Forecasting the Developer’s Impact in Managing the Technical Debt
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-49269-3_4
Reference25 articles.
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2. Ardimento, P., Aversano, L., Bernardi, M.L., Cimitile, M., Iammarino, M.: Using deep temporal convolutional networks to just-in-time forecast technical debt principal. J. Syst. Softw. 194, 111481 (2022). https://doi.org/10.1016/j.jss.2022.111481. https://www.sciencedirect.com/science/article/pii/S0164121222001649
3. Aversano, L., Bernardi, M.L., Cimitile, M., Iammarino, M., Montano, D.: Forecasting technical debt evolution in software systems: an empirical study. Front. Comp. Sci. 17(3), 173210 (2023)
4. Aversano, L., Iammarino, M., Carapella, M., Vecchio, A.D., Nardi, L.: On the relationship between self-admitted technical debt removals and technical debt measures. Algorithms 13(7), 168 (2020). https://www.mdpi.com/1999-4893/13/7/168
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