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2023

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Nebraska Tractor Test: 2262 John Deere 9r 540, Nebraska Tractor Test Lab Jan 2023

Nebraska Tractor Test: 2262 John Deere 9r 540, Nebraska Tractor Test Lab

Nebraska Tractor Tests

ABOUT THE TEST REPORT AND USE OF THE DATA The test data contained in this report are a tabulation of the results of a series of tests. Due to the restricted format of these pages, only a limited amount of data and not all of the tractor specifications are included. The full OECD report contains usually about 30 pages of data and specifications. The test data were obtained for each tractor under similar conditions and therefore, provide a means of comparison of performance based on a limited set of reported data. EXPLANATION OF THE TEST PROCEDURES Purpose The purpose of …

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Nebraska Tractor Summary: S1257 Fendt 724 Vario Gen7, Nebraska Tractor Test Lab Jan 2023

Nebraska Tractor Summary: S1257 Fendt 724 Vario Gen7, Nebraska Tractor Test Lab

Nebraska Tractor Tests

ABOUT THE TEST REPORT AND USE OF THE DATA The test data contained in this report are a tabulation of the results of a series of tests. Due to the restricted format of these pages, only a limited amount of data and not all of the tractor specifications are included. The full OECD report contains usually about 30 pages of data and specifications. The test data were obtained for each tractor under similar conditions and therefore, provide a means of comparison of performance based on a limited set of reported data. EXPLANATION OF THE TEST PROCEDURES Purpose The purpose of …

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Nebraska Tractor Test: 2273 John Deere 5130m Powrquad Plus, Nebraska Tractor Test Lab Jan 2023

Nebraska Tractor Test: 2273 John Deere 5130m Powrquad Plus, Nebraska Tractor Test Lab

Nebraska Tractor Tests

ABOUT THE TEST REPORT AND USE OF THE DATA The test data contained in this report are a tabulation of the results of a series of tests. Due to the restricted format of these pages, only a limited amount of data and not all of the tractor specifications are included. The full OECD report contains usually about 30 pages of data and specifications. The test data were obtained for each tractor under similar conditions and therefore, provide a means of comparison of performance based on a limited set of reported data. EXPLANATION OF THE TEST PROCEDURES Purpose The purpose of …

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Nebraska Summary #1274: New Holland T7.340 Hd, Nebraska Tractor Test Lab Jan 2023

Nebraska Summary #1274: New Holland T7.340 Hd, Nebraska Tractor Test Lab

Nebraska Tractor Tests

ABOUT THE TEST REPORT AND USE OF THE DATA The test data contained in this report are a tabulation of the results of a series of tests. Due to the restricted format of these pages, only a limited amount of data and not all of the tractor specifications are included. The full OECD report contains usually about 30 pages of data and specifications. The test data were obtained for each tractor under similar conditions and therefore, provide a means of comparison of performance based on a limited set of reported data. EXPLANATION OF THE TEST PROCEDURES Purpose The purpose of …

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Nebraska Summary #1297: Fendt 724 Vario Gen 7.1, Nebraska Tractor Test Lab Jan 2023

Nebraska Summary #1297: Fendt 724 Vario Gen 7.1, Nebraska Tractor Test Lab

Nebraska Tractor Tests

ABOUT THE TEST REPORT AND USE OF THE DATA The test data contained in this report are a tabulation of the results of a series of tests. Due to the restricted format of these pages, only a limited amount of data and not all of the tractor specifications are included. The full OECD report contains usually about 30 pages of data and specifications. The test data were obtained for each tractor under similar conditions and therefore, provide a means of comparison of performance based on a limited set of reported data. EXPLANATION OF THE TEST PROCEDURES Purpose The purpose of …

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Modeling Repairable System Failure Data Using Nhpp Reliability Growth Mode., Eunice Ofori-Addo Jan 2023

Modeling Repairable System Failure Data Using Nhpp Reliability Growth Mode., Eunice Ofori-Addo

EWU Masters Thesis Collection

Stochastic point processes have been widely used to describe the behaviour of repairable systems. The Crow nonhomogeneous Poisson process (NHPP) often known as the Power Law model is regarded as one of the best models for repairable systems. The goodness-of-fit test rejects the intensity function of the power law model, and so the log-linear model was fitted and tested for goodness-of-fit. The Weibull Time to Failure recurrent neural network (WTTE-RNN) framework, a probabilistic deep learning model for failure data, is also explored. However, we find that the WTTE-RNN framework is only appropriate failure data with independent and identically distributed interarrival …

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State Energy Research Center, University Of North Dakota. Energy And Environmental Research Center Jan 2023

State Energy Research Center, University Of North Dakota. Energy And Environmental Research Center

EERC Brochures and Fact Sheets

Information on the Energy & Environmental Research Center and its role as the State Research Center (SERC) for North Dakota. Showcases the EERC’s technological capabilities, outreach efforts, funding sources, and workforce development. [This item is an outdated or superseded version and retained for historical purposes only. It may no longer reflect current information, policies, or practices.]

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Oil, Gas & Mineral Law, Austin W. Brister, Logan Jones Jan 2023

Oil, Gas & Mineral Law, Austin W. Brister, Logan Jones

SMU Annual Texas Survey

This Article summarizes, sorts, and discusses the most impactful cases relating to oil, gas, and mineral law in Texas decided by the Texas Courts of Appeals and the Texas Supreme Court during the Survey period.

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Using Geographic Location-Based Public Health Features In Survival Analysis, Navid Seidi, Ardhendu S. Tripathy, Sajal K. Das Jan 2023

Using Geographic Location-Based Public Health Features In Survival Analysis, Navid Seidi, Ardhendu S. Tripathy, Sajal K. Das

Computer Science Faculty Research & Creative Works

Time elapsed till an event of interest is often modeled using the survival analysis methodology, which estimates a survival score based on the input features. There is a resurgence of interest in developing more accurate prediction models for time-to-event prediction in personalized healthcare using modern tools such as neural networks. Higher quality features and more frequent observations improve the predictions for a patient, however, the impact of including a patient's geographic location-Based public health statistics on individual predictions has not been studied. This paper proposes a complementary improvement to survival analysis models by incorporating public health statistics in the input …

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Tweetace: A Fine-Grained Classification Of Disaster Tweets Using Transformer Model, Ademola Adesokan, Sanjay Madria, Long Nguyen Jan 2023

Tweetace: A Fine-Grained Classification Of Disaster Tweets Using Transformer Model, Ademola Adesokan, Sanjay Madria, Long Nguyen

Computer Science Faculty Research & Creative Works

Disaster management teams play a crucial role in responding to catastrophic events with speed and efficiency. However, when faced with large data of disaster-related information, manual systems can struggle to classify the information accurately, especially when they are unavailable. This challenge highlights the need for integrating social media and implementing machine learning models to address the issue. However, the development of such models is dependent on the availability of adequately annotated data, which presents a significant obstacle in the field of crisis management. to address this challenge, our study focuses on the need for disaster event classification through social media. …

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Towards A Domain-Agnostic Knowledge Graph-As-A-Service Infrastructure For Active Cyber Defense With Intelligent Agents, Prasad Calyam, Mayank Kejriwal, Praveen Rao, Jianlin Cheng, Weichao Wang, Linquan Bai, V. Sriram Siddhardh Nadendla, Sanjay Kumar Madria, Sajal K. Das, Rohit Chadha, Khaza Anuarul Hoque, Kannappan Palaniappan, Kiran Neupane, Roshan Lal Neupane, Sankeerth Gandhari, Mukesh Singhal, Lotfi Othmane, Meng Yu Jan 2023

Towards A Domain-Agnostic Knowledge Graph-As-A-Service Infrastructure For Active Cyber Defense With Intelligent Agents, Prasad Calyam, Mayank Kejriwal, Praveen Rao, Jianlin Cheng, Weichao Wang, Linquan Bai, V. Sriram Siddhardh Nadendla, Sanjay Kumar Madria, Sajal K. Das, Rohit Chadha, Khaza Anuarul Hoque, Kannappan Palaniappan, Kiran Neupane, Roshan Lal Neupane, Sankeerth Gandhari, Mukesh Singhal, Lotfi Othmane, Meng Yu

Computer Science Faculty Research & Creative Works

Active cyber defense mechanisms are necessary to perform automated, and even autonomous operations using intelligent agents that defend against modern/sophisticated AI-inspired cyber threats (e.g., ransomware, cryptojacking, deep-fakes). These intelligent agents need to rely on deep learning using mature knowledge and should have the ability to apply this knowledge in a situational and timely manner for a given AI-inspired cyber threat. in this paper, we describe a 'domain-Agnostic knowledge graph-As-A-service' infrastructure that can support the ability to create/store domain-specific knowledge graphs for intelligent agent Apps to deploy active cyber defense solutions defending real-world applications impacted by AI-inspired cyber threats. Specifically, we …

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Supervised Deep Tree In Alzheimer's Disease, Xiaowei Yu, Lu Zhang, Yanjun Lyu, Tianming Liu, Dajiang Zhu Jan 2023

Supervised Deep Tree In Alzheimer's Disease, Xiaowei Yu, Lu Zhang, Yanjun Lyu, Tianming Liu, Dajiang Zhu

Computer Science Faculty Research & Creative Works

As a progressive neurodegenerative disorder, the pathological changes of Alzheimer's disease (AD) might begin as much as two decades before the manifestation of clinical symptoms. Since the nature of the irreversible pathology of AD, early diagnosis provides a more tractable way for disease intervention and treatment. Therefore, numerous approaches have been developed for early diagnostic purposes. Although several important biomarkers have been established, most of the existing methods show limitations in describing the continuum of AD progression. However, understanding this continuous development is essential to understand the intrinsic progression mechanism of AD. In this work, we proposed a supervised deep …

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Representative Functional Connectivity Learning For Multiple Clinical Groups In Alzheimer's Disease, Lu Zhang, Xiaowei Yu, Yanjun Lyu, Tianming Liu, Dajiang Zhu Jan 2023

Representative Functional Connectivity Learning For Multiple Clinical Groups In Alzheimer's Disease, Lu Zhang, Xiaowei Yu, Yanjun Lyu, Tianming Liu, Dajiang Zhu

Computer Science Faculty Research & Creative Works

Mild cognitive impairment (MCI) is a high-risk dementia condition which progresses to probable Alzheimer's disease (AD) at approximately 10% to 15% per year. Characterization of group-level differences between two subtypes of MCI - stable MCI (sMCI) and progressive MCI (pMCI) is the key step to understand the mechanisms of MCI progression and enable possible delay of transition from MCI to AD. Functional connectivity (FC) is considered as a promising way to study MCI progression since which may show alterations even in preclinical stages and provide substrates for AD progression. However, the representative FC patterns during AD development for different clinical …

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Surviving Chatgpt In Healthcare, Zhengliang Liu, Lu Zhang, Zihao Wu, Xiaowei Yu, Chao Cao, Haixing Dai, Ninghao Liu, Jun Liu, Wei Liu, Quanzheng Li, Dinggang Shen, Xiang Li, Dajiang Zhu, Tianming Liu Jan 2023

Surviving Chatgpt In Healthcare, Zhengliang Liu, Lu Zhang, Zihao Wu, Xiaowei Yu, Chao Cao, Haixing Dai, Ninghao Liu, Jun Liu, Wei Liu, Quanzheng Li, Dinggang Shen, Xiang Li, Dajiang Zhu, Tianming Liu

Computer Science Faculty Research & Creative Works

At the dawn of Artificial General Intelligence (AGI), the emergence of large language models such as ChatGPT show promise in revolutionizing healthcare by improving patient care, expanding medical access, and optimizing clinical processes. However, their integration into healthcare systems requires careful consideration of potential risks, such as inaccurate medical advice, patient privacy violations, the creation of falsified documents or images, overreliance on AGI in medical education, and the perpetuation of biases. It is crucial to implement proper oversight and regulation to address these risks, ensuring the safe and effective incorporation of AGI technologies into healthcare systems. By acknowledging and mitigating …

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The Evolution Of Ai On The Commercial Flight Deck: Finding Balance Between Efficiency And Safety While Maintaining The Integrity Of Operator Trust, Mark Miller, Sam Holley, Leila Halawi Jan 2023

The Evolution Of Ai On The Commercial Flight Deck: Finding Balance Between Efficiency And Safety While Maintaining The Integrity Of Operator Trust, Mark Miller, Sam Holley, Leila Halawi

Publications

As artificial intelligence (AI) seeks to improve modern society, the commercial aviation industry offers a significant opportunity. Although many parts of commercial aviation including maintenance, the ramp, and air traffic control show promise to integrate AI, the highly computerized digital flight deck (DFD) could be challenging. The researchers seek to understand what role AI could provide going forward by assessing AI evolution on the commercial flight deck over the past 50 years. A modified SHELL diagram is used to complete a Human Factors (HF) analysis of the early use for AI on the commercial flight deck through introduction of the …

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Catalog 2023-2024, University Of North Florida Jan 2023

Catalog 2023-2024, University Of North Florida

UNF Course Catalogs

No abstract provided.

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Sustainability Impact Assessment Of New Ventures: An Emerging Field Of Research, Klaus Fichter, Florian Ludeke-Freund, Stefan Schaltegger, Simon J.D. Schillebeeckx Jan 2023

Sustainability Impact Assessment Of New Ventures: An Emerging Field Of Research, Klaus Fichter, Florian Ludeke-Freund, Stefan Schaltegger, Simon J.D. Schillebeeckx

Research Collection Lee Kong Chian School Of Business

Entrepreneurs and start-ups are key actors in implementing environmental innovation and accelerating sustainability transitions. Thus, analyzing as well as predicting the impact of entrepreneurial ventures is central to management and entrepreneurship research. The sustainability performance, value and impact of incumbent firms and their products and services has been a key topic in business-related sustainability research for many years. However, assessing the sustainability effects of new ventures such as start-ups is a neglected area in the research literature. This article therefore provides an overview, including key definitions, a new conceptual framework, and notions that can help guide and inspire a future …

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Learning Large Neighborhood Search For Vehicle Routing In Airport Ground Handling, Jianan Zhou, Yaoxin Wu, Zhiguang Cao, Wen Song, Jie Zhang, Zhenghua Chen Jan 2023

Learning Large Neighborhood Search For Vehicle Routing In Airport Ground Handling, Jianan Zhou, Yaoxin Wu, Zhiguang Cao, Wen Song, Jie Zhang, Zhenghua Chen

Research Collection School Of Computing and Information Systems

Dispatching vehicle fleets to serve flights is a key task in airport ground handling (AGH). Due to the notable growth of flights, it is challenging to simultaneously schedule multiple types of operations (services) for a large number of flights, where each type of operation is performed by one specific vehicle fleet. To tackle this issue, we first represent the operation scheduling as a complex vehicle routing problem and formulate it as a mixed integer linear programming (MILP) model. Then given the graph representation of the MILP model, we propose a learning assisted large neighborhood search (LNS) method using data generated …

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Reks: Role-Based Encrypted Keyword Search With Enhanced Access Control For Outsourced Cloud Data, Yibin Miao, Feng Li, Xiaohua Jia, Huaxiong Wang, Ximeng Liu, Kim-Kwang Raymond Choo, Robert H. Deng Jan 2023

Reks: Role-Based Encrypted Keyword Search With Enhanced Access Control For Outsourced Cloud Data, Yibin Miao, Feng Li, Xiaohua Jia, Huaxiong Wang, Ximeng Liu, Kim-Kwang Raymond Choo, Robert H. Deng

Research Collection School Of Computing and Information Systems

Keyword-based search over encrypted data is an important technique to achieve both data confidentiality and utilization in cloud outsourcing services. While commonly used access control mechanisms, such as identity-based encryption and attribute-based encryption, do not generally scale well for hierarchical access permissions. To solve this problem, we propose a Role-based Encrypted Keyword Search (REKS) scheme by using the role-based access control and broadcast encryption. Specifically, REKS allows owners to deploy hierarchical access control by allowing users with parent roles to have access permissions from child roles. Using REKS, we further facilitate token generation preprocessing and efficient user management, thereby significantly …

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I Know What You Are Searching For: Code Snippet Recommendation From Stack Overflow Posts, Zhipeng Gao, Xin Xia, David Lo, John C. Grundy, Xindong Zhang, Zhenchang Xing Jan 2023

I Know What You Are Searching For: Code Snippet Recommendation From Stack Overflow Posts, Zhipeng Gao, Xin Xia, David Lo, John C. Grundy, Xindong Zhang, Zhenchang Xing

Research Collection School Of Computing and Information Systems

Stack Overflow has been heavily used by software developers to seek programming-related information. More and more developers use Community Question and Answer forums, such as Stack Overflow, to search for code examples of how to accomplish a certain coding task. This is often considered to be more efficient than working from source documentation, tutorials, or full worked examples. However, due to the complexity of these online Question and Answer forums and the very large volume of information they contain, developers can be overwhelmed by the sheer volume of available information. This makes it hard to find and/or even be aware …

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Monadic Second-Order Incorrectness Logic For Gp 2, Christopher M. Poskitt, Detlef Plump Jan 2023

Monadic Second-Order Incorrectness Logic For Gp 2, Christopher M. Poskitt, Detlef Plump

Research Collection School Of Computing and Information Systems

Program logics typically reason about an over-approximation of program behaviour to prove the absence of bugs. Recently, program logics have been proposed that instead prove the presence of bugs by means of under-approximate reasoning, which has the promise of better scalability. In this paper, we present an under-approximate program logic for GP 2, a rule-based programming language for manipulating graphs. We define the proof rules of this program logic extensionally, i.e. independently of fixed assertion languages, then instantiate them with a morphism-based assertion language able to specify monadic second-order properties on graphs (e.g. path properties). We show how these proof …

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Is A Pretrained Model The Answer To Situational Awareness Detection On Social Media?, Siaw Ling Lo, Kahhe Lee, Yuhao Zhang Jan 2023

Is A Pretrained Model The Answer To Situational Awareness Detection On Social Media?, Siaw Ling Lo, Kahhe Lee, Yuhao Zhang

Research Collection School Of Computing and Information Systems

Social media can be valuable for extracting information about an event or incident on the ground. However, the vast amount of content shared, and the linguistic variants of languages used on social media make it challenging to identify important situational awareness content to aid in decision-making for first responders. In this study, we assess whether pretrained models can be used to address the aforementioned challenges on social media. Various pretrained models, including static word embedding (such as Word2Vec and GloVe) and contextualized word embedding (such as DistilBERT) are studied in detail. According to our findings, a vanilla DistilBERT pretrained language …

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Dual-View Preference Learning For Adaptive Recommendation, Zhongzhou Liu, Yuan Fang, Min Wu Jan 2023

Dual-View Preference Learning For Adaptive Recommendation, Zhongzhou Liu, Yuan Fang, Min Wu

Research Collection School Of Computing and Information Systems

While recommendation systems have been widely deployed, most existing approaches only capture user preferences in the , i.e., the user's general interest across all kinds of items. However, in real-world scenarios, user preferences could vary with items of different natures, which we call the . Both views are crucial for fully personalized recommendation, where an underpinning macro-view governs a multitude of finer-grained preferences in the micro-view. To model the dual views, in this paper, we propose a novel model called Dual-View Adaptive Recommendation (DVAR). In DVAR, we formulate the micro-view based on item categories, and further integrate it with the …

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Learning Analytics In Informal, Participatory Collaborative Learning, Michelle L. F. Cheong, Aditya Singh, Yun-Chen Chen, Bing Tian Dai Jan 2023

Learning Analytics In Informal, Participatory Collaborative Learning, Michelle L. F. Cheong, Aditya Singh, Yun-Chen Chen, Bing Tian Dai

Research Collection School Of Computing and Information Systems

Learning Analytics was recognized to be “the third wave of large-scale developments in instructional technology”. Learning Management Systems (LMSs) have been widely adopted as the learning analytics tools because the captured data represents how the learners’ interact with the system during formal learning. However, most LMSs’ analytics models do not capture learning activities outside the systems. We built an integrated Telegram mobile application and a web-based portal discussion forum, to enable informal, participatory and collaborative learning beyond the classroom. We analyzed student-initiated question-and-answer discussion posts where our machine learning algorithm will predict the quality of the posts, and the system …

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Anchorage: Visual Analysis Of Satisfaction In Customer Service Videos Via Anchor Events, Kam Kwai Wong, Xingbo Wang, Yong Wang, Jianben He, Rong Zhang, Huamin Qu Jan 2023

Anchorage: Visual Analysis Of Satisfaction In Customer Service Videos Via Anchor Events, Kam Kwai Wong, Xingbo Wang, Yong Wang, Jianben He, Rong Zhang, Huamin Qu

Research Collection School Of Computing and Information Systems

Delivering customer services through video communications has brought new opportunities to analyze customer satisfaction for quality management. However, due to the lack of reliable self-reported responses, service providers are troubled by the inadequate estimation of customer services and the tedious investigation into multimodal video recordings. We introduce , a visual analytics system to evaluate customer satisfaction by summarizing multimodal behavioral features in customer service videos and revealing abnormal operations in the service process. We leverage the semantically meaningful operations to introduce structured event understanding into videos which help service providers quickly navigate to events of their interest. supports a comprehensive …

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Dashboard Design Mining And Recommendation, Yanna Lin, Haotian Li, Aoyu Wu, Yong Wang, Huamin Qu Jan 2023

Dashboard Design Mining And Recommendation, Yanna Lin, Haotian Li, Aoyu Wu, Yong Wang, Huamin Qu

Research Collection School Of Computing and Information Systems

Dashboards, which comprise multiple views on a single display, help analyze and communicate multiple perspectives of data simultaneously. However, creating effective and elegant dashboards is challenging since it requires careful and logical arrangement and coordination of multiple visualizations. To solve the problem, we propose a data-driven approach for mining design rules from dashboards and automating dashboard organization. Specifically, we focus on two prominent aspects of the organization: , which describes the position, size, and layout of each view in the display space; and, which indicates the interaction between pairwise views. We build a new dataset containing 854 dashboards crawled online, …

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Relation Preserving Triplet Mining For Stabilising The Triplet Loss In Re-Identification Systems, Adhiraj Ghosh, Kuruparan Shanmugalingam, Wen-Yan Lin Jan 2023

Relation Preserving Triplet Mining For Stabilising The Triplet Loss In Re-Identification Systems, Adhiraj Ghosh, Kuruparan Shanmugalingam, Wen-Yan Lin

Research Collection School Of Computing and Information Systems

Object appearances change dramatically with pose variations. This creates a challenge for embedding schemes that seek to map instances with the same object ID to locations that are as close as possible. This issue becomes significantly heightened in complex computer vision tasks such as re-identification(reID). In this paper, we suggest that these dramatic appearance changes are indications that an object ID is composed of multiple natural groups, and it is counterproductive to forcefully map instances from different groups to a common location. This leads us to introduce Relation Preserving Triplet Mining (RPTM), a feature matching guided triplet mining scheme, that …

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Reinforcement Learning Enhanced Pichunter For Interactive Search, Zhixin Ma, Jiaxin Wu, Weixiong Loo, Chong-Wah Ngo Jan 2023

Reinforcement Learning Enhanced Pichunter For Interactive Search, Zhixin Ma, Jiaxin Wu, Weixiong Loo, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

With the tremendous increase in video data size, search performance could be impacted significantly. Specifically, in an interactive system, a real-time system allows a user to browse, search and refine a query. Without a speedy system quickly, the main ingredient to engage a user to stay focused, an interactive system becomes less effective even with a sophisticated deep learning system. This paper addresses this challenge by leveraging approximate search, Bayesian inference, and reinforcement learning. For approximate search, we apply a hierarchical navigable small world, which is an efficient approximate nearest neighbor search algorithm. To quickly prune the search scope, we …

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Human-Centred Artificial Intelligence In The Banking Sector, Krishnaraj Arul Obuchettiar, Alan @ Ali Madjelisi Megargel Jan 2023

Human-Centred Artificial Intelligence In The Banking Sector, Krishnaraj Arul Obuchettiar, Alan @ Ali Madjelisi Megargel

Research Collection School Of Computing and Information Systems

Changes in technology have shaped how corporate and retail businesses have evolved, alongside the customers’ preferences. The advent of smart digital devices and social media has shaped how consumers interact and transact with their financial institutions over the past two decades. With the rapid evolution of new technologies and customers' growing preference for digital engagement with financial institutions, organizations need to adopt and align with emerging technologies that support speed, accuracy, efficiency, and security in a user-friendly manner. Today, consumers want hyper-personalized interactions that are more frequent and proactive. Moreover, financial institutions have a growing need to cater to consumers' …

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Achieving High Map-Coverage Through Pattern Constraint Reduction, Yingquan Zhao, Zan Wang, Shuang Liu, Jun Sun, Junjie Chen, Xiang Chen Jan 2023

Achieving High Map-Coverage Through Pattern Constraint Reduction, Yingquan Zhao, Zan Wang, Shuang Liu, Jun Sun, Junjie Chen, Xiang Chen

Research Collection School Of Computing and Information Systems

Testing multi-threaded programs is challenging due to the enormous space of thread interleavings. Recently, a code coverage criterion for multi-threaded programs called MAP-coverage has been proposed and shown to be effective for testing concurrent programs. Existing approaches for achieving high MAP-coverage are based on random testing with simple heuristics, which is ineffective in systematically triggering rare thread interleavings. In this study, we propose a novel approach called pattern constraint reduction (PCR), which employs optimized constraint solving to generate thread interleavings for high MAP-coverage. The idea is to iteratively encode and solve path conditions to generate thread interleavings which are guaranteed …

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