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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
Product Backlog Rating: A Case Study On Measuring Test Qu...
Imrul Kayes, Mithun Sarker, Jacob Chakareski · 2013-10-10 · via cs.SE updates on arXiv.org

Agile software development methodologies focus on software projects which are behind schedule or highly likely to have a problematic development phase. In the last decade, Agile methods have transformed from cult techniques to mainstream methodologies. Scrum, an Agile software development method, has been widely adopted due to its adaptive nature. This paper presents a metric that measures the quality of the testing process in a Scrum process. As product quality and process quality correlate, improved test quality can ensure high quality products. Also, gaining experience from eight years of successful Scrum implementation at SoftwarePeople, we describe the Scrum process emphasizing the testing process. We propose a metric Product Backlog Rating (PBR) to assess the testing process in Scrum. PBR considers the complexity of the features to be developed in an iteration of Scrum, assesses test ratings and offers a numerical score of the testing process. This metric is able to provide a comprehensive overview of the testing process over the development cycle of a product. We present a case study which shows how the metric is used at SoftwarePeople. The case study explains some features that have been developed in a Sprint in terms of feature complexity and potential test assessment difficulties and shows how PBR is calculated during the Sprint. We propose a test process assessment metric that provides insights into the Scrum testing process. However, the metric needs further evaluation considering associated resources (e.g., quality assurance engineers, the length of the Scrum cycle).