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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
UCov: a User-Defined Coverage Criterion for Test Case Int...
Rawad Abou Assi, Fadi A. Zaraket, Wes Masri · 2014-07-11 · via cs.SE updates on arXiv.org

The goal of regression testing is to ensure that the behavior of existing code is not altered by new program changes. The primary focus of regression testing should be on code associated with: a) earlier bug fixes; and b) particular application scenarios considered to be important by the tester. Existing coverage criteria do not enable such focus, e.g., 100% branch coverage does not guarantee that a given bug fix is exercised or a given application scenario is tested. Therefore, there is a need for a complementary coverage criterion in which the user can define a test requirement characterizing a given behavior to be covered as opposed to choosing from a pool of pre-defined and generic program elements. We propose UCov, a user-defined coverage criterion wherein a test requirement is an execution pattern of program elements and predicates. Our proposed criterion is not meant to replace existing criteria, but to complement them as it focuses the testing on important code patterns that could go untested otherwise. UCov supports test case intent verification. For example, following a bug fix, the testing team may augment the regression suite with the test case that revealed the bug. However, this test case might become obsolete due to code modifications not related to the bug. But if an execution pattern characterizing the bug was defined by the user, UCov would determine that test case intent verification failed. We implemented our methodology for the Java platform and applied it onto two real life case studies. Our implementation comprises the following: 1) an Eclipse plugin allowing the user to easily specify non-trivial test requirements; 2) the ability of cross referencing test requirements across subsequent versions of a given program; and 3) the ability of checking whether user-defined test requirements were satisfied, i.e., test case intent verification.