An Empirical Analysis of the Maintainability Evolution of Open Source Systems

Abstract

Maintainability is a key factor for the evolution of an open source system due to the highly distributed development teams that contribute to many projects. In the literature there are a number of different approaches that has been developed to evaluate the maintainability of a product but almost each method has been developed in an independent way without leveraging on the existing work and with almost no independent evaluation of the performance of the models. In most of the cases, the models are only validated through a limited set of projects only by the people that propose the specific approach. This paper is a first step towards a different direction focusing on the independent application of the existing models to popular open source projects.

Quality attributes and performance satisfaction of open source library system: A survey amongst librarians in Pakistan

“…Because of the widely dispersed development teams that contribute to many projects, maintainability is an important factor in the evolution of an open source system (Kapllani et al 2020). Maintainability refers to the ability of KOHA systems to be updated, which can include corrections, enhancements, or modifications in response to changes in the environment, as well as criteria and functional specifications.…”

Analyzing Static Analysis Metric Trends towards Early Identification of Non-Maintainable Software Components

“…In the same context, Schnappinger et al [23] resorted to engaging experts in order to manually label a set of data regarding their maintainability degree and made use of a set of various metrics to evaluate maintainability. Other recent research works examine various metrics and approaches for evaluating maintainability, such as the examination of open source projects and their maintainability degree [24], the combination of machine learning techniques such as the Bayesian networks and association rules [25], the use of soft computing techniques such as the neuro-fuzzy model [26] or employing ensembles to predict maintainability on imbalanced data [27]. Other approaches involve the analysis of software releases as a way for evaluating the maintainability degree of software.…”