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...

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Autores principales:	Thomas Karanikiotis, Michail D. Papamichail, Andreas L. Symeonidis
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	software maintainability software quality software evolution trend analysis static analysis metrics Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/d7296517372a48a99a4f49f11513b16d
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spelling	oai:doaj.org-article:d7296517372a48a99a4f49f11513b16d2021-11-25T19:04:54ZAnalyzing Static Analysis Metric Trends towards Early Identification of Non-Maintainable Software Components10.3390/su1322128482071-1050https://doaj.org/article/d7296517372a48a99a4f49f11513b16d2021-11-01T00:00:00Zhttps://www.mdpi.com/2071-1050/13/22/12848https://doaj.org/toc/2071-1050Nowadays, 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.Thomas KaranikiotisMichail D. PapamichailAndreas L. SymeonidisMDPI AGarticlesoftware maintainabilitysoftware qualitysoftware evolutiontrend analysisstatic analysis metricsEnvironmental effects of industries and plantsTD194-195Renewable energy sourcesTJ807-830Environmental sciencesGE1-350ENSustainability, Vol 13, Iss 12848, p 12848 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	software maintainability software quality software evolution trend analysis static analysis metrics Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
spellingShingle	software maintainability software quality software evolution trend analysis static analysis metrics Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350 Thomas Karanikiotis Michail D. Papamichail Andreas L. Symeonidis Analyzing Static Analysis Metric Trends towards Early Identification of Non-Maintainable Software Components
description	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.
format	article
author	Thomas Karanikiotis Michail D. Papamichail Andreas L. Symeonidis
author_facet	Thomas Karanikiotis Michail D. Papamichail Andreas L. Symeonidis
author_sort	Thomas Karanikiotis
title	Analyzing Static Analysis Metric Trends towards Early Identification of Non-Maintainable Software Components
title_short	Analyzing Static Analysis Metric Trends towards Early Identification of Non-Maintainable Software Components
title_full	Analyzing Static Analysis Metric Trends towards Early Identification of Non-Maintainable Software Components
title_fullStr	Analyzing Static Analysis Metric Trends towards Early Identification of Non-Maintainable Software Components
title_full_unstemmed	Analyzing Static Analysis Metric Trends towards Early Identification of Non-Maintainable Software Components
title_sort	analyzing static analysis metric trends towards early identification of non-maintainable software components
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/d7296517372a48a99a4f49f11513b16d
work_keys_str_mv	AT thomaskaranikiotis analyzingstaticanalysismetrictrendstowardsearlyidentificationofnonmaintainablesoftwarecomponents AT michaildpapamichail analyzingstaticanalysismetrictrendstowardsearlyidentificationofnonmaintainablesoftwarecomponents AT andreaslsymeonidis analyzingstaticanalysismetrictrendstowardsearlyidentificationofnonmaintainablesoftwarecomponents
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Analyzing Static Analysis Metric Trends towards Early Identification of Non-Maintainable Software Components

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