Software Engineering Commons^™

Teachers’ Engagement And Self-Efficacy In A Pk–12 Computer Science Teacher Virtual Community Of Practice, Robert Schwarzhaupt, Feng Liu, Joseph Wilson, Fanny Lee, Melissa Rasberry

Journal of Computer Science Integration

Prekindergarten to 12th-grade teachers of computer science (CS) face many challenges, including isolation, limited CS professional development resources, and low levels of CS teaching self-efficacy that could be mitigated through communities of practice (CoPs). This study used survey data from 420 PK–12 CS teacher members of a virtual CoP, CS for All Teachers, to examine the needs of these teachers and how CS teaching self-efficacy, community engagement, and sharing behaviors vary by teachers’ instructional experiences and school levels taught. Results show that CS teachers primarily join the CoP to gain high-quality pedagogical, assessment, and instructional resources. The study also found …

International Comparative Studies On The Software Testing Profession, Luiz Fernando Capretz, Pradeep Waychal, Jingdong Jia, Daniel Varona, Yadira Lizama

Electrical and Computer Engineering Publications

This work attempts to fill a gap by exploring the human dimension in particular, by trying to understand the motivation of software professionals for taking up and sustaining their careers as software testers. Towards that goal, four surveys were conducted in four countries—India, Canada, Cuba, and China—to try to understand how professional software engineers perceive and value work-related factors that could influence their motivation to start or move into software testing careers. From our sample of 220 software professionals, we observed that very few were keen to take up testing careers. Some aspects of software testing, such as the potential …

The Empathetic Car: Exploring Emotion Inference Via Driver Behaviour And Traffic Context, Shu Liu, Kevin Koch, Zimu Zhou, Simon Foll, Xiaoxi He, Tina Menke, Elgar Fleisch, Felix Wortmann

Research Collection School Of Computing and Information Systems

An empathetic car that is capable of reading the driver’s emotions has been envisioned by many car manufacturers. Emotion inference enables in-vehicle applications to improve driver comfort, well-being, and safety. Available emotion inference approaches use physiological, facial, and speech-related data to infer emotions during driving trips. However, existing solutions have two major limitations: Relying on sensors that are not built into the vehicle restricts emotion inference to those people leveraging corresponding devices (e.g., smartwatches). Relying on modalities such as facial expressions and speech raises privacy concerns. By contrast, researchers in mobile health have been able to infer affective states (e.g., …

Characterization And Automatic Updates Of Deprecated Machine-Learning Api Usages, Stefanus Agus Haryono, Thung Ferdian, David Lo, Julia Lawall, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

Due to the rise of AI applications, machine learning (ML) libraries, often written in Python, have become far more accessible. ML libraries tend to be updated periodically, which may deprecate existing APIs, making it necessary for application developers to update their usages. In this paper, we build a tool to automate deprecated API usage updates. We first present an empirical study to better understand how updates of deprecated ML API usages in Python can be done. The study involves a dataset of 112 deprecated APIs from Scikit-Learn, TensorFlow, and PyTorch. Guided by the findings of our empirical study, we propose …

Biasheal: On-The-Fly Black-Box Healing Of Bias In Sentiment Analysis Systems, Zhou Yang, Harshit Jain, Jieke Shi, Muhammad Hilmi Asyrofi, David Lo

Research Collection School Of Computing and Information Systems

Although Sentiment Analysis (SA) is widely applied in many domains, existing research has revealed that the unfairness in SA systems can be harmful to the welfare of less privileged people. Several works propose pre-processing and in-processing methods to eliminate bias in SA systems, but little attention is paid to utilizing post-processing methods to heal bias. Postprocessing methods are particularly important for systems that use third-party SA services. Systems that use such services have no access to the SA engine or its training data and thus cannot apply pre-processing nor in-processing methods. Therefore, this paper proposes a black-box post-processing method to …

Which Variables Should I Log?, Zhongxin Liu, Xin Xia, David Lo, Zhenchang Xing, Ahmed E. Hassan, Shanping Li

Research Collection School Of Computing and Information Systems

Developers usually depend on inserting logging statements into the source code to collect system runtime information. Such logged information is valuable for software maintenance. A logging statement usually prints one or more variables to record vital system status. However, due to the lack of rigorous logging guidance and the requirement of domain-specific knowledge, it is not easy for developers to make proper decisions about which variables to log. To address this need, in this work, we propose an approach to recommend logging variables for developers during development by learning from existing logging statements. Different from other prediction tasks in software …

Injecting Descriptive Meta-Information Into Pre-Trained Language Models With Hypernetworks, Wenying Duan, Xiaoxi He, Zimu Zhou, Hong Rao, Lothar Thiele

Research Collection School Of Computing and Information Systems

Pre-trained language models have been widely adopted as backbones in various natural language processing tasks. However, existing pre-trained language models ignore the descriptive meta-information in the text such as the distinction between the title and the mainbody, leading to over-weighted attention to insignificant text. In this paper, we propose a hypernetwork-based architecture to model the descriptive meta-information and integrate it into pre-trained language models. Evaluations on three natural language processing tasks show that our method notably improves the performance of pre-trained language models and achieves the state-of-the-art results on keyphrase extraction.

Sediqa: Sound Emitting Document Image Quality Assessment In A Reading Aid For The Visually Impaired, Jane Courtney

Articles

For visually impaired people (VIPs), the ability to convert text to sound can mean a new level of independence or the simple joy of a good book. With significant advances in optical character recognition (OCR) in recent years, a number of reading aids are appearing on the market. These reading aids convert images captured by a camera to text which can then be read aloud. However, all of these reading aids suffer from a key issue—the user must be able to visually target the text and capture an image of sufficient quality for the OCR algorithm to function—no small task …

Software For The Frontiers Of Quantum Chemistry: An Overview Of Developments In The Q-Chem 5 Package, Evgeny Epifanovsky, Andrew T.B. Gilbert, Xintian Feng, Joonho Lee, Yuezhi Mao, Narbe Mardirossian, Pavel Pokhilko, Alec F. White, Marc P. Coons, Adiran L. Dempwolff, Zhengting Gan, Diptarka Hait, Paul R. Horn, Leif D. Jacobson, Ilya Kaliman, Jorg Kussmann, Adrian W. Lange, Ka Un Lao, Daniel S. Levine, Jie Liu, Simon C. Mckenzie, Adrian F. Morrison, Kaushik D. Nanda, Felix Plasser, Dirk R. Rehn, Marta L. Vidal, Zhi-Qiang You, Ying Zhu, Bushra Alam, Benjamin J. Albrecht, Abdulrahman Aldossary

Chemistry and Biochemistry Faculty Research

This article summarizes technical advances contained in the fifth major release of the Q-Chem quantum chemistry program package, covering developments since 2015. A comprehensive library of exchange–correlation functionals, along with a suite of correlated many-body methods, continues to be a hallmark of the Q-Chem software. The many-body methods include novel variants of both coupled-cluster and configuration-interaction approaches along with methods based on the algebraic diagrammatic construction and variational reduced density-matrix methods. Methods highlighted in Q-Chem 5 include a suite of tools for modeling core-level spectroscopy, methods for describing metastable resonances, methods for computing vibronic spectra, the nuclear–electronic orbital method, and …

Automated Statistical Structural Testing Techniques And Applications, Yang Shi

Dissertations and Theses

Statistical structural testing(SST) is an effective testing technique that produces random test inputs from probability distributions. SST shows superiority in fault-revealing power over random testing and deterministic approaches since it heritages the merits from both of them. SST ensures testing thoroughness by setting up a probability lower-bound criterion for each structural cover element and test inputs that exercise a structural cover element sampled from the probability distribution, ensuring testing randomness. Despite the advantages, SST is not a widely used approach in practice. There are two major limitations. First, to construct probability distributions, a tester must understand the underlying software's structure, …

Ensemble Data Fitting For Bathymetric Models Informed By Nominal Data, Samantha Zambo

Dissertations

Due to the difficulty and expense of collecting bathymetric data, modeling is the primary tool to produce detailed maps of the ocean floor. Current modeling practices typically utilize only one interpolator; the industry standard is splines-in-tension.

In this dissertation we introduce a new nominal-informed ensemble interpolator designed to improve modeling accuracy in regions of sparse data. The method is guided by a priori domain knowledge provided by artificially intelligent classifiers. We recast such geomorphological classifications, such as ‘seamount’ or ‘ridge’, as nominal data which we utilize as foundational shapes in an expanded ordinary least squares regression-based algorithm. To our knowledge …

Transitioning From Vue 2 To Vue 3, Adele Kanley

Theses/Capstones/Creative Projects

Frontend development is a field that is constantly changing because of the vast amounts of tools that are made available each year. One of the most popular frameworks being utilized to create fluid user experience is the Vue framework. Branching from the well-known Angular.js, Vue.js is an independent open-source project that is making its mark in the user interface community.

Regardless of the popularity of a framework, updates are inevitable to keep up with the innovations required by the IT Field. To ensure that UNO IS&T students are being offered opportunities to learn and develop in the most update to …

W8-Scope: Fine-Grained, Practical Monitoring Of Weight Stack-Based Exercises, Meera Radhakrishnan, Archan Misra, Rajesh K. Balan

Research Collection School Of Computing and Information Systems

Fine-grained, unobtrusive monitoring of gym exercises can help users track their own exercise routines and also provide corrective feedback. We propose W8-Scope, a system that uses a simple magnetic-cum-accelerometer sensor, mounted on the weight stack of gym exercise machines, to infer various attributes of gym exercise behavior. More specifically, using multiple machine learning models, W8-Scope helps identify who is exercising, what exercise she is doing, how much weight she is lifting, and whether she is committing any common mistakes. Real world studies, conducted with 50 subjects performing 14 different exercises over 103 distinct sessions in two gyms, show that W8-Scope …

Desktop Application For The Puzzle Board Game “Rush Hour”, Huanqing Nong

Electronic Theses, Projects, and Dissertations

Rush Hour is a sliding block puzzle board game. This game comes with a board of 6 x 6 grid simulating a parking lot with an exit at the right end of the third row and some vehicle models of size 1 x 2 or 1 x 3 which can slide along the grooves of the grid forward or backward. The goal of the game is to clear the path by moving the vehicles on the board in a certain way for the target car, which lies on the third row of the grid, to merge out the “parking lot” …

Learning To Interpret Knowledge From Software Q&A Sites, Bowen Xu

Dissertations and Theses Collection (Open Access)

Nowadays, software question and answer (SQA) data has become a treasure for software engineering as it contains a huge volume of programming knowledge. That knowledge can be interpreted in many different ways to support various software activities, such as code recommendation, program repair, and so on. In this dissertation, we interpret SQA data by addressing three novel research problems.

The first research problem is about linkable knowledge unit prediction. In this problem, a question and its answers within a post in Stack Overflow are considered as a knowledge unit (KU). KUs often contain semantically relevant knowledge, and thus linkable for …

Maintenance-Related Concerns For Post-Deployed Ethereum Smart Contract Development: Issues, Techniques, And Future Challenges, Jiachi Chen, Xin Xia, David Lo, John Grundy, Xiaohu Yang

Research Collection School Of Computing and Information Systems

Software development is a very broad activity that captures the entire life cycle of a software, which includes designing, programming, maintenance and so on. In this study, we focus on the maintenance-related concerns of the post-deployment of smart contracts. Smart contracts are self-executed programs that run on a blockchain. They cannot be modified once deployed and hence they bring unique maintenance challenges compared to conventional software. According to the definition of ISO/IEC 14764, there are four kinds of software maintenance, i.e., corrective, adaptive, perfective, and preventive maintenance. This study aims to answer (i) What kinds of issues will smart contract …

Code Integrity Attestation For Plcs Using Black Box Neural Network Predictions, Yuqi Chen, Christopher M. Poskitt, Jun Sun

Research Collection School Of Computing and Information Systems

Cyber-physical systems (CPSs) are widespread in critical domains, and significant damage can be caused if an attacker is able to modify the code of their programmable logic controllers (PLCs). Unfortunately, traditional techniques for attesting code integrity (i.e. verifying that it has not been modified) rely on firmware access or roots-of-trust, neither of which proprietary or legacy PLCs are likely to provide. In this paper, we propose a practical code integrity checking solution based on privacy-preserving black box models that instead attest the input/output behaviour of PLC programs. Using faithful offline copies of the PLC programs, we identify their most important …

Automating The Removal Of Obsolete Todo Comments, Zhipeng Gao, Xin Xia, David Lo, John C. Grundy, Thomas Zimmermann

Research Collection School Of Computing and Information Systems

TODO comments are very widely used by software developers to describe their pending tasks during software development. However, after performing the task developers sometimes neglect or simply forget to remove the TODO comment, resulting in obsolete TODO comments. These obsolete TODO comments can confuse development teams and may cause the introduction of bugs in the future, decreasing the software’s quality and maintainability. Manually identifying obsolete TODO comments is time-consuming and expensive. It is thus necessary to detect obsolete TODO comments and remove them automatically before they cause any unwanted side effects. In this work, we propose a novel model, named …

Pruning-Aware Merging For Efficient Multitask Inference, Dawei Gao, Xiaoxi He, Zimu Zhou, Yongxin Tong, Lothar Thiele

Research Collection School Of Computing and Information Systems

Many mobile applications demand selective execution of multiple correlated deep learning inference tasks on resource-constrained platforms. Given a set of deep neural networks, each pre-trained for a single task, it is desired that executing arbitrary combinations of tasks yields minimal computation cost. Pruning each network separately yields suboptimal computation cost due to task relatedness. A promising remedy is to merge the networks into a multitask network to eliminate redundancy across tasks before network pruning. However, pruning a multitask network combined by existing network merging schemes cannot minimise the computation cost of every task combination because they do not consider such …

Code2que: A Tool For Improving Question Titles From Mined Code Snippets In Stack Overflow, Zhipeng Gao, Xin Xia, David Lo, John C. Grundy, Yuan-Fang Li

Research Collection School Of Computing and Information Systems

Stack Overflow is one of the most popular technical Q&A sites used by software developers. Seeking help from Stack Overflow has become an essential part of software developers' daily work for solving programming-related questions. Although the Stack Overflow community has provided quality assurance guidelines to help users write better questions, we observed that a significant number of questions submitted to Stack Overflow are of low quality. In this paper, we introduce a new web-based tool, Code2Que, which can help developers in writing higher quality questions for a given code snippet. Code2Que consists of two main stages: offline learning and online …

Learning-Based Extraction Of First-Order Logic Representations Of Api Directives, Mingwei Liu, Xin Peng, Andrian Marcus, Christoph Treude, Xuefang Bai, Gang Lyu, Jiazhen Xie, Xiaoxin Zhang

Research Collection School Of Computing and Information Systems

Developers often rely on API documentation to learn API directives, i.e., constraints and guidelines related to API usage. Failing to follow API directives may cause defects or improper implementations. Since there are no industry-wide standards on how to document API directives, they take many forms and are often hard to understand by developers or challenging to parse with tools. In this paper, we propose a learning based approach for extracting first-order logic representations of API directives (FOL directives for short). The approach, called LeadFOL, uses a joint learning method to extract atomic formulas by identifying the predicates and arguments involved …

Deeprepair: Style-Guided Repairing For Deep Neural Networks In The Real-World Operational Environment, Bing Yu, Hua Qi, Guo Qing, Felix Juefei-Xu, Xiaofei Xie, Lei Ma, Jianjun Zhao

Research Collection School Of Computing and Information Systems

Deep neural networks (DNNs) are continuously expanding their application to various domains due to their high performance. Nevertheless, a well-trained DNN after deployment could oftentimes raise errors during practical use in the operational environment due to the mismatching between distributions of the training dataset and the potential unknown noise factors in the operational environment, e.g., weather, blur, noise, etc. Hence, it poses a rather important problem for the DNNs' real-world applications: how to repair the deployed DNNs for correcting the failure samples under the deployed operational environment while not harming their capability of handling normal or clean data with limited …

Independent Reinforcement Learning For Weakly Cooperative Multiagent Traffic Control Problem, Chengwei Zhang, Shan Jin, Wanli Xue, Xiaofei Xie, Shengyong Chen, Rong Chen

Research Collection School Of Computing and Information Systems

The adaptive traffic signal control (ATSC) problem can be modeled as a multiagent cooperative game among urban intersections, where intersections cooperate to counter the city's traffic conditions. Recently, reinforcement learning (RL) has achieved marked successes in managing sequential decision making problems, which motivates us to apply RL in the ATSC problem. One of the largest challenges of this problem is that the observation of intersection is typically partially observable, which limits the learning performance of RL algorithms. Considering the large scale of intersections in an urban traffic environment, we use independent RL to solve ATSC problem in this study. We …

An Empirical Study Of Gui Widget Detection For Industrial Mobile Games, Jiaming Ye, Ke Chen, Xiaofei Xie, Lei Ma, Ruochen Huang, Yingfeng Chen, Yinxing Xue, Jianjun Zhao

Research Collection School Of Computing and Information Systems

With the widespread adoption of smartphones in our daily life, mobile games experienced increasing demand over the past years. Meanwhile, the quality of mobile games has been continuously drawing more and more attention, which can greatly affect the player experience. For better quality assurance, general-purpose testing has been extensively studied for mobile apps. However, due to the unique characteristic of mobile games, existing mobile testing techniques may not be directly suitable and applicable. To better understand the challenges in mobile game testing, in this paper, we first initiate an early step to conduct an empirical study towards understanding the challenges …

Ava: Adversarial Vignetting Attack Against Visual Recognition, Binyu Tian, Felix Juefei-Xu, Qing Guo, Xiaofei Xie, Xiaohong Li, Yang Liu

Research Collection School Of Computing and Information Systems

Vignetting is an inherent imaging phenomenon within almost all optical systems, showing as a radial intensity darkening toward the corners of an image. Since it is a common effect for photography and usually appears as a slight intensity variation, people usually regard it as a part of a photo and would not even want to post-process it. Due to this natural advantage, in this work, we study the vignetting from a new viewpoint, i.e., adversarial vignetting attack (AVA), which aims to embed intentionally misleading information into the vignetting and produce a natural adversarial example without noise patterns. This example can …

Graph-Based Seed Object Synthesis For Search-Based Unit Testing, Yun Lin, You Seng Ong, Jun Sun, Gordon Fraser, Jin Song Dong

Research Collection School Of Computing and Information Systems

Search-based software testing (SBST) generates tests using search algorithms guided by measurements gauging how far a test case is away from exercising a coverage goal. The effectiveness of SBST largely depends on the continuity and monotonicity of the fitness landscape decided by these measurements and the search operators. Unfortunately, the fitness landscape is challenging when the function under test takes object inputs, as classical measurements hardly provide guidance for constructing legitimate object inputs. To overcome this problem, we propose test seeds, i.e., test code skeletons of legitimate objects which enable the use of classical measurements. Given a target branch in …

Type And Interval Aware Array Constraint Solving For Symbolic Execution, Ziqi Shuai, Zhenbang Chen, Yufeng Zhang, Jun Sun, Ji Wang

Research Collection School Of Computing and Information Systems

Array constraints are prevalent in analyzing a program with symbolic execution. Solving array constraints is challenging due to the complexity of the precise encoding for arrays. In this work, we propose to synergize symbolic execution and array constraint solving. Our method addresses the difficulties in solving array constraints with novel ideas. First, we propose a lightweight method for pre-checking the unsatisfiability of array constraints based on integer linear programming. Second, observing that encoding arrays at the byte-level introduces many redundant axioms that reduce the effectiveness of constraint solving, we propose type and interval aware axiom generation. Note that the type …

Biasrv: Uncovering Biased Sentiment Predictions At Runtime, Zhou Yang, Muhammad Hilmi Asyrofi, David Lo

Research Collection School Of Computing and Information Systems

Sentiment analysis (SA) systems, though widely applied in many domains, have been demonstrated to produce biased results. Some research works have been done in automatically generating test cases to reveal unfairness in SA systems, but the community still lacks tools that can monitor and uncover biased predictions at runtime. This paper fills this gap by proposing BiasRV, the first tool to raise an alarm when a deployed SA system makes a biased prediction on a given input text. To implement this feature, BiasRV dynamically extracts a template from an input text and from the template generates gender-discriminatory mutants (semanticallyequivalent texts …

Aware: Eliminating Implicit Bias Using Ar, Shagun Bose, Emma Strauch

Frameless

Implicit Bias is something that happens to real people in real spaces all the time. But we can’t see it. Since, AR allows us to overlay virtual objects in real environments, we tried to leverage AR to make more salient the various ways in which people experience bias.

Knot Theory In Virtual Reality, Donald Lee Price

Masters Theses & Specialist Projects

Throughout the study of Knot Theory, there have been several programmatic solutions to common problems or questions. These solutions have included software to draw knots, software to identify knots, or online databases to look up pre-computed data about knots. We introduce a novel prototype of software used to study knots and links by using Virtual Reality. This software can allow researchers to draw links in 3D, run physics simulations on them, and identify them. This technique has not yet been rigorously explored and we believe it will be of great interest to Knot Theory researchers. The computer code is written …