Computer Engineering Commons^™

ตัวสร้างสตับและไดร์เวอร์สำหรับการทดสอบรวมของคลาสจากแผนภาพลำดับและแผนภาพคลาส, พีรวุฒิ เหลืองเรืองโรจน์

Chulalongkorn University Theses and Dissertations (Chula ETD)

ในการพัฒนาซอฟต์แวร์ นักเขียนโปรแกรมแต่ละคนอาจจะเขียนโปรแกรมแต่ละส่วนเสร็จไม่พร้อมกัน หากต้องรอนักเขียนโปรแกรมทุกคนเขียนโปรแกรมเสร็จสิ้นจึงเริ่มการทดสอบจะเป็นการเสียเวลา การทดสอบซอฟต์แวร์จึงเริ่มทันทีเมื่อรหัสต้นฉบับบางส่วนพัฒนาเสร็จสิ้น ซึ่งสตับและไดร์เวอร์จะถูกนำมาใช้แทนมอดูลที่ยังพัฒนาไม่เสร็จ อย่างไรก็ตามสตับและไดร์เวอร์เป็นรหัสต้นฉบับเสียเปล่าที่ถูกสร้างเพื่อใช้เพียงครั้งเดียวและไม่สามารถนำมาใช้กับโครงการอื่น ๆ ได้ การสร้างสตับและไดร์เวอร์จึงควรใช้ความพยายามในการพัฒนาให้น้อยที่สุด งานวิจัยก่อนหน้าได้นำเสนอตัวสร้างสตับและไดร์เวอร์โดยใช้ข้อมูลจากแผนภาพลำดับและแผนภาพคลาส ซึ่งมีข้อจำกัดด้านการสร้างสตับและไดร์เวอร์สำหรับคลาสนามธรรม คลาสภายใน และอินเตอร์เฟส รวมทั้งสามารถเลือกคลาสภายใต้การทดสอบได้เพียงคลาสเดียวและไม่สามารถสร้างกรณีทดสอบได้ โดยงานวิจัยนี้จะพัฒนาตัวสร้าง สตับและไดร์เวอร์ที่แก้ไขข้อจำกัดของงานวิจัยดังกล่าว ผู้ทดสอบสามารถสร้างสตับและไดร์เวอร์สำหรับการทดสอบรวมของคลาสโดยนำเข้าแผนภาพลำดับและแผนภาพคลาสในรูปเอกซ์เอ็มแอล จากนั้นตัวสร้างจะประมวลผลแผนภาพและสร้างกราฟการเรียกใช้งานขึ้นมา ผู้ทดสอบสามารถเลือกกคลาสภายใต้การทดสอบได้ตั้งแต่หนึ่งคลาสขึ้นไป และตัวสร้างจะรวบรวมสตับและไดร์เวอร์ที่ต้องใช้ทดสอบคลาสที่เลือกและสร้างรหัสต้นฉบับของสตับและไดร์เวอร์ขึ้นมาเพื่อทดสอบคลาสเหล่านั้น นอกจากนี้ตัวสร้างจะสุ่มค่าของข้อมูลทดสอบได้แก่ข้อมูลนำเข้าและผลลัพธ์ที่คาดหวังลงไปในไดร์เวอร์ จากนั้นตัวสร้างจะส่งออกไฟล์รหัสต้นฉบับเพื่อการทดสอบรวมคลาสดังกล่าว ซึ่งสตับและไดร์เวอร์ที่สร้างขึ้นมาจะนำไปทดสอบรวมกับกรณีศึกษา 3 กรณี ซึ่งพบว่าสตับและไดร์เวอร์ที่ถูกสร้างขึ้นสามารถแทนที่คลาสที่ยังพัฒนาไม่เสร็จได้

การแปลงกระแสงานยอร์ลที่มีเงื่อนไขเวลาไปเป็นสโตแคสติกเพทริเน็ตส์ทั่วไป, สุพัตรา บุญญะวัตร

Chulalongkorn University Theses and Dissertations (Chula ETD)

ในปัจจุบันโลกทางธุรกิจมีการแข่งขันกันอยู่ตลอดเวลา ผู้ประกอบการทางธุรกิจจึงจำเป็นต้องมีการวางแผนและการบริหารจัดการเวลา ทรัพยากร รวมไปถึงค่าใช้จ่ายต่าง ๆ ให้เป็นไปตามแผนที่วางไว้ ในปัจจุบันมีเครื่องมือที่ช่วยในการจำลองกระบวนการทางธุรกิจ เช่น แผนภาพบีพีเอ็มเอ็น บีเพล และกระแสงานยอว์ล เป็นต้น แต่เครื่องมือดังกล่าวยังไม่สามารถระบุเวลาเฉลี่ยของแต่ละงานในการพัฒนาระบบได้ งานวิทยานิพนธ์นี้ได้เสนอการแปลงกระแสงานยอว์ลไปเป็นสโตแคสติกเพทริเน็ตแบบทั่วไป ซึ่งในงานวิทยานิพนธ์นี้เลือกใช้กระแสงานยอว์ลมาใช้ในการสร้างแบบจำลองกระบวนการทางธุรกิจเพราะกระแสงานยอว์ลมีเครื่องมือ YAWL Editor ที่มีฟีเจอร์ในการตรวจสอบคุณสมบัติของแบบจำลองที่สร้างขึ้น เช่น ตรวจสอบสภาวะติดตายของระบบ เป็นต้น ถึงแม้ว่ากระแสงานยอว์ลจะมีฟีเจอร์ที่ดีดังที่ได้กล่าวไว้ข้างต้น แต่กระแสงานยอว์ลไม่สามารถวิเคราะห์ข้อจำกัดด้านเวลาได้ ดังนั้นในงานวิทยานิพนธ์นี้จึงได้เสนอการแปลงกระแสงานยอว์ลที่มีเงื่อนไขของเวลาไปเป็นสโตแคสติกเพทริเน็ตส์ทั่วไป โดยผู้วิจัยจะเพิ่มค่าเฉลี่ยของเวลาเข้าไปในสัญลักษณ์ของกระแสงานยอว์ลมาตรฐานหลังจากที่แปลงเป็นสโตแคสติกเพทริเน็ตส์ทั่วไปแล้ว โดยใช้การวิเคราะห์ความน่าจะเป็นของเวลาในรูปแบบความน่าจะเป็นที่มีการแจกแจงแบบเอ็กโพเนนเชียลในการสาธิตการใช้งานโมเดล

เครื่องมือวิเคราะห์ผลกระทบต่อซอร์สโค้ดไฮเบอร์เนตและกรณีทดสอบสำหรับการเปลี่ยนแปลงสคีมาฐานข้อมูล, อมรรัตน์ ใจมูล

Chulalongkorn University Theses and Dissertations (Chula ETD)

การออกแบบฐานข้อมูลเป็นส่วนสำคัญของการพัฒนาซอฟต์แวร์ เพราะมีการจัดการความสอดคล้องของข้อมูลและทำให้มั่นใจได้ว่าข้อมูลไม่ได้อยู่ในสภาพที่ไม่ถูกต้อง โดยในแต่ละรอบของการพัฒนาซอฟต์แวร์จะมีเปอร์เซ็นต์ของความซับซ้อนและขนาดของฐานข้อมูลที่มีแนวโน้มเพิ่มขึ้นอย่างมาก ซึ่งการเปลี่ยนแปลงสคีมาฐานข้อมูลจะส่งผลกระทบต่อซอร์สโค้ดที่เกี่ยวข้องรวมไปถึงกรณีทดสอบที่มักจะนำไปสู่ความล้มเหลวของกระบวนการซอฟต์แวร์ได้ นักวิจัยบางคนวิเคราะห์ผลกระทบจากการเปลี่ยนแปลงโดยใช้เทคนิคต่าง ๆ เช่น การจัดการตัวกำหนดค่า การตรวจสอบความสัมพันธ์ในรูปแบบย้อนกลับ และการแบ่งส่วนของโปรแกรม เป็นการวิเคราะห์ผลกระทบเพื่อระบุปัจจัยที่ทำให้เกิดการเปลี่ยนแปลงขึ้น แต่อย่างไรก็ตามการศึกษาเหล่านั้นไม่ได้เน้นถึงผลกระทบที่มีต่อซอร์สโค้ดและกรณีทดสอบ ดังนั้นงานวิจัยนี้นำเสนอเครื่องมือที่ใช้ในการวิเคราะห์ผลกระทบต่อซอร์สโค้ดไฮเบอร์เนตและกรณีทดสอบสำหรับการเปลี่ยนแปลงสคีมาฐานข้อมูล นอกจากนี้เครื่องมือยังสามารถแสดงให้ผู้ใช้ได้ทราบถึงตำแหน่งของผลกระทบที่เกิดขึ้นในซอร์สโค้ดและกรณีทดสอบรวมไปถึงตำแหน่งของซอร์สโค้ดและกรณีทดสอบที่ได้รับการปรับปรุงแก้ไขบนซอร์สโค้ดและการณีทดสอบที่ได้รับผลกระทบอีกด้วย สุดท้ายนี้เครื่องมือที่สร้างขึ้นจะถูกนำไปทดสอบกับกรณีศึกษา 3 กรณี ซึ่งพบว่าซอร์สโค้ดและกรณีทดสอบที่ได้รับผลกระทบจากการเปลี่ยนแปลงสคีมาฐานข้อมูลสามารถนำกลับไปใช้งานต่อได้อย่างสมบูรณ์เนื่องจากได้รับการปรับปรุงแก้ไขจากเครื่องมือที่สร้างขึ้น

Security Bug Report Classification Using Feature Selection, Clustering, And Deep Learning, Tanner D. Gantzer

Graduate Theses, Dissertations, and Problem Reports (ETD)

As the numbers of software vulnerabilities and cybersecurity threats increase, it is becoming more difficult and time consuming to classify bug reports manually. This thesis is focused on exploring techniques that have potential to improve the performance of automated classification of software bug reports as security or non-security related. Using supervised learning, feature selection was used to engineer new feature vectors to be used in machine learning. Feature selection changes the vocabulary used by selecting words with the greatest impact on classification. Feature selection was able to increase the F-Score across the datasets by increasing the precision. We also explored …

Volumetric Error Compensation For Industrial Robots And Machine Tools, Le Ma

Doctoral Dissertations

“A more efficient and increasingly popular volumetric error compensation method for machine tools is to compute compensation tables in axis space with tool tip volumetric measurements. However, machine tools have high-order geometric errors and some workspace is not reachable by measurement devices, the compensation method suffers a curve-fitting challenge, overfitting measurements in measured space and losing accuracy around and out of the measured space. Paper I presents a novel method that aims to uniformly interpolate and extrapolate the compensation tables throughout the entire workspace. By using a uniform constraint to bound the tool tip error slopes, an optimal model with …

An Evaluation Of The Information Security Awareness Of University Students, Alan Pike

Dissertations

Between January 2017 and March 2018, it is estimated that more than 1.9 billion personal and sensitive data records were compromised online. The average cost of a data breach in 2018 was reported to be in the region of US$3.62 million. These figures alone highlight the need for computer users to have a high level of information security awareness (ISA). This research was conducted to establish the ISA of students in a university. There were three aspects to this piece of research. The first was to review and analyse the security habits of students in terms of their own personal …

An Investigation Into The Predictive Capability Of Customer Spending In Modelling Mortgage Default, Donal Finn [Thesis]

Dissertations

The mortgage arrears crisis in Ireland was and is among the most severe experienced on record and although there has been a decreasing trend in the number of mortgages in default in the past four years, it still continues to cause distress to borrowers and vulnerabilities to lenders. There are indications that one of the main factors associated with mortgage default is loan affordability, of which the level of disposable income is a driver. Additionally, guidelines set out by the European Central Bank instructed financial institutions to adopt measures to further reduce and prevent loans defaulting, including the implementation and …

Noise Reduction In Eeg Signals Using Convolutional Autoencoding Techniques, Conor Hanrahan

Dissertations

The presence of noise in electroencephalography (EEG) signals can significantly reduce the accuracy of the analysis of the signal. This study assesses to what extent stacked autoencoders designed using one-dimensional convolutional neural network layers can reduce noise in EEG signals. The EEG signals, obtained from 81 people, were processed by a two-layer one-dimensional convolutional autoencoder (CAE), whom performed 3 independent button pressing tasks. The signal-to-noise ratios (SNRs) of the signals before and after processing were calculated and the distributions of the SNRs were compared. The performance of the model was compared to noise reduction performance of Principal Component Analysis, with …

Effective Plant Discrimination Based On The Combination Of Local Binary Pattern Operators And Multiclass Support Vector Machine Methods, Vi N T Le, Beniamin Apopei, Kamal Alameh

Research outputs 2014 to 2021

Accurate crop and weed discrimination plays a critical role in addressing the challenges of weed management in agriculture. The use of herbicides is currently the most common approach to weed control. However, herbicide resistant plants have long been recognised as a major concern due to the excessive use of herbicides. Effective weed detection techniques can reduce the cost of weed management and improve crop quality and yield. A computationally efficient and robust plant classification algorithm is developed and applied to the classification of three crops: Brassica napus (canola), Zea mays (maize/corn), and radish. The developed algorithm is based on the …

Computational Intelligence In Steganography: Adaptive Image Watermarking, Xin Zhong

Dissertations

Digital image watermarking, as an extension of traditional steganography, refers to the process of hiding certain messages into cover images. The transport image, called marked-image or stego-image, conveys the hidden messages while appears visibly similar to the cover-image. Therefore, image watermarking enables various applications such as copyright protection and covert communication. In a watermarking scheme, fidelity, capacity and robustness are considered as crucial factors, where fidelity measures the similarity between the cover- and marked-images, capacity measures the maximum amount of watermark that can be embedded, and robustness concerns the watermark extraction under attacks on the marked-image. Watermarking techniques are often …

Nanopower Analog Frontends For Cyber-Physical Systems, Kenji Aono

McKelvey School of Engineering Graduate Student Theses & Dissertations

In a world that is increasingly dominated by advances made in digital systems, this work will explore the exploiting of naturally occurring physical phenomena to pave the way towards a self-powered sensor for Cyber-Physical Systems (CPS). In general, a sensor frontend can be broken up into a handful of basic stages: transduction, filtering, energy conversion, measurement, and interfacing. One analog artifact that was investigated for filtering was the physical phenomenon of hysteresis induced in current-mode biquads driven near or at their saturation limit. Known as jump resonance, this analog construct facilitates a higher quality factor to be brought about without …

Open Source Foundations For Spatial Decision Support Systems, Jochen Albrecht

Publications and Research

Spatial Decision Support Systems (SDSS) were a hot topic in the 1990s, when researchers tried to imbue GIS with additional decision support features. Successful practical developments such as HAZUS or CommunityViz have since been built, based on commercial desktop software and without much heed for theory other than what underlies their process models. Others, like UrbanSim, have been completely overhauled twice but without much external scrutiny. Both the practical and the theoretical foundations of decision support systems have developed considerably over the past 20 years. This article presents an overview of these developments and then looks at what corresponding tools …

Gmaim: An Analytical Pipeline For Microrna Splicing Profiling Using Generative Model, Kan Liu

School of Computing: Dissertations, Theses, and Student Research

MicroRNAs (miRNAs) are a class of short (~22 nt) single strand RNA molecules predominantly found in eukaryotes. Being involved in many major biological processes, miRNAs can regulate gene expression by targeting mRNAs to facilitate their degradation or translational inhibition. The imprecise splicing of miRNA splicing which introduces severe variability in terms of sequences of miRNA products and their corresponding downstream gene expression regulation. For example, to study biogenesis of miRNAs, usually, biologists can deplete a gene in the miRNA biogenesis pathway and study the change of miRNA sequences, which can cause impression of miRNAs. Although high-throughput sequencing technologies such as …

Facepet: Enhancing Bystanders' Facial Privacy With Smart Wearables/Internet Of Things, Alfredo J. Perez, Sherali Zeadally, Luis Y. Matos Garcia, Jaouad A. Mouloud, Scott Griffith

Information Science Faculty Publications

Given the availability of cameras in mobile phones, drones and Internet-connected devices, facial privacy has become an area of major interest in the last few years, especially when photos are captured and can be used to identify bystanders’ faces who may have not given consent for these photos to be taken and be identified. Some solutions to protect facial privacy in photos currently exist. However, many of these solutions do not give a choice to bystanders because they rely on algorithms that de-identify photos or protocols to deactivate devices and systems not controlled by bystanders, thereby being dependent on the …

Empathetic Computing For Inclusive Application Design, Kenny Choo Tsu Wei

Dissertations and Theses Collection (Open Access)

The explosive growth of the ecosystem of personal and ambient computing de- vices coupled with the proliferation of high-speed connectivity has enabled ex- tremely powerful and varied mobile computing applications that are used every- where. While such applications have tremendous potential to improve the lives of impaired users, most mobile applications have impoverished designs to be inclusive– lacking support for users with specific disabilities. Mobile app designers today haveinadequate support to design existing classes of apps to support users with specific disabilities, and more so, lack the support to design apps that specifically target these users. One way to resolve …

A Transfer Learning Approach For Sentiment Classification., Omar Abdelwahab

Electronic Theses and Dissertations

The idea of developing machine learning systems or Artificial Intelligence agents that would learn from different tasks and be able to accumulate that knowledge with time so that it functions successfully on a new task that it has not seen before is an idea and a research area that is still being explored. In this work, we will lay out an algorithm that allows a machine learning system or an AI agent to learn from k different domains then uses some or no data from the new task for the system to perform strongly on that new task. In order …

Automatic Performance Optimization On Heterogeneous Computer Systems Using Manycore Coprocessors, Chenggang Lai

Graduate Theses and Dissertations

Emerging computer architectures and advanced computing technologies, such as Intel’s Many Integrated Core (MIC) Architecture and graphics processing units (GPU), provide a promising solution to employ parallelism for achieving high performance, scalability and low power consumption. As a result, accelerators have become a crucial part in developing supercomputers. Accelerators usually equip with different types of cores and memory. It will compel application developers to reach challenging performance goals. The added complexity has led to the development of task-based runtime systems, which allow complex computations to be expressed as task graphs, and rely on scheduling algorithms to perform load balancing between …

Scale-Out Algorithm For Apache Storm In Saas Environment, Ravi Kiran Puttaswamy

School of Computing: Dissertations, Theses, and Student Research

The main appeal of the Cloud is in its cost effective and flexible access to computing power. Apache Storm is a data processing framework used to process streaming data. In our work we explore the possibility of offering Apache Storm as a software service. Further, we take advantage of the cgroups feature in Storm to divide the computing power of worker machine into smaller units to be offered to users. We predict that the compute bounds placed on the cgroups could be used to approximate the state of the workflow. We discuss the limitations of the current schedulers in facilitating …

Reducing The Tail Latency Of A Distributed Nosql Database, Jun Wu

School of Computing: Dissertations, Theses, and Student Research

The request latency is an important performance metric of a distributed database, such as the popular Apache Cassandra, because of its direct impact on the user experience. Specifically, the latency of a read or write request is defined as the total time interval from the instant when a user makes the request to the instant when the user receives the request, and it involves not only the actual read or write time at a specific database node, but also various types of latency introduced by the distributed mechanism of the database. Most of the current work focuses only on reducing …

Collaborative Robotic Path Planning For Industrial Spraying Operations On Complex Geometries, Steven Brown

Graduate Theses and Dissertations

Implementation of automated robotic solutions for complex tasks currently faces a few major hurdles. For instance, lack of effective sensing and task variability – especially in high-mix/low-volume processes – creates too much uncertainty to reliably hard-code a robotic work cell. Current collaborative frameworks generally focus on integrating the sensing required for a physically collaborative implementation. While this paradigm has proven effective for mitigating uncertainty by mixing human cognitive function and fine motor skills with robotic strength and repeatability, there are many instances where physical interaction is impractical but human reasoning and task knowledge is still needed. The proposed framework consists …

Sensor-Based Human Activity Recognition Using Bidirectional Lstm For Closely Related Activities, Arumugam Thendramil Pavai

Electronic Theses, Projects, and Dissertations

Recognizing human activities using deep learning methods has significance in many fields such as sports, motion tracking, surveillance, healthcare and robotics. Inertial sensors comprising of accelerometers and gyroscopes are commonly used for sensor based HAR. In this study, a Bidirectional Long Short-Term Memory (BLSTM) approach is explored for human activity recognition and classification for closely related activities on a body worn inertial sensor data that is provided by the UTD-MHAD dataset. The BLSTM model of this study could achieve an overall accuracy of 98.05% for 15 different activities and 90.87% for 27 different activities performed by 8 persons with 4 …

Feature-Based Transfer Learning In Natural Language Processing, Jianfei Yu

Dissertations and Theses Collection (Open Access)

In the past few decades, supervised machine learning approach is one of the most important methodologies in the Natural Language Processing (NLP) community. Although various kinds of supervised learning methods have been proposed to obtain the state-of-the-art performance across most NLP tasks, the bottleneck of them lies in the heavy reliance on the large amount of manually annotated data, which is not always available in our desired target domain/task. To alleviate the data sparsity issue in the target domain/task, an attractive solution is to find sufficient labeled data from a related source domain/task. However, for most NLP applications, due to …

Modeling Movement Decisions In Networks: A Discrete Choice Model Approach, Larry Lin Junjie

Dissertations and Theses Collection (Open Access)

In this dissertation, we address the subject of modeling and simulation of agents and their movement decision in a network environment. We emphasize the development of high quality agent-based simulation models as a prerequisite before utilization of the model as an evaluation tool for various recommender systems and policies. To achieve this, we propose a methodological framework for development of agent-based models, combining approaches such as discrete choice models and data-driven modeling.

The discrete choice model is widely used in the field of transportation, with a distinct utility function (e.g., demand or revenue-driven). Through discrete choice models, the movement decision …

Automatically `Verifying’ Discrete-Time Complex Systems Through Learning, Abstraction And Refinement, Jingyi Wang, Jun Sun, Shengchao Qin, Cyrille Jegourel

Research Collection School Of Computing and Information Systems

Precisely modeling complex systems like cyber-physical systems is challenging, which often render model-based system verification techniques like model checking infeasible. To overcome this challenge, we propose a method called LAR to automatically ‘verify’ such complex systems through a combination of learning, abstraction and refinement from a set of system log traces. We assume that log traces and sampling frequency are adequate to capture ‘enough’ behaviour of the system. Given a safety property and the concrete system log traces as input, LAR automatically learns and refines system models, and produces two kinds of outputs. One is a counterexample with a bounded …

Co-Location Resistant Virtual Machine Placement In Cloud Data Centers, Amit Agarwal, Nguyen Binh Duong Ta

Research Collection School Of Computing and Information Systems

Due to increasing number of avenues for conducting cross-virtual machine (VM) side-channel attacks, the security of public IaaS cloud data centers is a growing concern. These attacks allow an adversary to steal private information from a target user whose VM instance is co-located with that of the adversary. To reduce the probability of malicious co-location, we propose a novel VM placement algorithm called “Previously Co-Located Users First”. We perform a theoretical and empirical analysis of our proposed algorithm to evaluate its resource efficiency and security. Our results, obtained using real-world cloud traces containing millions of VM requests and thousands of …

Early Prediction Of Merged Code Changes To Prioritize Reviewing Tasks, Yuanrui Fan, Xin Xia, David Lo, Shanping Li

Research Collection School Of Computing and Information Systems

Modern Code Review (MCR) has been widely used by open source and proprietary software projects. Inspecting code changes consumes reviewers much time and effort since they need to comprehend patches, and many reviewers are often assigned to review many code changes. Note that a code change might be eventually abandoned, which causes waste of time and effort. Thus, a tool that predicts early on whether a code change will be merged can help developers prioritize changes to inspect, accomplish more things given tight schedule, and not waste reviewing effort on low quality changes. In this paper, motivated by the above …

Deep Unsupervised Pixelization, Chu Han, Qiang Wen, Shengfeng He, Qianshu Zhu, Yinjie Tan, Guoqiang Han, Tien-Tsin Wong

Research Collection School Of Computing and Information Systems

In this paper, we present a novel unsupervised learning method for pixelization. Due to the difficulty in creating pixel art, preparing the paired training data for supervised learning is impractical. Instead, we propose an unsupervised learning framework to circumvent such difficulty. We leverage the dual nature of the pixelization and depixelization, and model these two tasks in the same network in a bi-directional manner with the input itself as training supervision. These two tasks are modeled as a cascaded network which consists of three stages for different purposes. GridNet transfers the input image into multi-scale grid-structured images with different aliasing …

Deploying, Improving And Evaluating Edge Bundling Methods For Visualizing Large Graphs, Jieting Wu

School of Computing: Dissertations, Theses, and Student Research

A tremendous increase in the scale of graphs has been witnessed in a wide range of fields, which demands efficient and effective visualization techniques to assist users in better understandings of large graphs. Conventional node-link diagrams are often used to visualize graphs, whereas excessive edge crossings can easily incur severe visual clutter in the node-link diagram of a large graph. Edge bundling can effectively remedy visual clutter and reveal high-level graph structures. Although significant efforts have been devoted to developing edge bundling, three challenging problems remain. First, edge bundling techniques are often computationally expensive and are not easy to deploy …

Evoalloy: An Evolutionary Approach For Analyzing Alloy Specifications, Jianghao Wang

School of Computing: Dissertations, Theses, and Student Research

Using mathematical notations and logical reasoning, formal methods precisely define a program’s specifications, from which we can instantiate valid instances of a system. With these techniques, we can perform a variety of analysis tasks to verify system dependability and rigorously prove the correctness of system properties. While there exist well-designed automated verification tools including ones considered lightweight, they still lack a strong adoption in practice. The essence of the problem is that when applied to large real world applications, they are not scalable and applicable due to the expense of thorough verification process. In this thesis, I present a new …

Controller Evolution And Divergence: A Software Perspective, Balaji Balasubramaniam

School of Computing: Dissertations, Theses, and Student Research

Successful controllers evolve as they are refined, extended, and adapted to new systems and contexts. This evolution occurs in the controller design and also in its software implementation. Model-based design and controller synthesis can help to synchronize this evolution of design and software, but such synchronization is rarely complete as software tends to also evolve in response to elements rarely present in a control model, leading to mismatches between the control design and the software.

In this thesis, we perform a first-of-its-kind study on the evolution of two popular open-source safety-critical autopilot control software -- ArduPilot, and Paparazzi, to better …