Comparing Maintainability Index, SIG Method, and SQALE for Technical Debt Identification
| Authors | |
|---|---|
| Year of publication | 2020 |
| Type | Article in Periodical |
| Magazine / Source | Scientific programming |
| MU Faculty or unit | |
| Citation | |
| Web | https://www.hindawi.com/journals/sp/2020/2976564/ |
| Doi | http://dx.doi.org/10.1155/2020/2976564 |
| Keywords | Software Technical Debt; Software Maintenance; Software Quality; Maintainability Index; SIG Method; SQALE |
| Description | There are many definitions of software Technical Debt (TD) that were proposed over time. While many techniques to measure TD emerged in recent times, there is still not a clear understanding about how different techniques compare when applied to software projects. The goal of this paper is to shed some light on this aspect, by comparing three techniques about TD identification that were proposed over time: (i) the Maintainability Index (MI), (ii) SIG TD models, and (iii) SQALE analysis. Considering 20 open source Python libraries, we compare the TD measurements time series in terms of trends and evolution according to different sets of releases (major, minor, and micro), to see if the perception of practitioners about TD evolution could be impacted. While all methods report generally growing trends of TD over time, there are different patterns. SQALE reports more periods of steady states compared to MI and SIG TD. MI is the method that reports more repayments of TD compared to the other methods. SIG TD and MI are the models that show more similarity in the way TD evolves, while SQALE and MI are less similar. The implications are that each method gives slightly a different perception about TD evolution. |
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