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An Analysis of Problem-Solving Patterns in Open Source Software

机译:开源软件中的问题解决模式分析

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Open Source Software (OSS) has become an important environment where developers can create, exchange, and improve reusable software assets by collaborating with other developers. Although developers may find useful software assets to reuse from OSS for their projects, they usually experience difficulties in solving problems that occur while integrating the assets to their own software. We investigated data from major open source environments such as Sourceforge. net and GitHub, and learned that there is a common pattern of solving reuse-related problems in OSS. To analyze the pattern in detail, we have developed an ontological model to formally represent the symptoms and causes of the reuse-related problems, and the correlations between them. Based on this model, we collected data from Sourceforge.net, and built a knowledge base for the most common problem type. We extracted the core types of symptoms and causes for the problem type and calculated the number of correlations between the types of symptoms and causes. We found that there exist correlations between the symptoms and causes that are extracted from the discussion threads for the problem type, and about 60% of them are statistically significant. We also conducted a study to understand the effective timing of recommending solutions to the developers by analyzing the recall rates of finding the causes of the problems in a timeline. We figured that most of the important causes of a problem are discussed at the beginning of the forum discussion. This leads us to the conclusion that recommending the causes of a problem early by using our knowledge framework may help developers spend less amount of time to solve the problem (around 50% less time than solving the problem without using our framework).
机译:开源软件(OSS)已成为重要的环境,开发人员可以通过与其他开发人员合作来创建,交换和改善可重用的软件资产。尽管开发人员可能会发现有用的软件资产可从OSS重复使用以用于其项目,但他们通常在解决将资产集成到其自己的软件时出现的问题时会遇到困难。我们调查了来自主要开源环境(例如Sourceforge)的数据。 net和GitHub,并了解到有一种解决OSS中与重用相关的问题的通用模式。为了详细分析模式,我们开发了一个本体模型来正式表示与重用相关的问题的症状和原因,以及它们之间的相关性。基于此模型,我们从Sourceforge.net收集了数据,并为最常见的问题类型建立了知识库。我们提取了问题类型的症状和原因的核心类型,并计算了症状和原因的类型之间的相关性数量。我们发现从问题类型的讨论线程中提取的症状和原因之间存在相关性,其中约60%具有统计学意义。我们还进行了一项研究,以通过分析在时间表中找出问题原因的召回率,来了解向开发人员推荐解决方案的有效时机。我们认为在论坛讨论的开始阶段已经讨论了问题的大多数重要原因。这导致我们得出的结论是,通过使用我们的知识框架尽早推荐问题的原因可能会帮助开发人员花费更少的时间来解决问题(比不使用我们的框架解决问题的时间少50%)。

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