The Delta Maintainability Model: Measuring Maintainability of Fine-Grained Code Changes

M. D. Biase, Ayushi Rastogi, M. Bruntink, A. Deursen
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引用次数: 18

Abstract

Existing maintainability models are used to identify technical debt of software systems. Targeting entire codebases, such models lack the ability to determine shortcomings of smaller, fine-grained changes. This paper proposes a new maintainability model – the Delta Maintainability Model (DMM) – to measure fine-grained code changes, such as commits, by adapting and extending the SIG Maintainability Model. DMM categorizes changed lines of code into low and high risk, and then uses the proportion of low risk change to calculate a delta score. The goal of the DMM is twofold: first, producing meaningful and actionable scores; second, compare and rank the maintainability of fine-grained modifications. We report on an initial study of the model, with the goal of understanding if the adapted measurements from the SIG Maintainability Model suit the fine-grained scope of the DMM. In a manual inspection process for 100 commits, 67 cases matched the expert judgment. Furthermore, we report an exploratory empirical study on a data set of DMM scores on 3,017 issue-fixing commits of four open source and four closed source systems. Results show that the scores of DMM can be used to compare and rank commits, providing developers with a means to do root cause analysis on activities that impacted maintainability and, thus, address technical debt at a finer granularity.
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增量可维护性模型:测量细粒度代码更改的可维护性
现有的可维护性模型用于识别软件系统的技术债务。针对整个代码库,这种模型缺乏确定较小的、细粒度更改的缺点的能力。本文提出了一个新的可维护性模型——Delta可维护性模型(DMM)——通过适应和扩展SIG可维护性模型来度量细粒度的代码更改,比如提交。DMM将更改的代码行分为低风险和高风险,然后使用低风险更改的比例来计算增量分数。DMM的目标有两个:第一,产生有意义和可操作的分数;其次,对细粒度修改的可维护性进行比较和排序。我们报告了对模型的初步研究,目的是了解来自SIG可维护性模型的适应度量是否适合DMM的细粒度范围。在100个提交的人工检查过程中,有67个案例符合专家的判断。此外,我们报告了一项探索性实证研究,该研究对四个开源和四个闭源系统的3,017个问题修复提交的DMM分数数据集进行了研究。结果表明,DMM的分数可以用于比较和对提交进行排序,为开发人员提供了一种方法,可以对影响可维护性的活动进行根本原因分析,从而在更细的粒度上处理技术债务。
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