activity
20162024
most citedMotivations, Challenges, Best Practices, and Benefits for Bots and Conversational Agents in Software Engineering: A Multivocal Literature Review

14 citations · 39 across the 10 of their papers we have counts for

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8 papers · 1 filter

cs.SE2024★ 1 cited

Investigating the Role of Cultural Values in Adopting Large Language Models for Software Engineering

Stefano Lambiase, Gemma Catolino, Fabio Palomba +2

As a socio-technical activity, software development involves the close interconnection of people and technology. The integration of Large Language Models (LLMs) into this process e…

cs.SE2024★ 14 cited

Motivations, Challenges, Best Practices, and Benefits for Bots and Conversational Agents in Software Engineering: A Multivocal Literature Review

Stefano Lambiase, Gemma Catolino, Fabio Palomba +1

Bots are software systems designed to support users by automating a specific process, task, or activity. When such systems implement a conversational component to interact with the…

cs.SE2024★ 6 cited

A Catalog of Fairness-Aware Practices in Machine Learning Engineering

Gianmario Voria, Giulia Sellitto, Carmine Ferrara +5

Machine learning's widespread adoption in decision-making processes raises concerns about fairness, particularly regarding the treatment of sensitive features and potential discrim…

cs.SE2024★ 3 cited

When Code Smells Meet ML: On the Lifecycle of ML-specific Code Smells in ML-enabled Systems

Gilberto Recupito, Giammaria Giordano, Filomena Ferrucci +2

Context. The adoption of Machine Learning (ML)--enabled systems is steadily increasing. Nevertheless, there is a shortage of ML-specific quality assurance approaches, possibly beca…

cs.SE2023

Test Code Refactoring Unveiled: Where and How Does It Affect Test Code Quality and Effectiveness?

Luana Martins, Valeria Pontillo, Heitor Costa +3

Context. Refactoring has been widely investigated in the past in relation to production code quality, yet still little is known on how developers apply refactoring on test code. Sp…

cs.SE2022★ 1 cited

Machine Learning-Based Test Smell Detection

Valeria Pontillo, Dario Amoroso d'Aragona, Fabiano Pecorelli +3

Context: Test smells are symptoms of sub-optimal design choices adopted when developing test cases. Previous studies have proved their harmfulness for test code maintainability and…