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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
Another Systematic Review? A Critical Analysis of Systema...
Henry Edison, Nauman Ali · 2026-01-28 · via cs.SE updates on arXiv.org

Background: Systematic literature reviews (SLRs) have become prevalent in software engineering research. Several researchers may conduct SLRs on similar topics without a prospective register for SLR protocols. However, even ignoring these unavoidable duplications of effort in the simultaneous conduct of SLRs, the proliferation of overlapping and often repetitive SLRs indicates that researchers are not extensively checking for existing SLRs on a topic. Given how effort-intensive it is to design, conduct, and report an SLR, the situation is less than ideal for software engineering research. Aim: To understand how authors justify additional SLRs on a topic. Method: To illustrate the issue and develop suggestions for improvement to address this issue, we have intentionally picked a sufficiently narrow but well-researched topic, i.e., effort estimation in Agile software development. We identify common justification patterns through a qualitative content analysis of 18 published SLRs. We further consider the citation data, publication years, publication venues, and the quality of the SLRs when interpreting the results. Results: The common justification patterns include authors claiming gaps in coverage, methodological limitations in prior studies, temporal obsolescence of previous SLRs, or rapid technological and methodological advancements necessitating updated syntheses. Conclusion: Our in-depth analysis of SLRs on a fairly narrow topic provides insights into SLRs in software engineering in general. By emphasizing the need for identifying existing SLRs and for justifying the undertaking of further SLRs, both in design and review guidelines and as a policy of conferences and journals, we can reduce the likelihood of duplication of effort and increase the rate of progress in the field.