Fashion plays a significant role in cultures around the world and in the global economy. Similarly to what has happened with other consumer products, the Internet has trans-formed the way people shop for fashion, transitioning from an in-store experience to an increasingly online one thanks to ever-evolving e-commerce. While much of today’s e-commerce experience for fashion still relies on a keyword-search-based approach for web and mobile catalogs, the advent of Generative Artificial Intelligence (GenAI) and multimodal Large Language Models (LLMs) has brought unprecedented opportunities for searching, exploring, and recommending fashion products online. This paper aims to leverage state-of-the-art GenAI and multimodal LLM technologies to provide a different customer experience based on conversational, intent-driven exploration of fashion catalogs and outfit recommendations through our proposed solution, VestiME. The results of our user study involving fashion experts show the potential of these highly interactive, conversational, and multimodal e-commerce experiences for fashion.

The huge amount of information regarding software vulnerabilities, the multiple and heterogeneous information sources, and the lack of awareness about the dangers of software vulnerabilities, exacerbates the risks of security threats being materialized. In this complex context, this paper approaches the problem of managing early alerts for software vulnerablities by leveraging existing vulnerability information found in vulnerability repositories and social networks. To this end, we propose a solution based on techniques that stem from automated retrieval of information about vulneratilities from the above sources, userdefined preferences regarding their technological environment and intelligent vulnerability tagging. Our user studies reveal the feasibility of our approach as a tool for managing early alerts regarding software vulnerabilities and keeping security professionals aware of them.

Application Programming Interfaces (APIs) have become one of the key assets within modern businesses, facilitating the linking and integration of intra- and inter-organizational data and systems in the context of complex and heterogeneous technology ecosystems. APIs allow organizations to monetize data, build profitable partnerships and foster innovation and growth. Understanding APIs and their usage are therefore key to building solutions for enabling successful business operations. This paper aims at understanding API topic issues posted on Stack Overflow (SO), a Community Question Answering (CQA) site for programmers. We conduct an empirical analysis on a sample of 400 randomly-selected Q&As threads to help identify API-related issues and their main topics. A thematic analysis performed on this sample reveals eight main topics related to APIs, among which API usage, debugging, API constraints and API security emerged as the major ones. We also exemplify the types of support provided by SO community in addressing each of the identified topics and discuss possible venues on how to further leverage this knowledge.

Application Programming Interface (API) is a core technology that facilitates developers' productivity by enabling the reuse of software components. Understanding APIs and gaining knowledge about their usage are therefore fundamental needs for developers that impact a wide range of software development activities. This paper presents an approach to enable API users to explore, discover and learn about APIs through API topic issues discussed in Stack Overflow (SO), a widely used programming, community question-answering (CQA) site. Our work proposes an integrated API Knowledge Base (KB) and indexing technique that combines both SO API-related posts as well as other API learning resources collected from the Web (e.g., API video-tutorials from Youtube). The resulting indexed and enriched API community knowledge can be queried in a API-topic-issue driven manner using a simple yet powerful domain-specific language (DSL). We demonstrate the feasibility of our approach through Scout-bot, our tool for exploration and discovery of API topic issues.