- Authors: Nikolaos Nikolaidis; Nikolaos Mittas; Apostolos Ampatzoglou; Elvira-Maria Arvanitou; Alexander Chatzigeorgiou
(Department of Applied Informatics, University of Macedonia, Greece)
- Location: Published in: IEEE Transactions on Software Engineering
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.