Ying Zhou

Data constraints are important business rules that specify the values allowed or required for the data used in a software system. These constraints are typically described in textual software artifacts ( e.g., requirements, developer documents, user manuals, etc. ). In this paper, we focus on automating the task of identifying data constraints in natural language software documents. In order to automate this task, we first study how data constraints are described in natural language. Specifically, we studied 546 data constraints described in the natural language artifacts of nine software systems. We uncovered and documented nine linguistic discourse patterns that stakeholders use to describe data constraints in natural language. We cast the constraint identification and extraction as a text matching and classification problem. We explore the suitability of traditional machine learning techniques, using the discourse patterns we discovered, together with other linguistic elements, as features, for automatically classifying sentence fragments as data constraint descriptions. The best combination of features and learner achieves 63.63% precision and 61.40% recall (62.34% F1). Automating the extraction of data constraints from natural language text is essential for automating their traceability to code, test generation, compliance analysis, etc.

Bug report management is a costly software maintenance process comprised of several challenging tasks. Given the UI-driven nature of mobile apps, bugs typically manifest through the UI, hence the identification of buggy UI screens and UI components (Buggy UI Localization) is important to localizing the buggy behavior and eventually fixing it. However, this task is challenging as developers must reason about bug descriptions (which are often low-quality), and the visual or code-based representations of UI screens. This paper is the first to investigate the feasibility of automating the task of Buggy UI Localization through a comprehensive study that evaluates the capabilities of one textual and two multi-modal deep learning (DL) techniques and one textual unsupervised technique. We evaluate such techniques at two levels of granularity, Buggy UI Screen and UI Component localization. Our results illustrate the individual strengths of models that make use of different representations, wherein models that incorporate visual information perform better for UI screen localization, and models that operate on textual screen information perform better for UI component localization – highlighting the need for a localization approach that blends the benefits of both types of techniques. Furthermore, we study whether Buggy UI Localization can improve traditional buggy code localization, and find that incorporating localized buggy UIs leads to improvements of 9%-12% in Hits@10.

We propose a scheme to generate entanglement between two cavity modes and squeeze magnon mode in a magnon-cavity QED system, where the two microwave cavity modes are coupled with a massive yttrium iron garnet (YIG) sphere through magnetic dipole interaction. The nonlinearity used in our system originates from a squeezed driving via parametric down-conversion process, which is the reason to cause entanglement and squeezing. By using the mean field approximation and employing experimentally feasible parameters, we demonstrate that the system shows zero entanglement and squeezing without squeezed driving. Meanwhile, our QED system denotes that the entanglement between squeezed cavity mode and magnon mode can be transferred to the other cavity mode and magnon mode via magnon-cavity coupling interaction, and then the two cavity modes get entangled. A genuinely tripartite entangled state is formed. We also show that magnon mode can be prepared in a squeezed state via magnon-cavity beam-splitter interaction, which is as a result of the squeezed field. Moreover, we show that it is a good way to enhance entanglement and squeezing by increasing the nonlinear gain coefficient of squeezed driving. Our results denote that magnon-cavity QED system is a powerful platform for studying macroscopic quantum phenomena, which illustrates a new method to photon-photon entanglement and magnon squeezing.