迈向精确的技术债务评估:早期结果和研究路线图

Proceedings of the 3rd ACM SIGSOFT International Workshop on Machine Learning Techniques for Software Quality Evaluation Pub Date : 2019-08-02 DOI:10.1145/3340482.3342747

Valentina Lenarduzzi, A. Martini, D. Taibi, D. Tamburri

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引用次数: 31

摘要

技术债务的概念已经从许多角度进行了探索，但其精确估计仍处于大量的实证和实验探究之下。我们的目标是了解，通过利用近似的、数据驱动的、机器学习的方法，是否有可能改进当前的技术债务估计技术，如SonarQube等顶级行业质量分析工具。为了简单起见，我们将重点放在相对简单的回归建模技术上，并将其应用于与所研究项目中存在的次优条件相关的额外项目成本建模。我们的结果表明，当前的技术可以朝着更精确的技术债务估计的方向改进，并且案例研究显示了在识别更准确的技术债务估计方面有希望的结果。

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Towards surgically-precise technical debt estimation: early results and research roadmap

The concept of technical debt has been explored from many perspectives but its precise estimation is still under heavy empirical and experimental inquiry. We aim to understand whether, by harnessing approximate, data-driven, machine-learning approaches it is possible to improve the current techniques for technical debt estimation, as represented by a top industry quality analysis tool such as SonarQube. For the sake of simplicity, we focus on relatively simple regression modelling techniques and apply them to modelling the additional project cost connected to the sub-optimal conditions existing in the projects under study. Our results shows that current techniques can be improved towards a more precise estimation of technical debt and the case study shows promising results towards the identification of more accurate estimation of technical debt.

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Proceedings of the 3rd ACM SIGSOFT International Workshop on Machine Learning Techniques for Software Quality Evaluation

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