Predicting whether a newly submitted bug will be resolved quickly or slowly is a crucial aspect of the bug triage process, as it enables project managers to estimate software maintenance efforts and manage development workflows more effectively. This paper proposes a deep learning approach for classifying bug reports into two categories— FAST or SLOW —based on their expected fixing time. The method leverages a feature set composed of the bug description and reporter comments and adopts a transfer learning strategy using pre-trained Large Language Models (LLMs). The problem is framed as a supervised text classification task, where LLMs exploit their ability to learn rich contextual representations of language. We introduce a novel classification workflow that guides the LLM through a structured prompt, combining two design patterns: the persona pattern to contextualize the task and the input semantic pattern to organize textual information. The workflow relies on zero-shot learning to assess whether the intrinsic knowledge embedded in the LLMs is sufficient for this prediction task. We conducted a comprehensive evaluation of three state-of-the-art LLMs across multiple real-world datasets sourced from Bugzilla, encompassing a diverse range of software projects. The experimental results demonstrate that the proposed method is effective in accurately identifying fast-resolving bugs. Among the evaluated models, LLaMA3-8B consistently delivered superior performance. Additionally, the absence of statistically significant performance variations across datasets highlights the generalizability of the approach. Notably, the LLMs maintained strong performance even on small and imbalanced datasets, underscoring their robustness and practical applicability in real-world, data-scarce scenarios.

Concept maps are significant tools able to support several tasks in the educational area such as curriculum design, knowledge organization and modeling, students’ assessment and many others. They are also successfully used in learning activities in which students have to represent domain knowledge according to teacher’s assignment. In this context, the development of Learning Analytics approaches would benefit of methods that automatically compare concept maps. Detecting concept maps similarities is relevant to identify how the same concepts are used in different knowledge representations. Algorithms for comparing graphs have been extensively studied in the literature, but they do not appear appropriate for concept maps. In concept maps, concepts exposed are at least as relevant as the structure that contains them. Neglecting the semantic and didactic aspect inevitably causes inaccuracies and the consequently limited applicability in Learning Analytics approaches. In this work, starting from an algorithm which compares didactic characteristic of concept maps, we present an extension which exploits a semantic approach to catch the actual meaning of the concepts expressed in the nodes of the map.

In this paper, we tackle the problem of the irony and sarcasm detection for the Italian language to contribute to the enrichment of the sentiment analysis field. We analyze and compare five deep-learning systems. Results show the high suitability of such systems to face the problem by achieving 93% of F1-Score in the best case. Furthermore, we briefly analyze the model architectures in order to choose the best compromise between performances and complexity.