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

Nowadays, agile software development is considered a mainstream approach for software with fast release cycles and frequent changes in requirements. Most of the time, high velocity in software development implies poor software quality, especially when it comes to maintainability. In this work, we ar...

Descripción completa

Guardado en:
Detalles Bibliográficos
Autores principales: Thomas Karanikiotis, Michail D. Papamichail, Andreas L. Symeonidis
Formato: article
Lenguaje:EN
Publicado: MDPI AG 2021
Materias:
Acceso en línea:https://doaj.org/article/d7296517372a48a99a4f49f11513b16d
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
Descripción
Sumario:Nowadays, agile software development is considered a mainstream approach for software with fast release cycles and frequent changes in requirements. Most of the time, high velocity in software development implies poor software quality, especially when it comes to maintainability. In this work, we argue that ensuring the maintainability of a software component is not the result of a one-time only (or few-times only) set of fixes that eliminate technical debt, but the result of a continuous process across the software’s life cycle. We propose a maintainability evaluation methodology, where data residing in code hosting platforms are being used in order to identify non-maintainable software classes. Upon detecting classes that have been dropped from their project, we examine the progressing behavior of their static analysis metrics and evaluate maintainability upon the four primary source code properties: complexity, cohesion, inheritance and coupling. The evaluation of our methodology upon various axes, both qualitative and quantitative, indicates that our approach can provide actionable and interpretable maintainability evaluation at class level and identify non-maintainable components around 50% ahead of the software life cycle. Based on these results, we argue that the progressing behavior of static analysis metrics at a class level can provide valuable information about the maintainability degree of the component in time.