1,721,082 research outputs found
LOD.CS.UNIPA Project: an experience of LOD at the University of Palermo
No full textThis paper describes the LOD.CS.UNI.PA Project and its main
goal, the transformation process of data already available on the
web site of the Computer Science curricula web site at the
University of Palermo into data ready to be connected to the LOD.
Since 1997 information about bachelor and master degrees in
Computer Science at the University of Palermo has been
published on the web, and provides a reference point for students,
teachers and researchers who have easy access to the information
they require. However, the users of the web are now changing;
data cannot be published only for human comprehension but
intelligent devices also need access to web data and above all they
need to understand them. In 2006 Tim Berners Lee presented a
star rating system for the data available on the web. Following his
five star classification, the aim of the work described in this paper
is to raise the level of data concerning degrees in Computer
Science at Palermo University, from one star (data available on
the web in whatever format), to five stars (data connected to other
LOD datasets). So far the data has been transformed to a four star
level in which each item has a URL that can be dereferenced, but
the final objective of this project is to reach the five star level
Retrieval of Educational Resources from the Web: A Comparison Between Google and Online Educational Repositories
No full textThe retrieval and composition of educational material are topics that attract many studies from the field of Information Retrieval and Artificial Intelligence. The Web is gradually gaining popularity among teachers and students as a source of learning resources. This transition is, however, facing skepticism from some scholars in the field of education. The main concern is about the quality and reliability of the teaching on the Web. While online educational repositories are explicitly built for educational purposes by competent teachers, web pages are designed and created for offering different services, not only education. In this study, we analyse if the Internet is a good source of teaching material compared to the currently available repositories in education. Using a collection of 50 queries related to educational topics, we compare how many useful learning resources a teacher can retrieve in Google and three popular learning object repositories. The results are very insightful and in favour of Google supported by the t-tests. For most of the queries, Google retrieves a larger number of useful web pages than the repositories p <.01, and no queries resulted in zero useful items. Instead, the repositories struggle to find even one relevant material for many queries. This study is clear evidence that even though the repositories offer a richer description of the learning resources through metadata, it is time to undertake more research towards the retrieval of web pages for educational applications
Enriching Didactic Similarity Measures of Concept Maps by a Deep Learning Based Approach
No full textConcept 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
A Dynamical Quality Model to Continuously Monitor Software Maintenance
No full textContext: several companies, particularly Small and Medium Sized Enterprises (SMEs), often face software maintenance issues due to the lack of Software Quality Assurance (SQA). SQA is a complex task that requires a lot of effort and expertise, often not available in SMEs. Several SQA models, including maintenance prediction models, have been defined in research papers. However, these models are commonly defined as "one-size-fits-all" and are mainly targeted at the big industry, which can afford software quality experts who undertake the data interpretation tasks. Objective: in this work, we propose an approach to continuously monitor the software operated by end users, automatically collecting issues and recommending possible fixes to developers. The continuous exception monitoring system will also serve as knowledge base to suggest a set of quality practices to avoid (re) introducing bugs into the code. Method: first, we identify a set of SQA practices applicable to SMEs, based on the main constraints of these. Then, we identify a set of prediction techniques, including regressions and machine learning, keeping track of bugs and exceptions raised by the released software. Finally, we provide each company with a tailored SQA model, automatically obtained from companies' bug/issue history. Developers are then provided with the quality models through a set of plug-ins for integrated development environments. These suggest a set of SQA actions that should be undertaken, in order to maintain a certain quality level and allowing to remove the most severe issues with the lowest possible effort. Conclusion: The collected measures will be made available as public dataset, so that researchers can also benefit of the project's results. This work is developed in collaboration with local SMEs and existing Open Source projects and communities
Intelligent Knowledge Understanding from Students Questionnaires: A Case Study
No full textLearning Analytics techniques are widely used to improve students’ performance. Data collected from students’ assessments are helpful to predict their success and questionnaires are extensively adopted to assess students’ knowledge. Several mathematical models studying the correlation between students’ hidden skills and their performance to questionnaires’ items have been introduced. Among them, Non-negative matrix factorizations (NMFs) have been proven to be effective in automatically extracting hidden skills, a time-consuming activity that is usually tackled manually prone to subjective interpretations. In this paper, we present an intelligent data analysis approach based upon NMF. Data are collected from a competition, namely MathsChallenge, performed by the University of Foggia. In 2021 the competition has been held, for the first time, online due to the Covid-19 pandemic
Operationalizing the experience factory for effort estimation in agile processes
No full text[Background] The effort required to systematically collect historical data is not always allocable in agile processes and historical data management is usually delegated to the developers' experience, who need to remember previous project details. However, even if well trained, developers cannot precisely remember a huge number of details, resulting in wrong decisions being made during the development process. [Aims] The goal of this paper is to operationalize the Experience Factory in an agile way, i.e., defining a strategy for collecting historical project data using an agile approach. [Method] We provide a mechanism for understanding whether a measure must be collected or not, based on the Return on Invested Time (ROIT). In order to validate this approach, we instantiated the factory with an exploratory case study, comparing four projects that did not use our approach with one project that used it after 12 weeks out of 37 and two projects that used it from the beginning. [Results] The proposed approach helps developers to constantly improve their estimation accuracy with a very positive ROIT of the collected measure. [Conclusions] From this first experience, we can conclude that the Experience Factory can be applied effectively to agile processes, supporting developers in improving their performance and reducing potential decision mistakes
A novel LLM-based classifier for predicting bug-fixing time in Bug Tracking Systems
No full textPredicting 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 realworld 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
Experimenting Traditional and Modern Reliability Models in a 3-Years European Software Project
No full textReliability is a very important non-functional aspect for software systems and artefacts. In literature, several definitions of software reliability exist and several methods and approaches exist to measure reliability of a software project. However, in the literature no works focus on the applicability of these methods in all the development phases of real software projects.In this paper, we describe the methodology we adopted during the S-CASE FP7 European Project to predict reliability for both the S-CASE platform as well as for the software artefacts automatically generated by using the S-CASE platform. Two approaches have been adopted to compute reliability: the first one is the ROME Lab Model, a well adopted traditional approach in industry; the second one is an empirical approach defined by the authors in a previous work. An extensive dataset of results has been collected during all the phases of the project.The two approaches can complement each other, to support to prediction of reliability during all the development phases of a software system in order to facilitate the project management from a non-functional point-of-view
Functional size measures and effort estimation in agile development: A replicated study
No full textTo help developers during the Scrum planning poker, in our previous work we ran a case study on a Moonlight Scrum process to understand if it is possible to introduce functional size metrics to improve estimation accuracy and to measure the accuracy of expert-based estimation. The results of this original study showed that expert-based estimations are more accurate than those obtained by means of models, calculated with functional size measures. To validate the results and to extend them to plain Scrum processes, we replicated the original study twice, applying an exact replication to two plain Scrum development processes. The results of this replicated study show that the accuracy of the effort estimated by the developers is very accurate and higher than that obtained through functional size measures. In particular, SiFP and IFPUG Function Points, have low predictive power and are thus not help to improve the estimation accuracy in Scrum
An innovative platform to promote social media literacy in school contexts
No full textIn spite of the impressive number of adolescents using social media, only a minority is aware of the risks associated with the use of the Internet. Hate speech, violation of personal rights, psychological attacks, deceiving people with fake accounts, as well as cyberbullying, harassment and insults are some examples of toxic content that can jeopardize adolescent well-being on the Web. Social Media literacy paths in school contexts provide students with the proper defence instruments to face these problems. Furthermore, it is important to underline the role of social media on both the intrinsic and extrinsic motivation of adolescents which has short-and long-term influences when using these virtual environments. However, traditional teaching approaches are not enough to engage students, and the need for innovative learning activities and tools emerges. In this paper we present an online platform specifically designed to support the development of competences related to Information and Data Literacy, Communication and Collaboration and Digital Content Creation. These competences are connected to the most recent versions of the Digital Competence Framework for Citizens, and the Global framework of reference on digital literacy skills promoted by UNESCO. The platform is based on PixelFed, an open-source alternative to Instagram, so that adolescents can practice with an environment they are familiar with. Our platform extends the PixelFed environment with functionalities designed to implement use cases that make students aware of the mechanisms behind social media, such as the use of artificial intelligence algorithms to filter the content they have access to. This platform has been experimented during a pilot run with secondary school students, by proposing them educational activities based on our platform, aimed at educating and supporting students to increase their awareness and counteract the problems that arise within social media
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