activity
20192026
most citedUnderstanding Flaky Tests: The Developer's Perspective

169 citations · 335 across the 56 of their papers we have counts for

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Showing 2023Show all

6 papers · 1 filter

cs.SE2023★ 2 cited

Classification, Challenges, and Automated Approaches to Handle Non-Functional Requirements in ML-Enabled Systems: A Systematic Literature Review

Vincenzo De Martino, Fabio Palomba

Context: Machine learning (ML) is nowadays so pervasive and diffused that virtually no application can avoid its use. Nonetheless, its enormous potential is often tempered by the n…

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.SE2023★ 43 cited

A systematic literature review on the code smells datasets and validation mechanisms

Morteza Zakeri-Nasrabadi, Saeed Parsa, Ehsan Esmaili +1

The accuracy reported for code smell-detecting tools varies depending on the dataset used to evaluate the tools. Our survey of 45 existing datasets reveals that the adequacy of a d…

cs.SE2023★ 15 cited

FedCSD: A Federated Learning Based Approach for Code-Smell Detection

Sadi Alawadi, Khalid Alkharabsheh, Fahed Alkhabbas +4

This paper proposes a Federated Learning Code Smell Detection (FedCSD) approach that allows organizations to collaboratively train federated ML models while preserving their data p…

cs.SE2023★ 4 cited

The Quantum Frontier of Software Engineering: A Systematic Mapping Study

Manuel De Stefano, Fabiano Pecorelli, Dario Di Nucci +2

Context. Quantum computing is becoming a reality, and quantum software engineering (QSE) is emerging as a new discipline to enable developers to design and develop quantum programs…

cs.SE2023★ 3 cited

Toward End-to-End MLOps Tools Map: A Preliminary Study based on a Multivocal Literature Review

Sergio Moreschi, Gilberto Recupito, Valentina Lenarduzzi +3

MLOps tools enable continuous development of machine learning, following the DevOps process. Different MLOps tools have been presented on the market, however, such a number of tool…