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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
Empirical Analysis of Factors and their Effect on Test Fl...
Azeem Ahmad, Ola Leifler, Kristian Sandahl · 2019-06-03 · via cs.SE updates on arXiv.org

Developers always wish to ensure that their latest changes to the code base do not break existing functionality. If test cases fail, they expect these failures to be connected to the submitted changes. Unfortunately, a flaky test can be the reason for a test failure. Developers spend time to relate possible test failures to the submitted changes only to find out that the cause for these failures is test flakiness. The dilemma of an identification of the real failures or flaky test failures affects developers' perceptions about what is test flakiness. Prior research on test flakiness has been limited to test smells and tools to detect test flakiness. In this paper, we have conducted a multiple case study with four different industries in Scandinavia to understand practitioners' perceptions about test flakiness and how this varies between industries. We observed that there are little differences in how the practitioners perceive test flakiness. We identified 23 factors that are perceived to affect test flakiness. These perceived factors are categorized as 1) Software test quality, 2) Software Quality, 3) Actual Flaky test and 4) Company-specific factors. We have studied the nature of effects such as whether factors increase, decrease or affect the ability to detect test flakiness. We validated our findings with different participants of the 4 companies to avoid biases. The average agreement rate of the identified factors and their effects are 86% and 86% respectively, among participants.