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Authors: Pasquale Ardimento 1 ; Lerina Aversano 2 ; Mario Luca Bernardi 2 ; Marta Cimitile 3 and Martina Iammarino 2

Affiliations: 1 Computer Science Department, University of Bari Aldo Moro, Via E. Orabona 4, Bari, Italy ; 2 University of Sannio, Benevento, Italy ; 3 Unitelma Sapienza, University of Rome, Italy

Keyword(s): Design Smells Prediction, Software Quality, Deep Learning, Transfer Learning.

Abstract: This paper investigates whether the adoption of a transfer learning approach can be effective for just-in-time design smells prediction. The approach uses a variant of Temporal Convolutional Networks to predict design smells and a carefully selected fine-grained process and product metrics. The validation is performed on a dataset composed of three open-source systems and includes a comparison between transfer and direct learning. The hypothesis, which we want to verify, is that the proposed transfer learning approach is feasible to transfer the knowledge gained on mature systems to the system of interest to make reliable predictions even at the beginning of development when the available historical data is limited. The obtained results show that, when the class imbalance is high, the transfer learning provides F1-scores very close to the ones obtained by direct learning.

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Paper citation in several formats:
Ardimento, P.; Aversano, L.; Bernardi, M.; Cimitile, M. and Iammarino, M. (2021). Transfer Learning for Just-in-Time Design Smells Prediction using Temporal Convolutional Networks. In Proceedings of the 16th International Conference on Software Technologies - ICSOFT; ISBN 978-989-758-523-4; ISSN 2184-2833, SciTePress, pages 310-317. DOI: 10.5220/0010602203100317

@conference{icsoft21,
author={Pasquale Ardimento. and Lerina Aversano. and Mario Luca Bernardi. and Marta Cimitile. and Martina Iammarino.},
title={Transfer Learning for Just-in-Time Design Smells Prediction using Temporal Convolutional Networks},
booktitle={Proceedings of the 16th International Conference on Software Technologies - ICSOFT},
year={2021},
pages={310-317},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010602203100317},
isbn={978-989-758-523-4},
issn={2184-2833},
}

TY - CONF

JO - Proceedings of the 16th International Conference on Software Technologies - ICSOFT
TI - Transfer Learning for Just-in-Time Design Smells Prediction using Temporal Convolutional Networks
SN - 978-989-758-523-4
IS - 2184-2833
AU - Ardimento, P.
AU - Aversano, L.
AU - Bernardi, M.
AU - Cimitile, M.
AU - Iammarino, M.
PY - 2021
SP - 310
EP - 317
DO - 10.5220/0010602203100317
PB - SciTePress

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