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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
Inclusive Employment Pathways: Career Success Factors for...
Orvila Sarker, Mona Jamshaid, M. Ali Babar · 2025-08-13 · via cs.SE updates on arXiv.org

Research has highlighted the valuable contributions of autistic individuals in the Information and Communication Technology (ICT) sector, particularly in areas such as software development, testing, and cybersecurity. Their strengths in information processing, attention to detail, innovative thinking, and commitment to high-quality outcomes in the ICT domain are well-documented. However, despite their potential, autistic individuals often face barriers in Software Engineering (SE) roles due to a lack of personalised tools, complex work environments, non-inclusive recruitment practices, limited co-worker support, challenging social dynamics and so on. Motivated by the ethical framework of the neurodiversity movement and the success of pioneering initiatives like the Dandelion program, corporate Diversity, Equity, and Inclusion (DEI) in the ICT sector has increasingly focused on autistic talent. This movement fundamentally reframes challenges not as individual deficits but as failures of environments designed for a neurotypical majority. Despite this progress, there is no synthesis of knowledge reporting the full pathway from software engineering education through to sustainable workplace inclusion. To address this, we conducted a Systematic Review of 30 studies and identified 18 success factors grouped into four thematic categories: (1) Software Engineering Education, (2) Career and Employment Training, (3) Work Environment, and (4) Tools and Assistive Technologies. Our findings offer evidence-based recommendations for educational institutions, employers, organisations, and tool developers to enhance the inclusion of autistic individuals in SE. These include strategies for inclusive meeting and collaboration practices, accessible and structured work environments, clear role and responsibility definitions, and the provision of tailored workplace accommodations.