: Statement-Based Vulnerability Detection
Many recent approaches for vulnerability detection use large language models (LLMs) to classify functions as vulnerable or benign, leaving developers to locate the exploitable statements.CoSafe: Cooperative Runtime Safety Assurance for Mixed-Traffic Autonomous Driving
With the rapid development of autonomous driving systems (ADSes), real-world roads are evolving into mixed-traffic environments where autonomous vehicles (AVs) and other traffic participants (e.g…Bug2Scenario: Root-Cause-Preserving Test Scenario Amplification for Autonomous Driving Software
Simulation-based fuzzing exposes autonomous driving system (ADS) failures by repeatedly mutating and executing scenarios, but converting one failure into reusable regression scenarios remains…Analysis of Local Open-Weight LLM Agents on Vulnerability-Reproduction Tasks
Open-weight language models that fit on a single consumer GPU are becoming capable on a growing range of tasks, and they matter because they can run locally without sending the code to a hosted…CLEAR: Structured Specifications for Controllable Long-Horizon ML Research Engineering
Large language model (LLM) agents are increasingly being used to automate machine learning (ML) research workflows, ranging from code development and experiment execution to research reproduction…A.S.E 2.0: Understanding How Repository Context Impacts the Security of LLM-Generated Code
Large language model (LLM) coding assistants and agents construct a repository context before generating code.EvoCodeAgent: Project-Level Code Generation via Architecture-Code Co-Evolution and Experience-Guided Repair
Project-level code generation aims to automatically synthesize complete software repositories from high-level requirements, producing coherent multi-file systems with well-structured modules and…Keeping Fuzzers on Duty: Repairing Fuzzing Build Breakages in Continuous Open-Source Fuzzing
Continuous fuzzing platforms such as OSS-Fuzz provide sustained security testing for open-source projects, but long-term build breakages reduce security coverage and waste infrastructure resources.Reassessing Dataset Quality and Fine-Tuning Practices for Vulnerability Detection on PrimeVul dataset
Recent studies report promising results from supervised fine-tuning of pre-trained code language models for automated vulnerability detection on benchmarks such as PrimeVul.