Comparing Multivariate Time Series Analysis and Machine Learning Performance for Technical Debt Prediction: The SQALE Index Case-Reference-Cited by-同舟云学术

Comparing Multivariate Time Series Analysis and Machine Learning Performance for Technical Debt Prediction: The SQALE Index Case

Published:2024-04-14 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 7th ACM/IEEE International Conference on Technical Debt
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Author:

Robredo Mikel¹^ORCID,Saarimaki Nyyti²^ORCID,Penaloza Rafael³^ORCID,Taibi Davide¹^ORCID,Lenarduzzi Valentina¹^ORCID

Affiliation:

1. University of Oulu, Oulu, Finland

2. Tampere University, Tampere, Finland

3. University of Milano-Bicocca, Milano, Italy

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3644384.3644472

Reference12 articles.

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2. Valentina Lenarduzzi, Nyyti Saarimäki, and Davide Taibi. 2020. Some sonarqube issues have a significant but small effect on faults and changes. a large-scale empirical study. Journal of Systems and Software (2020), 110750.

3. Maria Mathioudaki Dimitrios Tsoukalas Miltiadis Siavvas and Dionysios Kehagias. 2022. Comparing Univariate and Multivariate Time Series Models For Technical Debt Forecasting. In Computational Science and Its Applications. 62--78.

4. S. Olbrich, D. S. Cruzes, V. Basili, and N. Zazworka. 2009. The evolution and impact of code smells: A case study of two open source systems. In International Symposium on Empirical Software Engineering and Measurement. 390--400.

5. Fabio Palomba, Gabriele Bavota, Massimiliano Di Penta, Fausto Fasano, Rocco Oliveto, and Andrea De Lucia. 2018. On the diffuseness and the impact on maintainability of code smells: a large scale empirical investigation. Empirical Software Engineering 23, 3 (01 Jun 2018), 1188--1221.

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