Comparing maintainability index, SIG Method, and SQALE for technical debt identification | Zendy

Peter Strečanský | Zendy; Stanislav Chren | Zendy; Bruno Rossi | Zendy

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Comparing maintainability index, SIG Method, and SQALE for technical debt identification

Author(s) -

Peter Strečanský,

Stanislav Chren,

Bruno Rossi

Publication year - 2020

Publication title -

hindawi journal of chemistry (hindawi)

Language(s) - English

Resource type - Conference proceedings

ISBN - 978-1-4503-6866-7

DOI - 10.1145/3341105.3374079

Subject(s) - maintainability , technical debt , python (programming language) , computer science , index (typography) , identification (biology) , software , debt , open source , data mining , data science , software engineering , world wide web , software development , operating system , finance , business , botany , biology

Many techniques have emerged to evaluate software Technical Debt (TD). However, differences in reporting TD are not yet studied widely, as they can give different perceptions about the evolution of TD in projects. The goal of this paper is to compare three TD identification techniques: i. Maintainability Index (MI), ii. SIG TD models and iii. SQALE analysis. Considering 17 large open source Python libraries, we compare TD measurements time series in terms of trends in different sets of releases (major, minor, micro). While all methods report generally growing trends of TD over time, MI, SIG TD, and SQALE all report different patterns of TD evolution.

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