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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
Gamifying Testing in IntelliJ: A Replicability Study
Philipp Straubinger, Tommaso Fulcini, Giacomo Garaccione, Luca A · 2025-04-28 · via cs.SE updates on arXiv.org

Gamification is an emerging technique to enhance motivation and performance in traditionally unengaging tasks like software testing. Previous studies have indicated that gamified systems have the potential to improve software testing processes by providing testers with achievements and feedback. However, further evidence of these benefits across different environments, programming languages, and participant groups is required. This paper aims to replicate and validate the effects of IntelliGame, a gamification plugin for IntelliJ IDEA to engage developers in writing and executing tests. The objective is to generalize the benefits observed in earlier studies to new contexts, i.e., the TypeScript programming language and a larger participant pool. The replicability study consists of a controlled experiment with 174 participants, divided into two groups: one using IntelliGame and one with no gamification plugin. The study employed a two-group experimental design to compare testing behavior, coverage, mutation scores, and participant feedback between the groups. Data was collected through test metrics and participant surveys, and statistical analysis was performed to determine the statistical significance. Participants using IntelliGame showed higher engagement and productivity in testing practices than the control group, evidenced by the creation of more tests, increased frequency of executions, and enhanced utilization of testing tools. This ultimately led to better code implementations, highlighting the effectiveness of gamification in improving functional outcomes and motivating users in their testing endeavors. The replication study confirms that gamification, through IntelliGame, positively impacts software testing behavior and developer engagement in coding tasks.