Luigi Quaranta

Developers experience a wide range of emotions while creating software. Being able to identify the causes of one’s own and peers’ emotions can equip developers with the ability to regulate their behavior to restore positive moods and productivity. In this article, we investigate to what extent self-monitoring of emotions can enhance agile retrospective meetings by improving the emotion awareness of participants. To this aim, we conducted a controlled experiment involving three software development teams involving two student teams and one professional developers team. The experimental design involves the collection of biometrics and self-reported information about emotions, which are then visualized before the retrospective meetings to inform discussion using EmoVizPhy, a tool that we designed and implemented for this aim. While students found that self-monitoring helped them recall significant emotional episodes, leading to more meaningful contributions during retrospectives, professional developers perceived limited benefits from this practice. Furthermore, based on the analysis of corrective actions identified by the participants during the study, we hypothesize that self-monitoring of emotions through EmoVizPhy may play a valuable role in facilitating the consolidation of new agile teams for which roles and collaboration dynamics are still being defined.

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 presents the project outline, provides an executive summary of the research activities, outlines the expected project outcomes, and reports the results obtained to date.

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 performance even for tasks they have not been trained for. To the best of our knowledge, GPT-like models have not been applied yet to the problem of issue classification, despite the promising results achieved for many other software engineering tasks. In this paper, we investigate to what extent we can leverage GPT-like LLMs to automate the issue labeling task. Our results demonstrate the ability of GPT-like models to correctly classify issue reports in the absence of labeled data that would be required to fine-tune BERT-like LLMs.

In this paper, we present an overview of a project-based course on MLOps by showcasing a couple of sample projects developed by our students. Additionally, we share the lessons learned from offering the course at two different institutions.

Building and maintaining production-grade ML-enabled components is a complex endeavor that goes beyond the current approach of academic education, focused on the optimization of ML model performance in the lab. In this paper, we present a project-based learning approach to teaching MLOps, focused on the demonstration and experience with emerging practices and tools to automatize the construction of ML-enabled components. We examine the design of a course based on this approach, including laboratory sessions that cover the end-to-end ML component life cycle, from model building to production deployment. Moreover, we report on preliminary results from the first edition of the course. During the present year, an updated version of the same course is being delivered in two independent universities; the related learning outcomes will be evaluated to analyze the effectiveness of project-based learning for this specific subject.