Assessing TD Macro-Management: A Nested Modeling Statistical Approach

Nikolaos Nikolaidis

doi:10.1109/TSE.2023.3237460

2023-04-01

PRE

Nikolaos Nikolaidis

Nikolaos Mittas

Apostolos Ampatzoglou *

Elvira-Maria Arvanitou

Alexander Chatzigeorgiou

DOI：10.1109/TSE.2023.3237460

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摘要

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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.

Keyword:

Codes

Software

Logic gates

Informatics

Quality control

Project management

Benchmark testing

Technical debt

metrics

measurement

quality analysis and evaluation