Towards identifying and minimizing customer-facing documentation debt

Lakmal Silva, M. Unterkalmsteiner, K. Wnuk
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Abstract

Background: Software documentation often struggles to catch up with the pace of software evolution. The lack of correct, complete, and up-to-date documentation results in an increasing number of documentation defects which could introduce delays in integrating software systems. In our previous study on a bug analysis tool called MultiDimEr, we provided evidence that documentation-related defects contribute to a significant number of bug reports. Aims: First, we want to identify documentation defect types contributing to documentation defects and thereby identifying documentation debt. Secondly, we aim to find pragmatic solutions to minimize most common documentation defects to pay off the documentation debt in the long run. Method: We investigated documentation defects related to an industrial software system. First, we looked at the types of different documentation and associated bug reports. We categorized the defects according to an existing documentation defect taxonomy. Results: Based on a sample of 101 defects, we found that a majority of defects are caused by documentation defects falling into the Information Content (What) category (86). Within this category, the documentation defect types Erroneous code examples (23), Missing documentation (35), and Outdated content (19) contributed to most of the documentation defects. We propose to adapt two solutions to mitigate these types of documentation defects. Conclusions: In practice, documentation debt can easily go undetected since a large share of resources and focus is dedicated to deliver high-quality software. This study provides evidence that documentation debt can contribute to increase in maintenance costs due to the number of documentation defects. We suggest to adapt two main solutions to tackle documentation debt by implementing (i) Dynamic Documentation Generation (DDG) and/or (ii) Automated Documentation Testing (ADT), which are both based on defining a single and robust information source for documentation.
识别并最小化面向客户的文档债务
背景:软件文档常常努力追赶软件发展的步伐。缺乏正确、完整和最新的文档会导致越来越多的文档缺陷,这可能会导致软件系统集成的延迟。在我们之前对称为MultiDimEr的错误分析工具的研究中,我们提供了与文档相关的缺陷导致大量错误报告的证据。目标:首先,我们想要确定导致文档缺陷的文档缺陷类型,从而确定文档债务。其次,我们的目标是找到实用的解决方案,以最小化最常见的文档缺陷,从而在长期运行中偿还文档债务。方法:我们调查了与工业软件系统相关的文档缺陷。首先,我们查看了不同文档的类型和相关的bug报告。我们根据现有的文档缺陷分类法对缺陷进行了分类。结果:基于101个缺陷的样本,我们发现大多数缺陷是由落入信息内容(What)类别(86)的文档缺陷引起的。在这个类别中,文档缺陷类型错误的代码示例(23)、缺失的文档(35)和过时的内容(19)导致了大多数文档缺陷。我们建议采用两种解决方案来减轻这些类型的文档缺陷。结论:在实践中,文档债务很容易被忽略,因为大量的资源和焦点都用于交付高质量的软件。本研究提供的证据表明,由于文件缺陷的数量,文件债务可能导致维护成本的增加。我们建议采用两种主要的解决方案,通过实现(i)动态文档生成(DDG)和/或(ii)自动文档测试(ADT)来解决文档债务,这两种解决方案都基于为文档定义一个单一且健壮的信息源。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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