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Forecasting technical debt evolution in software systems:an empirical study

Frontiers of Computer Science 2023年第3期17卷 63-75页

作者： Lerina AVERSANO Mario Luca BERNARDI marta cimitile Martina IAMMARINO Debora MONTANO Department of Engineering University of SannioBenevento 82100Italy Department of Law and Economics Unitelma Sapienza University of RomeRome 00161Italy

Technical debt is considered detrimental to the long-term success of software development,but despite the numerous studies in the literature,there are still many aspects that need to be investigated for a better understanding of *** particular,the main problems that hinder its complete understanding are the absence of a clear definition and a model for its identification,management,and *** on forecasting technical debt,there is a growing notion that preventing technical debt build-up allows you to identify and address the riskiest debt items for the project before they can permanently compromise ***,despite this high relevance,the forecast of technical debt is still little *** this end,this study aims to evaluate whether the quality metrics of a software system can be useful for the correct prediction of the technical ***,the data related to the quality metrics of 8 different open-source software systems were analyzed and supplied as input to multiple machine learning algorithms to perform the prediction of the technical *** addition,several partitions of the initial dataset were evaluated to assess whether prediction performance could be improved by performing a data *** results obtained show good forecasting performance and the proposed document provides a useful approach to understanding the overall phenomenon of technical debt for practical purposes.

关键词： technical debt empirical study software quality metrics machine learning

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