Zifazah: A Scientific Visualization Language for Tensor Field Visualizations

This thesis presents the design and prototype implementation of a scientific visualization language called Zifazah for composing and exploring 3D visualizations of diffusion tensor magnetic resonance imaging (DT-MRI or DTI) data. Unlike existing tools allowing flexible customization of data visualizations that are programmer-oriented, Zifazah focuses on domain scientists as end users in order to enable them to freely compose visualizations of their scientific data set. Verbal descriptions of end users about how they would build and explore DTI visualizations are analyzed to collect syntax, semantics, and control structures of the language. Zifazah makes use of the initial set of lexical terms and semantical patterns to provide a declarative language in the spirit of intuitive syntax and usage. Along with sample scripts representative of the main language design features, some new DTI visualizations created by end users using the novel language have also been presented

Exploiting Parts-of-Speech for Effective Automated Requirements Traceability

Context: Requirement traceability (RT) is defined as the ability to describe and follow the life of a requirement. RT helps developers ensure that relevant requirements are implemented and that the source code is consistent with its requirement with respect to a set of traceability links called trace links. Previous work leverages Parts Of Speech (POS) tagging of software artifacts to recover trace links among them. These studies work on the premise that discarding one or more POS tags results in an improved accuracy of Information Retrieval (IR) techniques. Objective: First, we show empirically that excluding one or more POS tags could negatively impact the accuracy of existing IR-based traceability approaches, namely the Vector Space Model (VSM) and the Jensen Shannon Model (JSM). Second, we propose a method that improves the accuracy of IR-based traceability approaches. Method: We developed an approach, called ConPOS, to recover trace links using constraint-based pruning. ConPOS uses major POS categories and applies constraints to the recovered trace links for pruning as a filtering process to significantly improve the effectiveness of IR-based techniques. We conducted an experiment to provide evidence that removing POSs does not improve the accuracy of IR techniques. Furthermore, we conducted two empirical studies to evaluate the effectiveness of ConPOS in recovering trace links compared to existing peer RT approaches. Results: The results of the first empirical study show that removing one or more POS negatively impacts the accuracy of VSM and JSM. Furthermore, the results from the other empirical studies show that ConPOS provides 11%-107%, 8%-64%, and 15%-170% higher precision, recall, and mean average precision (MAP) than VSM and JSM. Conclusion: We showed that ConPosout performs existing IR-based RT approaches that discard some POS tags from the input documents

Do Pre-trained Language Models Indeed Understand Software Engineering Tasks?

Artificial intelligence (AI) for software engineering (SE) tasks has recently achieved promising performance. In this paper, we investigate to what extent the pre-trained language model truly understands those SE tasks such as code search, code summarization, etc. We conduct a comprehensive empirical study on a board set of AI for SE (AI4SE) tasks by feeding them with variant inputs: 1) with various masking rates and 2) with sufficient input subset method. Then, the trained models are evaluated on different SE tasks, including code search, code summarization, and duplicate bug report detection. Our experimental results show that pre-trained language models are insensitive to the given input, thus they achieve similar performance in these three SE tasks. We refer to this phenomenon as overinterpretation, where a model confidently makes a decision without salient features, or where a model finds some irrelevant relationships between the final decision and the dataset. Our study investigates two approaches to mitigate the overinterpretation phenomenon: whole word mask strategy and ensembling. To the best of our knowledge, we are the first to reveal this overinterpretation phenomenon to the AI4SE community, which is an important reminder for researchers to design the input for the models and calls for necessary future work in understanding and implementing AI4SE tasks.Comment: arXiv admin note: text overlap with arXiv:2202.08005 by other author

Focusing MSs for High-Gain Antenna Applications

Recently, metasurfaces (MSs) have continuously drawn significant attentions in the area of enhancing the performances of the conventional antennas. Thereinto, focusing MSs with hyperbolic phase distributions can be used for designing high-gain antennas. In this chapter, we first design a new reflected MS and use a spiral antenna as the feeding source to achieve a wideband high-gain antenna. On this basis, we propose a bi-layer reflected MS to simultaneously enhance the gain and transform the linear polarization to circular polarization of the Vivaldi antenna. Then, we proposed a multilayer transmitted MS and use it to enhance the gain of a patch antenna. This kind of high-gain antenna eliminates the feed-block effect of the reflected ones but suffer from multilayer fabrication. To conquer this problem, we finally propose a single-layer transmitted focusing MS by grouping two different kinds of elements and use it to successfully design a low-profile high-gain antenna

High Performance Metasurface Antennas

Recently, metasurfaces (MSs) have received tremendous attention because their electromagnetic properties can be controlled at will. Generally, metasurface with hyperbolic phase distributions, namely, focusing metasurface, can be used to design high-gain antennas. Besides, metasurface has the ability of controlling the polarization state of electromagnetic wave. In this chapter, we first propose a new ultrathin broadband reflected MS and take it into application for high-gain planar antenna. Then, we propose multilayer multifunctional transmitted MSs to simultaneously enhance the gain and transform the linear polarization to circular polarization of the patch antenna. This kind of high-gain antenna eliminates the feed-block effect of the reflected ones

EnHMM: On the Use of Ensemble HMMs and Stack Traces to Predict the Reassignment of Bug Report Fields

Bug reports (BR) contain vital information that can help triaging teams prioritize and assign bugs to developers who will provide the fixes. However, studies have shown that BR fields often contain incorrect information that need to be reassigned, which delays the bug fixing process. There exist approaches for predicting whether a BR field should be reassigned or not. These studies use mainly BR descriptions and traditional machine learning algorithms (SVM, KNN, etc.). As such, they do not fully benefit from the sequential order of information in BR data, such as function call sequences in BR stack traces, which may be valuable for improving the prediction accuracy. In this paper, we propose a novel approach, called EnHMM, for predicting the reassignment of BR fields using ensemble Hidden Markov Models (HMMs), trained on stack traces. EnHMM leverages the natural ability of HMMs to represent sequential data to model the temporal order of function calls in BR stack traces. When applied to Eclipse and Gnome BR repositories, EnHMM achieves an average precision, recall, and F-measure of 54%, 76%, and 60% on Eclipse dataset and 41%, 69%, and 51% on Gnome dataset. We also found that EnHMM improves over the best single HMM by 36% for Eclipse and 76% for Gnome. Finally, when comparing EnHMM to Im.ML.KNN, a recent approach in the field, we found that the average F-measure score of EnHMM improves the average F-measure of Im.ML.KNN by 6.80% and improves the average recall of Im.ML.KNN by 36.09%. However, the average precision of EnHMM is lower than that of Im.ML.KNN (53.93% as opposed to 56.71%).Comment: Published in Proceedings of the 28th IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER 2021), 11 pages, 7 figure