Silverback AI Chatbot Announces Development of AI Assistant Feature to Support Automated Digital Interaction and Workflow Management - Enterprise News
<a href="https://news.google.com/rss/articles/CBMimgJBVV95cUxPbDFfYnNQcHlNU0ZLWHhCM2hfeHNuV29jdVBiQjZWNFRYVzRuWjhfbGp1cm9hUHY2T3pIQ1lvMjJ2c0F1QmZ3dzBfMTRCeXo0dGlmdmNwRHVvS1pCWEtDa2tLQ29JRmNqMmYyMGtCWnJaMHp3dkNXa0FVMGpzekN4aDRtMVVJak41djNCUlRPUXZpZGVmbGtHbXZEZzRwUlRxcVZ5V3N1VnREaVNxQXlaMHVTbXFnTV9ZWmxZck1uX1pLalMwNVVJQUt0SHFwVE9BV1I2aGNTY2JNVzdrZmFLRmlUQUw0V3FDd3BpVUhoWWwzbFBRbkhFOF9sOEU1VjRoeUhvZkRvM1lLbFlUc1libkxJT0JqZ0NtaHc?oc=5" target="_blank">Silverback AI Chatbot Announces Development of AI Assistant Feature to Support Automated Digital Interaction and Workflow Management</a> <font color="#6f6f6f">Enterprise News</font>
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An Empirical Study of Testing Practices in Open Source AI Agent Frameworks and Agentic Applications
arXiv:2509.19185v3 Announce Type: replace Abstract: Foundation model (FM)-based AI agents are rapidly gaining adoption across diverse domains, but their inherent non-determinism and non-reproducibility pose testing and quality assurance challenges. While recent benchmarks provide task-level evaluations, there is limited understanding of how developers verify the internal correctness of these agents during development. To address this gap, we conduct the first large-scale empirical study of testing practices in the AI agent ecosystem, analyzing 39 open-source agent frameworks and 439 agentic applications. We identify ten distinct testing patterns and find that novel, agent-specific methods like DeepEval are seldom used (around 1%), while traditional patterns like negative and membership tes
A Multi-Language Perspective on the Robustness of LLM Code Generation
arXiv:2504.19108v5 Announce Type: replace Abstract: Large language models have gained significant traction and popularity in recent times, extending their usage to code-generation tasks. While this field has garnered considerable attention, the exploration of testing and evaluating the robustness of code generation models remains an ongoing endeavor. Previous studies have primarily focused on code generation models specifically for the Python language, overlooking other widely used programming languages. In this work, we conduct a comprehensive comparative analysis to assess the robustness performance of several prominent code generation models and investigate whether robustness can be improved by repairing perturbed docstrings using an LLM. Furthermore, we investigate how their performanc
Precision or Peril: A PoC of Python Code Quality from Quantized Large Language Models
arXiv:2411.10656v2 Announce Type: replace Abstract: Context: Large Language Models (LLMs) like GPT-5 and LLaMA-405b exhibit advanced code generation abilities, but their deployment demands substantial computation resources and energy. Quantization can reduce memory footprint and hardware requirements, yet may degrade code quality. Objective: This study investigates code generation performance of smaller LLMs, examines the effect of quantization, and identifies common code quality issues as a proof of concepts (PoC). Method: Four open-source LLMs are evaluated on Python benchmarks using code similarity metrics, with an analysis on 8-bit and 4-bit quantization, alongside static code quality assessment. Results: While smaller LLMs can generate functional code, benchmark performance is limited
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