论文标题
部分可观测时空混沌系统的无模型预测
Recommending Code Improvements Based on Stack Overflow Answer Edits
论文作者
论文摘要
储层计算是预测湍流的有力工具，其简单的架构具有处理大型系统的计算效率。然而，其实现通常需要完整的状态向量测量和系统非线性知识。我们使用非线性投影函数将系统测量扩展到高维空间，然后将其输入到储层中以获得预测。我们展示了这种储层计算网络在时空混沌系统上的应用，该系统模拟了湍流的若干特征。我们表明，使用径向基函数作为非线性投影器，即使只有部分观测并且不知道控制方程，也能稳健地捕捉复杂的系统非线性。最后，我们表明，当测量稀疏、不完整且带有噪声，甚至控制方程变得不准确时，我们的网络仍然可以产生相当准确的预测，从而为实际湍流系统的无模型预测铺平了道路。
Background: Sub-optimal code is prevalent in software systems. Developers may write low-quality code due to many reasons, such as lack of technical knowledge, lack of experience, time pressure, management decisions, and even unhappiness. Once sub-optimal code is unknowingly (or knowingly) integrated into the codebase of software systems, its accumulation may lead to large maintenance costs and technical debt. Stack Overflow is a popular website for programmers to ask questions and share their code snippets. The crowdsourced and collaborative nature of Stack Overflow has created a large source of programming knowledge that can be leveraged to assist developers in their day-to-day activities. Objective: In this paper, we present an exploratory study to evaluate the usefulness of recommending code improvements based on Stack Overflow answers' edits. Method: We propose Matcha, a code recommendation tool that leverages Stack Overflow code snippets with version history and code clone search techniques to identify sub-optimal code in software projects and suggest their optimised version. By using SOTorrent and GitHub datasets, we will quali-quantitatively investigate the usefulness of recommendations given by \textsc{Matcha} to developers using manual categorisation of the recommendations and acceptance of pull-requests to open-source projects.
