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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
On Improving Research Methodology Course at Blekinge Inst...
Shoaib Bakhtyar, Ahmad Nauman Ghazi · 2017-04-22 · via cs.SE updates on arXiv.org

The Research Methodology in Software Engineering and Computer Science (RM) is a compulsory course that must be studied by graduate students at Blekinge Institute of Technology (BTH) prior to undertaking their theses work. The course is focused on teaching research methods and techniques for data collection and analysis in the fields of Computer Science and Software Engineering. It is intended that the course should help students in practically applying appropriate research methods in different courses (in addition to the RM course) including their Master's theses. However, it is believed that there exist deficiencies in the course due to which the course implementation (learning and assessment activities) as well as the performance of different participants (students, teachers, and evaluators) are affected negatively. In this article our aim is to investigate potential deficiencies in the RM course at BTH in order to provide a concrete evidence on the deficiencies faced by students, evaluators, and teachers in the course. Additionally, we suggest recommendations for resolving the identified deficiencies. Our findings gathered through semi-structured interviews with students, teachers, and evaluators in the course are presented in this article. By identifying a total of twenty one deficiencies from different perspectives, we found that there exist critical deficiencies at different levels within the course. Furthermore, in order to overcome the identified deficiencies, we suggest seven recommendations that may be implemented at different levels within the course and the study program. Our suggested recommendations, if implemented, will help in resolving deficiencies in the course, which may lead to achieving an improved teaching and learning in the RM course at BTH.