Towards Visualizing Large Scale Evolving Clones

2019 IEEE/ACM 41st International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) Pub Date : 2019-05-25 DOI:10.1109/ICSE-Companion.2019.00125

Debajyoti Mondal, Manishankar Mondal, C. Roy, Kevin A. Schneider, Shisong Wang, Yukun Li

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引用次数: 5

Abstract

Software systems in this big data era are growing larger and becoming more intricate. Tracking and managing code clones in such evolving software systems are challenging tasks. To understand how clone fragments are evolving, the programmers often analyze the co-evolution of clone fragments manually to decide about refactoring, tracking, and bug removal. Such manual analysis is infeasible for a large number of clones with clones evolving over hundreds of software revisions. We propose a visual analytics framework, that leverages big data visualization techniques to manage code clones in large software systems. Our framework combines multiple information-linked zoomable views, where users can explore and analyze clones through interactive exploration in real time. We discuss several scenarios where our framework may assist developers in real-life software development and clone maintenance. Experts' reviews reveal many future potentials of our framework.

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走向可视化大规模进化克隆

在这个大数据时代，软件系统变得越来越大，越来越复杂。在这种不断发展的软件系统中跟踪和管理代码克隆是一项具有挑战性的任务。为了理解克隆片段是如何进化的，程序员经常手动分析克隆片段的共同进化，以决定重构、跟踪和bug移除。这样的手工分析对于大量的克隆是不可行的，这些克隆在数百个软件版本中进化。我们提出了一个可视化分析框架，它利用大数据可视化技术来管理大型软件系统中的代码克隆。我们的框架结合了多个信息链接的可缩放视图，用户可以通过实时的交互式探索来探索和分析克隆。我们讨论了几个场景，在这些场景中，我们的框架可以帮助开发人员进行实际的软件开发和克隆维护。专家的评论揭示了我们框架的许多未来潜力。

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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来源期刊

2019 IEEE/ACM 41st International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)

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