Unit testing is an essential but resource-intensive step in software development, ensuring individual code units function correctly. This paper introduces AgoneTest, an automated evaluation framework for Large Language Model-generated (LLM) unit tests in Java. AgoneTest does not aim to propose a novel test generation algorithm; rather, it supports researchers and developers in comparing different LLMs and prompting strategies through a standardized end-to-end evaluation pipeline under realistic conditions. We introduce the CLASSES2TEST dataset, which maps Java classes under test to their corresponding test classes, and a framework that integrates advanced evaluation metrics, such as mutation score and test smells, for a comprehensive assessment. Experimental results show that, for the subset of tests that compile, LLM-generated tests can match or exceed human-written tests in terms of coverage and defect detection. Our findings also demonstrate that enhanced prompting strategies contribute to test quality. AgoneTest clarifies the potential of LLMs in software testing and offers insights for future improvements in model design, prompt engineering, and testing practices.

Maintaining a consistent brand identity across a global network of retail stores while adhering to local constraints has long challenged Visual Merchandisers.Legacy processes, often reliant on subjective "by-eye" adjustments, can drive up operational costs and lead to inconsistent in-store execution.We formalize a user modeling framework implementing a multi-criteria utility function that balances brand identity and operational overhead.We integrated our framework in a 3D virtual tour design platform, deploying it in the ecosystem of OVS, a global fashion firm.Through a preliminary user study, we showcase that our solution enables lower iteration cycles and decreases store-to-store discrepancies.

The rapid digitalization of the fashion industry has transformed marketing strategies, emphasizing the need for personalized and adaptive advertising content.This paper presents a case study on fine-tuning Large Language Models (LLMs) for fashion advertising, focusing on OVS, a major Italian fashion retailer.By leveraging real-world marketing data from OVS's newsletters and social media campaigns, we developed a fine-tuned model capable of generating engaging and stylistically coherent promotional content.To evaluate the effectiveness of this approach, we introduced a novel brand compliance index, measuring the alignment of AI-generated text with key branding requirements, such as audience targeting, event specificity, and platform appropriateness.Experimental results show that the fine-tuned model achieved a compliance score of 0.82, significantly outperforming the baseline model (0.63).Although this approach introduces a minor increase in generation latency, the enhanced alignment with brand identity justifies its use in marketing automation.Our findings highlight the potential of fine-tuned LLMs to streamline advertising content generation while maintaining brand consistency, offering valuable insights for the future of AI-driven digital marketing.

Software correctness is crucial, with unit testing playing an indispensable role in the software development lifecycle. However, creating unit tests is time-consuming and costly, underlining the need for automation. Leveraging Large Language Models (LLMs) for unit test generation is a promising solution, but existing studies focus on simple, small-scale scenarios, leaving a gap in understanding LLMs' performance in real-world applications, particularly regarding integration and assessment efficacy at scale. Here, we present AgoneTest, a system focused on automatically generating and evaluating complex class-level test suites. Our contributions include a scalable automated system, a newly developed dataset for rigorous evaluation, and a detailed methodology for test quality assessment.