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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
User Interface Design Smell: Automatic Detection and Refa...
Arnaud Blouin, Valéria Lelli, Benoit Baudry, Fabien Coulon · 2017-03-31 · via cs.SE updates on arXiv.org

User Interfaces (UIs) intensively rely on event-driven programming: widgets send UI events, which capture users' interactions, to dedicated objects called controllers. Controllers use several UI listeners that handle these events to produce UI commands. First, we reveal the presence of design smells in the code that describes and controls UIs. Second, we demonstrate that specific code analyses are necessary to analyze and refactor UI code, because of its coupling with the rest of the code. We conducted an empirical study on four large Java Swing and SWT open-source software systems. We study to what extent the number of UI commands that a UI listener can produce has an impact on the change- and fault-proneness of the UI listener code. We develop a static code analysis for detecting UI commands in the code. We identify a new type of design smell, called Blob Listener that characterizes UI listeners that can produce more than two UI commands. We propose a systematic static code analysis procedure that searches for Blob Listeners that we implement in InspectorGuidget. We conducted experiments on the four software systems for which we manually identified 53 instances of Blob Listener. InspectorGuidget successfully detected 52 Blob Listeners out of 53. The results exhibit a precision of 81.25% and a recall of 98.11%. We then developed a semi-automatically and behavior-preserving refactoring process to remove Blob Listeners. 49.06% of the 53 Blob Listeners were automatically refactored. Patches for JabRef, and FreeCol have been accepted and merged. Discussions with developers of the four software systems assess the relevance of the Blob Listener. This work shows that UI code also suffers from design smells that have to be identified and characterized. We argue that studies have to be conducted to find other UI design smells and tools that analyze UI code must be developed.