论文标题
用于AST差异的超参数优化
Hyperparameter Optimization for AST Differencing
论文作者
论文摘要
计算同一程序的两个版本之间的差异是软件开发和软件进化研究的重要任务。 AST差异是最先进的方法，也是一个活跃的研究领域。但是，AST差异算法依赖于可能对其有效性产生强大影响的配置参数。在本文中，我们提出了一种名为DAT（DIFF自动调整）的新型方法，用于对AST差异的优化。我们彻底说明了AST差异的高配置问题。我们评估了数据驱动的方法DAT，以优化由最新的AST差异算法在不同方案中名为Gumtree的算法生成的编辑订阅。 DAT能够找到Gumtree的新配置，该配置可改善21.8％的评估案例。
Computing the differences between two versions of the same program is an essential task for software development and software evolution research. AST differencing is the most advanced way of doing so, and an active research area. Yet, AST differencing algorithms rely on configuration parameters that may have a strong impact on their effectiveness. In this paper, we present a novel approach named DAT (Diff Auto Tuning) for hyperparameter optimization of AST differencing. We thoroughly state the problem of hyper-configuration for AST differencing. We evaluate our data-driven approach DAT to optimize the edit-scripts generated by the state-of-the-art AST differencing algorithm named GumTree in different scenarios. DAT is able to find a new configuration for GumTree that improves the edit-scripts in 21.8% of the evaluated cases.
