Abstract As the complexity of a software system grows, it becomes harder for developers to be aware of all the dependencies between its artifacts (e.g., files or methods). Change impact analysis helps to overcome this challenge, by recommending relevant source-code artifacts related to a developer’s current changes. Association rule mining has shown promise in determining change impact by uncovering relevant patterns in the system’s change history. State-of-the-art change impact mining typically uses a change history of tens of thousands of transactions. For efficiency, targeted association rule mining constrains the transactions used to those potentially relevant to answering a particular query. However, it still considers all the relevant transactions in the history. This paper presents Atari , a new adaptive approach that further constrains targeted association rule mining by considering a dynamic selection of the relevant transactions. Our investigation of adaptive change impact mining empirically studies fourteen algorithm variants. We show that adaptive algorithms are viable, can be just as applicable as the start-of-the-art complete-history algorithms, and even outperform them for certain queries. However, more important than this direct comparison, our investigation motivates and lays the groundwork for the future study of adaptive techniques, and their application to challenges such as on-the-fly impact analysis at GitHub-scale.

中文翻译：

---

自适应变更推荐

摘要 随着软件系统复杂性的增加，开发人员越来越难以意识到其工件（例如，文件或方法）之间的所有依赖关系。变更影响分析通过推荐与开发人员当前变更相关的相关源代码工件，有助于克服这一挑战。通过揭示系统变更历史中的相关模式，关联规则挖掘在确定变更影响方面显示出前景。最先进的变更影响挖掘通常使用数万笔交易的变更历史。为了提高效率，有针对性的关联规则挖掘将使用的事务限制为与回答特定查询可能相关的事务。但是，它仍然考虑历史中的所有相关交易。本文介绍了雅达利，一种新的自适应方法，通过考虑相关事务的动态选择来进一步限制目标关联规则挖掘。我们对自适应变化影响挖掘的调查实证研究了 14 种算法变体。我们表明自适应算法是可行的，可以与最先进的完整历史算法一样适用，甚至在某些查询上的表现优于它们。然而，比这种直接比较更重要的是，我们的调查激发并奠定了未来自适应技术研究的基础，以及它们在 GitHub 规模的即时影响分析等挑战中的应用。我们表明自适应算法是可行的，可以与最先进的完整历史算法一样适用，甚至在某些查询上的表现优于它们。然而，比这种直接比较更重要的是，我们的调查激发并奠定了未来自适应技术研究的基础，以及它们在 GitHub 规模的即时影响分析等挑战中的应用。我们表明自适应算法是可行的，可以与最先进的完整历史算法一样适用，甚至在某些查询上的表现优于它们。然而，比这种直接比较更重要的是，我们的调查激发并奠定了未来自适应技术研究的基础，以及它们在 GitHub 规模的即时影响分析等挑战中的应用。