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The set of concepts collectively known as Technical Debt (TD) assume that software liabilities set up a context that can make a future change more costly or impossible; and therefore repaying the debt should be pursued. However, software developers often disagree with an automatically generated list of improvement suggestions, which they consider not fitting or important for their own code. To shed light into the reasons that drive developers to adopt or reject refactoring opportunities (i.e. TD repayment), we have performed an empirical study on the potential factors that affect the developers' decision to agree with the removal of a specific TD liability. The study has been addressed to the developers of four well-known open-source applications. To increase the response rate, a personalized assessment has first been sent to each developer, summarizing his/her own contribution to the TD of the corresponding project. Responds have been collected through a custom built web application that presented code fragments suffering from violations as identified by SonarQube along with information that could possibly affect their level of agreement to the importance of resolving an issue. These factors include data such as the frequency of past changes in the module under study, the number of bugs, the type and intensity of the violation, the level of involvement of the developer and whether he/she is a contributor in the corresponding project. Multivariate statistical analysis methods have been used to understand the importance and the underlying relationships among these factors and the results are expected to be useful for researchers and practitioners in TD Management.
| citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 8 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
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