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The Developer's Dilemma: Factors Affecting the Decision to Repay Code Debt

机译:开发者的困境:影响决定还清代码债务的因素

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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.
机译:统称为技术债务(TD)的概念集假设软件债务建立了可以使将来的变更变得更加昂贵或不可能的环境。因此,应追回债务。但是,软件开发人员通常不同意自动生成的改进建议列表,他们认为这对他们自己的代码不适合或不重要。为了阐明导致开发人员采用或拒绝重构机会的原因(即TD偿还),我们对影响开发人员的潜在因素进行了实证研究。决定同意免除特定的道明责任。该研究已针对四个著名的开源应用程序的开发人员。为了提高响应速度,首先已向每个开发人员发送了个性化评估,总结了他/她对相应项目的TD的贡献。已通过一个定制的Web应用程序收集了响应,该应用程序显示了SonarQube识别出的违反代码的代码片段以及可能影响其就解决问题的重要性达成协议的信息。这些因素包括数据,例如,正在研究的模块中过去更改的频率,错误的数量,违规的类型和强度,开发人员的参与程度以及他/她是否是相应项目的贡献者。多变量统计分析方法已被用于理解这些因素之间的重要性和潜在关系,并且预期结果将对TD Management的研究人员和从业人员有用。

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