Software Engineering Commons^™

Can Earables Support Effective User Engagement During Weight-Based Gym Exercises?, Meeralakshmi Radhakrishnan, Archan Misra

Research Collection School Of Computing and Information Systems

We explore the use of personal ‘earable’ devices (widely used by gym-goers) in providing personalized, quantified insights and feedback to users performing gym exercises. As in-ear sensing by itself is often too weak to pick up exercise-driven motion dynamics, we propose a novel, low-cost system that can monitor multiple concurrent users by fusing data from (a) wireless earphones, equipped with inertial and physiological sensors and (b) inertial sensors attached to exercise equipment. We share preliminary findings from a small-scale study to demonstrate the promise of this approach, as well as identify open challenges.

Enhancing Python Compiler Error Messages Via Stack Overflow, Emillie Thiselton, Christoph Treude

Research Collection School Of Computing and Information Systems

Background: Compilers tend to produce cryptic and uninformative error messages, leaving programmers confused and requiring them to spend precious time to resolve the underlying error. To find help, programmers often take to online question-and-answer forums such as Stack Overflow to start discussion threads about the errors they encountered.Aims: We conjecture that information from Stack Overflow threads which discuss compiler errors can be automatically collected and repackaged to provide programmers with enhanced compiler error messages, thus saving programmers' time and energy.Method: We present Pycee, a plugin integrated with the popular Sublime Text IDE to provide enhanced compiler error messages for the …

Going Big: A Large-Scale Study On What Big Data Developers Ask, Mehdi Bagherzadeh, Raffi Khatchadourian

Publications and Research

Software developers are increasingly required to write big data code. However, they find big data software development challenging. To help these developers it is necessary to understand big data topics that they are interested in and the difficulty of finding answers for questions in these topics. In this work, we conduct a large-scale study on Stackoverflow to understand the interest and difficulties of big data developers. To conduct the study, we develop a set of big data tags to extract big data posts from Stackoverflow; use topic modeling to group these posts into big data topics; group similar topics into …

Texture-Based Deep Neural Network For Histopathology Cancer Whole Slide Image (Wsi) Classification, Nelson Zange Tsaku

Master of Science in Computer Science Theses

Automatic histopathological Whole Slide Image (WSI) analysis for cancer classification has been highlighted along with the advancements in microscopic imaging techniques. However, manual examination and diagnosis with WSIs is time-consuming and tiresome. Recently, deep convolutional neural networks have succeeded in histopathological image analysis. In this paper, we propose a novel cancer texture-based deep neural network (CAT-Net) that learns scalable texture features from histopathological WSIs. The innovation of CAT-Net is twofold: (1) capturing invariant spatial patterns by dilated convolutional layers and (2) Reducing model complexity while improving performance. Moreover, CAT-Net can provide discriminative texture patterns formed on cancerous regions of histopathological …

A Multimodal Approach To Sarcasm Detection On Social Media, Dipto Das

Graduate Theses/Dissertations

In recent times, a major share of human communication takes place online. The main reason being the ease of communication on social networking sites (SNSs). Due to the variety and large number of users, SNSs have drawn the attention of the computer science (CS) community, particularly the affective computing (also known as emotional AI), information retrieval, natural language processing, and data mining groups. Researchers are trying to make computers understand the nuances of human communication including sentiment and sarcasm. Emotion or sentiment detection requires more insights about the communication than it does for factual information retrieval. Sarcasm detection is particularly …

Formally Designing And Implementing Cyber Security Mechanisms In Industrial Control Networks., Mehdi Sabraoui

Electronic Theses and Dissertations

This dissertation describes progress in the state-of-the-art for developing and deploying formally verified cyber security devices in industrial control networks. It begins by detailing the unique struggles that are faced in industrial control networks and why concepts and technologies developed for securing traditional networks might not be appropriate. It uses these unique struggles and examples of contemporary cyber-attacks targeting control systems to argue that progress in securing control systems is best met with formal verification of systems, their specifications, and their security properties. This dissertation then presents a development process and identifies two technologies, TLA+ and seL4, that can be …

Blocks' Network: Redesign Architecture Based On Blockchain Technology, Moataz Hanif

Doctoral Dissertations and Master's Theses

The Internet is a global network that uses communication protocols. It is considered the most important system reached by humanity, which no one can abandon. However, this technology has become a weapon that threatens the privacy of users, especially in the client-server model, where data is stored and managed privately. Additionally, users have no power over their data that store in a private server, which means users’ data may interrupt by government or might be sold via service provider for-profit purposes. Furthermore, blockchain is a technology that we can rely on to solve issues related to client-server model if appropriately …

Suitability Of Finite State Automata To Model String Constraints In Probablistic Symbolic Execution, Andrew Harris

Boise State University Theses and Dissertations

Probabilistic Symbolic Execution (PSE) extends Symbolic Execution (SE), a path-sensitive static program analysis technique, by calculating the probabilities with which program paths are executed. PSE relies on the ability of the underlying symbolic models to accurately represent the execution paths of the program as the collection of input values following these paths. While researchers established PSE for numerical data types, PSE for complex data types such as strings is a novel area of research.

For string data types SE tools commonly utilize finite state automata to represent a symbolic string model. Thus, PSE inherits from SE automata-based symbolic string models …

Deviant: A Mutation Testing Tool For Solidity Smart Contracts, Patrick Chapman

Boise State University Theses and Dissertations

Blockchain in recent years has exploded in popularity with Ethereum being one of the leading blockchain platforms. Solidity is a widely used scripting language for creating smart contracts in Ethereum applications. Quality assurance in Solidity contracts is of critical importance because bugs or vulnerabilities can lead to a considerable loss of financial assets. However, it is unclear what level of quality assurance is provided in many of these applications.

Mutation testing is the process of intentionally injecting faults into a target program and then running the provided test suite against the various injected faults. Mutation testing is used to evaluate …

A Survey On Bluetooth 5.0 And Mesh: New Milestones Of Iot, Juenjie Yin, Zheng Yang, Hao Cao, Tongtong Liu, Zimu Zhou, Chenshu Wu

Research Collection School Of Computing and Information Systems

No abstract provided.

Enhancing Multi-Hop Sensor Calibration With Uncertainty Estimates, Balz Maag, Zimu Zhou, Lothar Thiele

Research Collection School Of Computing and Information Systems

Low-cost sensors, installed on mobile vehicles, provide a cost-effective way for fine-grained urban air pollution monitoring. However, frequent calibration is crucial for lowcost sensors to consistently deliver accurate measurements. Multi-hop calibration is a common practice to calibrate mobile sensor deployments, but is prone to severe error accumulation over hops. Prior research mitigates error accumulation by designing special calibration models, which only apply to linear models. In this paper, we propose an orthogonal approach by selecting reliable measurements for calibration at each hop. We analyze the impact of different data-induced uncertainties on calibration errors and devise a scheme to estimate these …

Let Me In: Guidelines For The Successful Onboarding Of Newcomers To Open Source Projects, Igor Steinmacher, Christoph Treude, Marco Aurélio Gerosa

Research Collection School Of Computing and Information Systems

Many community-based open source software (OSS) projects depend on a continuous influx of newcomers for their survival and continuity, yet newcomers face many barriers to contributing to a project. We provide guidelines based on our previous work for both OSS communities and newcomers to OSS projects.

Locating Vulnerabilities In Binaries Via Memory Layout Recovering, Haijun Wang, Xiaofei Xie, Shang-Wei Lin, Yun Lin, Yuekang Li, Shengchao Qin, Yang Liu, Ting Liu

Research Collection School Of Computing and Information Systems

Locating vulnerabilities is an important task for security auditing, exploit writing, and code hardening. However, it is challenging to locate vulnerabilities in binary code, because most program semantics (e.g., boundaries of an array) is missing after compilation. Without program semantics, it is difficult to determine whether a memory access exceeds its valid boundaries in binary code. In this work, we propose an approach to locate vulnerabilities based on memory layout recovery. First, we collect a set of passed executions and one failed execution. Then, for passed and failed executions, we restore their program semantics by recovering fine-grained memory layouts based …

Cerebro: Context-Aware Adaptive Fuzzing For Effective Vulnerability Detection, Yuekang Li, Yinxing Xue, Hongxu Chen, Xiuheng Wu, Cen Zhang, Xiaofei Xie, Haijun Wang, Yang Liu

Research Collection School Of Computing and Information Systems

Existing greybox fuzzers mainly utilize program coverage as the goal to guide the fuzzing process. To maximize their outputs, coverage-based greybox fuzzers need to evaluate the quality of seeds properly, which involves making two decisions: 1) which is the most promising seed to fuzz next (seed prioritization), and 2) how many efforts should be made to the current seed (power scheduling). In this paper, we present our fuzzer, Cerebro, to address the above challenges. For the seed prioritization problem, we propose an online multi-objective based algorithm to balance various metrics such as code complexity, coverage, execution time, etc. To address …

Diffchaser: Detecting Disagreements For Deep Neural Networks, Xiaofei Xie, Lei Ma, Haijun Wang, Yuekang Li, Yang Liu, Xiaohong Li

Research Collection School Of Computing and Information Systems

The platform migration and customization have become an indispensable process of deep neural network (DNN) development lifecycle. A highprecision but complex DNN trained in the cloud on massive data and powerful GPUs often goes through an optimization phase (e.g., quantization, compression) before deployment to a target device (e.g., mobile device). A test set that effectively uncovers the disagreements of a DNN and its optimized variant provides certain feedback to debug and further enhance the optimization procedure. However, the minor inconsistency between a DNN and its optimized version is often hard to detect and easily bypasses the original test set. This …

Who Should Make Decision On This Pull Request? Analyzing Time-Decaying Relationships And File Similarities For Integrator Prediction, Jing Jiang, David Lo, Jiateng Zheng, Xin Xia, Yun Yang, Li Zhang

Research Collection School Of Computing and Information Systems

In pull-based development model, integrators are responsible for making decisions about whether to accept pull requests andintegrate code contributions. Ideally, pull requests are assigned to integrators and evaluated within a short time after their submissions. However, the volume of incoming pull requests is large in popular projects, and integrators often encounter difficulties inprocessing pull requests in a timely fashion. Therefore, an automatic integrator prediction approach is required to assign appropriate pull requests to integrators. In this paper, we propose an approach TRFPre which analyzes Time-decaying Relationships andFile similarities to predict integrators. We evaluate the effectiveness of TRFPre on 24 projects …

How Does Machine Learning Change Software Development Practices?, Zhiyuan Wan, Xin Xia, David Lo, Gail C. Murphy

Research Collection School Of Computing and Information Systems

Adding an ability for a system to learn inherently adds uncertainty into the system. Given the rising popularity of incorporating machine learning into systems, we wondered how the addition alters software development practices. We performed a mixture of qualitative and quantitative studies with 14 interviewees and 342 survey respondents from 26 countries across four continents to elicit significant differences between the development of machine learning systems and the development of non-machine-learning systems. Our study uncovers significant differences in various aspects of software engineering (e.g., requirements, design, testing, and process) and work characteristics (e.g., skill variety, problem solving and task identity). …

Multiagent Decision Making And Learning In Urban Environments, Akshat Kumar

Research Collection School Of Computing and Information Systems

Our increasingly interconnected urban environments provide several opportunities to deploy intelligent agents—from self-driving cars, ships to aerial drones—that promise to radically improve productivity and safety. Achieving coordination among agents in such urban settings presents several algorithmic challenges—ability to scale to thousands of agents, addressing uncertainty, and partial observability in the environment. In addition, accurate domain models need to be learned from data that is often noisy and available only at an aggregate level. In this paper, I will overview some of our recent contributions towards developing planning and reinforcement learning strategies to address several such challenges present in largescale urban …

Industry 4.0: Ethical And Moral Predicaments, W. Wang, Keng Siau

Research Collection School Of Computing and Information Systems

The advancements in software technology and data science are enabling Industry 4.0, aka the Fourth Industrial Revolution or the Industrial Internet of Things (IIoT). While the first three industrial revolutions have brought about immense change, the impact of Industry 4.0 will be much wider and far greater, especially with regard to the easily overlooked ethical and moral aspects. Widening wealth gaps between countries and among classes of people within countries, a potential growing unemployment rate, data privacy and accessibility issues, and the treatment of intelligent agents (e.g., military robots) present new and complex ethical and moral dilemmas. In this article, …

Deepstellar: Model-Based Quantitative Analysis Of Stateful Deep Learning Systems, Xiaoning Du, Xiaofei Xie, Yi Li, Lei Ma, Yang Liu, Jianjun Zhao

Research Collection School Of Computing and Information Systems

Deep Learning (DL) has achieved tremendous success in many cutting-edge applications. However, the state-of-the-art DL systems still suffer from quality issues. While some recent progress has been made on the analysis of feed-forward DL systems, little study has been done on the Recurrent Neural Network (RNN)-based stateful DL systems, which are widely used in audio, natural languages and video processing, etc. In this paper, we initiate the very first step towards the quantitative analysis of RNN-based DL systems. We model RNN as an abstract state transition system to characterize its internal behaviors. Based on the abstract model, we design two …