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Catalogo Ricerca: [37913] Home page macrotipologia

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1 Contributo su Rivista 20709
- 1.1 Articolo in rivista 18948
- 1.2 Recensione in rivista 787
- 1.3 Scheda bibliografica 146
- 1.4 Nota a sentenza 293
- 1.5 Abstract in rivista 514
- 1.6 Traduzione in rivista 21

2 Contributo in Volume 7714
- 2.1 Contributo in volume (Capitolo o Saggio) 6715
- 2.2 Prefazione/Postfazione 254
- 2.3 Breve introduzione 117
- 2.4 Voce (in dizionario o enciclopedia) 445
- 2.5 Traduzione in volume 50
- 2.6 Recensione in volume 7
- 2.7 Schede di catalogo 126

3 Libro 1261
- 3.1 Monografia o trattato scientifico 1129
- 3.2 Concordanza 0
- 3.3 Indice 7
- 3.4 Bibliografia 3
- 3.5 Edizione critica 42
- 3.6 Pubblicazione di fonti inedite 15
- 3.7 Commento scientifico 20
- 3.8 Traduzione di libro 45

4 Contributo in Atti di Convegno (Proceeding) 6399
- 4.1 Contributo in Atti di convegno 4717
- 4.2 Abstract in Atti di convegno 1467
- 4.3 Poster 215

5 Altro 494
- 5.01 Composizione 1
- 5.02 Disegno 1
- 5.03 Design 0
- 5.04 Performance 0
- 5.05 Esposizione 0
- 5.06 Mostra 5
- 5.07 Manufatto 0
- 5.08 Prototipo d'arte e relativi progetti 0
- 5.09 Cartografia 6
- 5.10 Banca dati 7
- 5.11 Software 3
- 5.12 Altro 471
- 5.13 Progetto architettonico 0

6 Brevetti 113
- 6.1 Brevetto 113

7 Curatele 679
- 7.1 Curatela 679

8 Tesi 544
- 8.1 Tesi di dottorato 331
- 8.2 Tesi di dottorato (Ex-ROAD) 213

Prodotti recenti

QualAI: Continuous Quality Improvement of AI-based Systems

QualAI is a two-year project that aims to define a set of recommenders to continuously monitor, assess, and improve the quality of AI-based systems, with a particular focus on ML-based systems. Quality assurance will be guaranteed from different perspectives and during both the development and operations phases. We will define recommenders for the quality assurance of both data and ML models to enable practitioners to mitigate technical debt. Emphasis will be given to communication issues tha...

Continuous Quality Improvement of AI-based Systems: the QualAI Project

QualAI is a two-year project aimed at defining a set of recommenders to continuously monitor, assess, and improve the quality of AI-based systems, with a particular focus on machine learning (ML) applications. We will develop recommenders for the quality assurance of both data and ML models to enable practitioners to mitigate technical debt. Special attention will be paid to communication challenges that may arise in hybrid teams comprising data scientists and software developers. This paper ...

Is it Really Fun? Detecting Low Engagement Events in Video Games

The gaming industry has witnessed remarkable growth in recent years, attracting millions of people who engage in its products both as a hobby and for professional purposes (e.g., e-sports). Video games are software products that have a unique and fundamental requirement: They must be engaging. Previous research introduced approaches aimed at measuring engagement, some of which specifically designed for video games. Such approaches could be useful for practitioners since they can be adopted on...

Personalized Code Readability Assessment: Are We There Yet?

Unreadable code could be a breeding ground for errors. Thus, previous work defined approaches based on machine learning to automatically assess code readability that can warn developers when some code artifacts (e.g., classes) become unreadable. Given datasets of code snippets manually evaluated by several developers in terms of their perceived readability, such approaches (i) establish a snippet-level ground truth, and (ii) train a binary (readable/unreadable) or a ternary (readable/neutral/...

Enhancing Just-In-Time Defect Prediction Models with Developer-Centric Features

Ensuring high software quality in development cycles with frequent updates is critical, especially in Agile and CI/CD environments. Just-In-Time Software Defect Prediction (JIT-SDP) has emerged as a promising solution for finding bugs early, as it enables immediate identification of changes prone to defects. JIT-SPD models based on Machine Learning focus primarily on project- and change-specific features, such as number of lines added and number of files modified in the change. Recent researc...

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IRIS Catalogo Istituzionale della Ricerca dell'Università degli Studi del Molise