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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? 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The risk factors affecting to the software quality failures in Sri Lankan Software industry
Namadawa Bashini Jeewanthi Gamage · 2017-05-27 · via cs.SE updates on arXiv.org

Software project failure and cancellation rates increase day by day due to technical failures, quality failures, lack of end client acceptance etc. and also the lack of proper management. There are a number of reasons affected by the software project failures. According to empirical evidence, inadequate testing resources are one of the major factors that contribute to the poor quality. The main objectives of this study are to study the risk factors that affect the software quality to provide some recommendation to minimize the risk of poor quality. There are three main factors affecting to software quality namely proper testing, test planning and QA team which are directly impacted to the software quality risks. To conduct this study, I employed an open-ended questionnaire for collecting qualitative data from responses analyzed them using thematic approach method. The participants with their experiences agreed only with requirement clarity and clearly defined acceptance criteria, not with adequate unit testing and finally and also with that not doing regression testing force to quality failures. As of data analysis, not having proper formal test planning, initial test planning not being realistic, not following quality risk management, non-proper process and contingency action planning also lead to the risk of poor project quality. According to the participants added that the following factors are also behind the reasons for the lack quality of software. The experienced and skilled employees move out from the company as there is not a proper QA process and team members as they do not have the risk management mentality.