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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
ToffA-DSPL: an approach of trade-off analysis for designi...
Michelle Larissa Luciano Carvalho, Paulo Cesar Masiero, Ismayle · 2024-07-02 · via cs.SE updates on arXiv.org

Software engineers have adopted the Dynamic Software Product Lines (DSPL) engineering practices to develop Dynamically Adaptable Software (DAS). DAS is seen as a DSPL application and must cope with a large number of configurations of features, Non-functional Requirements (NFRs), and contexts. However, the accurate representation of the impact of features over NFRs and contexts for the identification of optimal configurations is not a trivial task. Software engineers need to have domain knowledge and design DAS before deploying to satisfy those requirements. Aiming to handle them, we proposed an approach of Trade-off Analysis for DSPL at design-time, named ToffA-DSPL. It deals with the configuration selection process considering interactions between NFRs and contexts. We performed an exploratory study based on simulations to identify the usefulness of the ToffA-DSPL approach. In general, the configurations suggested by ToffA-DSPL provide high satisfaction levels of NFRs. Based on simulations, we evidenced that our approach aims to explore reuse and is useful for generating valid and optimal configurations. In addition, ToffA-DSPL enables software engineers to conduct trade-off analysis, evaluate changes in the context feature, and define an adaptation model from optimal configurations found in the analysis.