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Title: Assessing TD Macro-Management: A Nested Modelling Statistical Approach
Authors: Nikolaidis, Nikolaos
Mittas, Nikolaos
Ampatzoglou, Apostolos
Arvanitou, Elvira-Maria
Chatzigeorgiou, Alexander
Type: Article
Subjects: FRASCATI::Natural sciences::Computer and information sciences
Keywords: Technical Debt
Quality Analysis and Evaluation
Software maintenance
Issue Date: 23-Jan-2023
Source: IEEE Transactions on Software Engineering
First Page: 1
Last Page: 13
Abstract: Quality improvement can be performed at the: (a) micro-management level: interventions applied at a fine-grained level (e.g., at a class or method level, by applying a refactoring); or (b) macro-management level: interventions applied at a large-scale (e.g., at project level, by using a new framework or imposing a quality gate). By considering that the outcome of any activity can be characterized as the product of impact and scale , in this paper we aim at exploring the impact of Technical Debt (TD) Macro-Management, whose scale is by definition larger than TD Micro-Management. By considering that TD artifacts reside at the micro-level, the problem calls for a nested model solution; i.e., modeling the structure of the problem: artifacts have some inherent characteristics (e.g., size and complexity), but obey the same project management rules (e.g., quality gates, CI/CD features, etc.). In this paper, we use the Under-Bagging based Generalized Linear Mixed Models approach, to unveil project management activities that are associated with the existence of HIGH_TD artifacts, through an empirical study on 100 open-source projects. The results of the study confirm that micro-management parameters are associated with the probability of a class to be classified as HIGH_TD, but the results can be further improved by controlling some project-level parameters. Based on the findings of our nested analysis, we can advise practitioners on macro-technical debt management approaches (such as “ control the number of commits per day ”, “ adopt quality control practices ”, and “ separate testing and development teams ”) that can significantly reduce the probability of all software artifacts to concentrate HIGH_TD. Although some of these findings are intuitive, this is the first work that delivers empirical quantitative evidence on the relation between TD values and project- or process-level metrics.
ISSN: 0098-5589
Other Identifiers: 10.1109/TSE.2023.3237460
Appears in Collections:Department of Applied Informatics

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