4 Contributo in Atti di Convegno (Proceeding)

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4.1 Contributo in Atti di convegno 19636
4.2 Abstract in Atti di convegno 5550
4.3 Poster 943

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A Preliminary Investigation of MLOps Practices in GitHub

Background. The rapid and growing popularity of machine learning (ML) applications has led to an increasing interest in MLOps, that is, the practice of continuous integration and deployment (CI/CD) of ML-enabled systems. Aims. Since changes may affect not only the code but also the ML model parameters and the data themselves, the automation of traditional CI/CD needs to be extended to manage model retraining in production. Method. In this paper, we present an initial investigat...

Leveraging GPT-like LLMs to Automate Issue Labeling

Issue labeling is a crucial task for the effective management of software projects. To date, several approaches have been put forth for the automatic assignment of labels to issue reports. In particular, supervised approaches based on the fine-tuning of BERT-like language models have been proposed, achieving state-of-the-art performance. More recently, decoder-only models such as GPT have become prominent in SE research due to their surprising capabilities to achieve state-of-the-art perfo...

Recognizing Developers' Emotions while Programming

Developers experience a wide range of emotions during programming tasks, which may have an impact on job performance. In this paper, we present an empirical study aimed at (i) investigating the link between emotion and progress, (ii) understanding the triggers for developers’ emotions and the strategies to deal with negative ones, (iii) identifying the minimal set of non-invasive biometric sensors for emotion recognition during programming tasks. Results confirm previous findings about the re...

Predicting Bugs by Monitoring Developers During Task Execution

Knowing which parts of the source code will be defective can allow practitioners to better allocate testing resources. For this reason, many approaches have been proposed to achieve this goal. Most state-of-the-art predictive models rely on product and process metrics, i.e., they predict the defectiveness of a component by considering what developers did. However, there is still limited evidence of the benefits that can be achieved in this context by monitoring how developers complete a dev...

ArguLens: Anatomy of Community Opinions On Usability Issues Using Argumentation Models

In open-source software (OSS), the design of usability is often influenced by the discussions among community members on platforms such as issue tracking systems (ITSs). However, digesting the rich information embedded in issue discussions can be a major challenge due to the vast number and diversity of the comments. We propose and evaluate ArguLens, a conceptual framework and automated technique leveraging an argumentation model to support effective understanding and consolidation of communi...

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