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cs.SE updates on arXiv.org

VLA Foundry: A Unified Framework for Training Vision-Language-Action Models Evaluating LLM-Generated Obfuscated XSS Payloads for Machine Learning-Based Detection Do Agents Dream of Root Shells? Partial-Credit Evaluation of LLM Agents in Capture the Flag Challenges Refute-or-Promote: An Adversarial Stage-Gated Multi-Agent Review Methodology for High-Precision LLM-Assisted Defect Discovery From Particles to Perils: SVGD-Based Hazardous Scenario Generation for Autonomous Driving Systems Testing Choose Your Own Adventure: Non-Linear AI-Assisted Programming with EvoGraph Human-Machine Co-Boosted Bug Report Identification with Mutualistic Neural Active Learning LLMSniffer: Detecting LLM-Generated Code via GraphCodeBERT and Supervised Contrastive Learning Neurosymbolic Repo-level Code Localization CodeMMR: Bridging Natural Language, Code, and Image for Unified Retrieval Symbolic Guardrails for Domain-Specific Agents: Stronger Safety and Security Guarantees Without Sacrificing Utility Verification Modulo Tested Library Contracts The Semi-Executable Stack: Agentic Software Engineering and the Expanding Scope of SE Scaling Test-Time Compute for Agentic Coding AI-Assisted Requirements Engineering: An Empirical Evaluation Relative to Expert Judgment From Procedural Skills to Strategy Genes: Towards Experience-Driven Test-Time Evolution Atropos: Improving Cost-Benefit Trade-off of LLM-based Agents under Self-Consistency with Early Termination and Model Hotswap Vibe-Coding: Feedback-Based Automated Verification with no Human Code Inspection, a Feasibility Study Benchmarks for Trajectory Safety Evaluation and Diagnosis in OpenClaw and Codex: ATBench-Claw and ATBench-Codex Bounded Autonomy for Enterprise AI: Typed Action Contracts and Consumer-Side Execution AIPC: Agent-Based Automation for AI Model Deployment with Qualcomm AI Runtime Analyzing Chain of Thought (CoT) Approaches in Control Flow Code Deobfuscation Tasks Asking What Matters: Reward-Driven Clarification for Software Engineering Tasks Prompt-Driven Code Summarization: A Systematic Literature Review LinuxArena: A Control Setting for AI Agents in Live Production Software Environments LLMs taking shortcuts in test generation: A study with SAP HANA and LevelDB Large Language Models to Enhance Business Process Modeling: Past, Present, and Future Trends CollabCoder: Plan-Code Co-Evolution via Collaborative Decision-Making for Efficient Code Generation Sentiment analysis for software engineering: How far can zero-shot learning (ZSL) go? Learning from Change: Predictive Models for Incident Prevention in a Regulated IT Environment
Unifying Classification Schemes for Software Engineering ...
Angelika Kaplan, Thomas Kühn, Ralf Reussner · 2022-09-22 · via cs.SE updates on arXiv.org

Background: Classifications in meta-research enable researchers to cope with an increasing body of scientific knowledge. They provide a framework for, e.g., distinguishing methods, reports, reproducibility, and evaluation in a knowledge field as well as a common terminology. Both eases sharing, understanding and evolution of knowledge. In software engineering (SE), there are several classifications that describe the nature of SE research. Regarding the consolidation of the large body of classified knowledge in SE research, a generally applicable classification scheme is crucial. Moreover, the commonalities and differences among different classification schemes have rarely been studied. Due to the fact that classifications are documented textual, it is hard to catalog, reuse, and compare them. To the best of our knowledge, there is no research work so far that addresses documentation and systematic investigation of classifications in SE meta-research. Objective: We aim to construct a unified, generally applicable classification scheme for SE meta-research by collecting and documenting existing classification schemes and unifying their classes and categories. Method: Our execution plan is divided into three phases: construction, validation, and evaluation phase. For the construction phase, we perform a literature review to identify, collect, and analyze a set of established SE research classifications. In the validation phase, we analyze individual categories and classes of included papers. We use quantitative metrics from literature to conduct and assess the unification process to build a generally applicable classification scheme for SE research. Lastly, we investigate the applicability of the unified scheme. Therefore, we perform a workshop session followed by user studies w.r.t. investigations about reliability, correctness, and ease of use.