Open Access. Powered by Scholars. Published by Universities.®

Computer Sciences Commons™

Open Access. Powered by Scholars. Published by Universities.®

Software Engineering

Institution
Keyword
Publication Year
Publication
Publication Type
File Type

Articles 1471 - 1500 of 4315

Full-Text Articles in Computer Sciences

Experimental Comparison Of Features And Classifiers For Android Malware Detection, Lwin Khin Shar, Biniam Fisseha Demissie, Mariano Ceccato, Wei Minn Oct 2020

Experimental Comparison Of Features And Classifiers For Android Malware Detection, Lwin Khin Shar, Biniam Fisseha Demissie, Mariano Ceccato, Wei Minn

Research Collection School Of Computing and Information Systems

Android platform has dominated the smart phone market for years now and, consequently, gained a lot of attention from attackers. Malicious apps (malware) pose a serious threat to the security and privacy of Android smart phone users. Available approaches to detect mobile malware based on machine learning rely on features extracted with static analysis or dynamic analysis techniques. Dif- ferent types of machine learning classi ers (such as support vector machine and random forest) deep learning classi ers (based on deep neural networks) are then trained on extracted features, to produce models that can be used to detect mobile malware. …

Go to article

Towards Systematically Deriving Defence Mechanisms From Functional Requirements Of Cyber-Physical Systems, Cheah Huei Yoong, Venkata Reddy Palleti, Arlindo Silva, Christopher M. Poskitt Oct 2020

Towards Systematically Deriving Defence Mechanisms From Functional Requirements Of Cyber-Physical Systems, Cheah Huei Yoong, Venkata Reddy Palleti, Arlindo Silva, Christopher M. Poskitt

Research Collection School Of Computing and Information Systems

The threats faced by cyber-physical systems (CPSs) in critical infrastructure have motivated the development of different attack detection mechanisms, such as those that monitor for violations of invariants, i.e. properties that always hold in normal operation. Given the complexity of CPSs, several existing approaches focus on deriving invariants automatically from data logs, but these can miss possible system behaviours if they are not represented in that data. Furthermore, resolving any design flaws identified in this process is costly, as the CPS is already built. In this position paper, we propose a systematic method for deriving invariants before a CPS is …

Go to article

Peer-Inspired Student Performance Prediction In Interactive Online Question Pools With Graph Neural Network, Haotian Li, Huan Wei, Yong Wang, Yangqiu Song, Huamin. Qu Oct 2020

Peer-Inspired Student Performance Prediction In Interactive Online Question Pools With Graph Neural Network, Haotian Li, Huan Wei, Yong Wang, Yangqiu Song, Huamin. Qu

Research Collection School Of Computing and Information Systems

Student performance prediction is critical to online education. It can benefit many downstream tasks on online learning platforms, such as estimating dropout rates, facilitating strategic intervention, and enabling adaptive online learning. Interactive online question pools provide students with interesting interactive questions to practice their knowledge in online education. However, little research has been done on student performance prediction in interactive online question pools. Existing work on student performance prediction targets at online learning platforms with predefined course curriculum and accurate knowledge labels like MOOC platforms, but they are not able to fully model knowledge evolution of students in interactive online …

Go to article

Did Our Course Design On Software Architecture Meet Our Student’S Learning Expectations?, Eng Lieh Ouh, Benjamin Gan, Yunghans Irawan Oct 2020

Did Our Course Design On Software Architecture Meet Our Student’S Learning Expectations?, Eng Lieh Ouh, Benjamin Gan, Yunghans Irawan

Research Collection School Of Computing and Information Systems

This Innovative Practice Full Paper discusses our course design on software architecture to meet the learning expectations of two groups of software engineers. Software engineers with working experiences frequently find themselves the need to upskill in their lifelong learning journey. Their learning expectations are shaped not just by their need to know but also other learning characteristics such as their working experiences. In many cases, we design courses based on the required learning outcomes and assessment criteria. In this paper, we wish to find out whether our course design on software architecture has met the learning expectations of our students …

Go to article

Experience Report On The Use Of Technology To Manage Capstone Course Projects, Benjamin Gan, Eng Lieh Ouh Oct 2020

Experience Report On The Use Of Technology To Manage Capstone Course Projects, Benjamin Gan, Eng Lieh Ouh

Research Collection School Of Computing and Information Systems

This full paper presents an experience report describing lessons learnt from innovative practice use of technologies in an undergraduate computing capstone course. At our school, around fifty-five teams comprising of around 300 students take this course every year. With fifty-five teams, we needed a system to schedule presentations; improve communications; collaborate between stakeholders; share knowledge; monitor progress; team up students; match students to projects; improve grading process; showcase posters; and track improvements using analytics. The Learning Management Systems (LMS) is great to manage course content and grade submission. On the other hand, students are required to conduct agile sprint reviews …

Go to article

Crossasr: Efficient Differential Testing Of Automatic Speech Recognition Via Text-To-Speech, Muhammad Hilmi Asyrofi, Thung Ferdian, David Lo, Lingxiao Jiang Oct 2020

Crossasr: Efficient Differential Testing Of Automatic Speech Recognition Via Text-To-Speech, Muhammad Hilmi Asyrofi, Thung Ferdian, David Lo, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

Automatic speech recognition (ASR) systems are ubiquitous parts of modern life. It can be found in our smartphones, desktops, and smart home systems. To ensure its correctness in recognizing speeches, ASR needs to be tested. Testing ASR requires test cases in the form of audio files and their transcribed texts. Building these test cases manually, however, is tedious and time-consuming.To deal with the aforementioned challenge, in this work, we propose CrossASR, an approach that capitalizes the existing Text-To-Speech (TTS) systems to automatically generate test cases for ASR systems. CrossASR is a differential testing solution that compares outputs of multiple ASR …

Go to article

Generating Question Titles For Stack Overflow From Mined Code Snippets, Zhipeng Gao, Xin Xia, John Grundy, David Lo, Yuan-Fang Li Oct 2020

Generating Question Titles For Stack Overflow From Mined Code Snippets, Zhipeng Gao, Xin Xia, John Grundy, David Lo, Yuan-Fang Li

Research Collection School Of Computing and Information Systems

Stack Overflow has been heavily used by software developers as a popular way to seek programming-related information from peers via the internet. The Stack Overflow community recommends users to provide the related code snippet when they are creating a question to help others better understand it and offer their help. Previous studies have shown that a significant number of these questions are of low-quality and not attractive to other potential experts in Stack Overflow. These poorly asked questions are less likely to receive useful answers and hinder the overall knowledge generation and sharing process. Considering one of the reasons for …

Go to article

Viscene: A Collaborative Authoring Tool For Scene Descriptions In Videos, Rosiana Natalie, Ebrima Jarjue, Hernisa Kacorri, Kotaro Hara Oct 2020

Viscene: A Collaborative Authoring Tool For Scene Descriptions In Videos, Rosiana Natalie, Ebrima Jarjue, Hernisa Kacorri, Kotaro Hara

Research Collection School Of Computing and Information Systems

Audio descriptions can make the visual content in videos accessible to people with visual impairments. However, the majority of the online videos lack audio descriptions due in part to the shortage of experts who can create high-quality descriptions. We present ViScene, a web-based authoring tool that taps into the larger pool of sighted non-experts to help them generate high-quality descriptions via two feedback mechanisms - succinct visualizations and comments from an expert. Through a mixed-design study with N = 6 participants, we explore the usability of ViScene and the quality of the descriptions created by sighted non-experts with and without …

Go to article

Zoomwalls: Dynamic Walls That Simulate Haptic Infrastructure For Room-Scale Vr World, Yan Yixian, Kazuki Takashima, Anthony Tang, Takayuki Tanno, Kazuyuki Fujita, Yoshifumi Kitamura Oct 2020

Zoomwalls: Dynamic Walls That Simulate Haptic Infrastructure For Room-Scale Vr World, Yan Yixian, Kazuki Takashima, Anthony Tang, Takayuki Tanno, Kazuyuki Fujita, Yoshifumi Kitamura

Research Collection School Of Computing and Information Systems

We focus on the problem of simulating the haptic infrastructure of a virtual environment (i.e. walls, doors). Our approach relies on multiple ZoomWalls---autonomous robotic encounter-type haptic wall-shaped props---that coordinate to provide haptic feedback for room-scale virtual reality. Based on a user's movement through the physical space, ZoomWall props are coordinated through a predict-and-dispatch architecture to provide just-in-time haptic feedback for objects the user is about to touch. To refine our system, we conducted simulation studies of different prediction algorithms, which helped us to refine our algorithmic approach to realize the physical ZoomWall prototype. Finally, we evaluated our system through a …

Go to article

Fakepolisher: Making Deepfakes More Detection-Evasive By Shallow Reconstruction, Yihao Huang, Felix Juefei-Xu, Run Wang, Qing Guo, Lei Ma, Xiaofei Xie, Jianwen Li, Weikai Miao, Yang Liu, Geguang Pu Oct 2020

Fakepolisher: Making Deepfakes More Detection-Evasive By Shallow Reconstruction, Yihao Huang, Felix Juefei-Xu, Run Wang, Qing Guo, Lei Ma, Xiaofei Xie, Jianwen Li, Weikai Miao, Yang Liu, Geguang Pu

Research Collection School Of Computing and Information Systems

At this moment, GAN-based image generation methods are still imperfect, whose upsampling design has limitations in leaving some certain artifact patterns in the synthesized image. Such artifact patterns can be easily exploited (by recent methods) for difference detection of real and GAN-synthesized images. However, the existing detection methods put much emphasis on the artifact patterns, which can become futile if such artifact patterns were reduced.Towards reducing the artifacts in the synthesized images, in this paper, we devise a simple yet powerful approach termed FakePolisher that performs shallow reconstruction of fake images through a learned linear dictionary, intending to effectively and …

Go to article

Amora: Black-Box Adversarial Morphing Attack, Run Wang, Felix Juefei-Xu, Qing Guo, Yihao Huang, Xiaofei Xie, Lei Ma, Yang Liu Oct 2020

Amora: Black-Box Adversarial Morphing Attack, Run Wang, Felix Juefei-Xu, Qing Guo, Yihao Huang, Xiaofei Xie, Lei Ma, Yang Liu

Research Collection School Of Computing and Information Systems

Nowadays, digital facial content manipulation has become ubiquitous and realistic with the success of generative adversarial networks (GANs), making face recognition (FR) systems suffer from unprecedented security concerns. In this paper, we investigate and introduce a new type of adversarial attack to evade FR systems by manipulating facial content, called adversarial morphing attack (a.k.a. Amora). In contrast to adversarial noise attack that perturbs pixel intensity values by adding human-imperceptible noise, our proposed adversarial morphing attack works at the semantic level that perturbs pixels spatially in a coherent manner. To tackle the black-box attack problem, we devise a simple yet effective …

Go to article

Regression Testing Of Massively Multiplayer Online Role-Playing Games, Yuechen Wu, Yingfeng Chen, Xiaofei Xie, Bing Yu, Changjie Fan, Lei Ma Oct 2020

Regression Testing Of Massively Multiplayer Online Role-Playing Games, Yuechen Wu, Yingfeng Chen, Xiaofei Xie, Bing Yu, Changjie Fan, Lei Ma

Research Collection School Of Computing and Information Systems

Regression testing aims to check the functionality consistency during software evolution. Although general regression testing has been extensively studied, regression testing in the context of video games, especially Massively Multiplayer Online Role-Playing Games (MMORPGs), is largely untouched so far. One big challenge is that game testing requires a certain level of intelligence in generating suitable action sequences among the huge search space, to accomplish complex tasks in the MMORPG. Existing game testing mainly relies on either the manual playing or manual scripting, which are labor-intensive and time-consuming. Even worse, it is often unable to satisfy the frequent industrial game evolution. …

Go to article

What Is The Vocabulary Of Flaky Tests?, Gustavo Pinto, Breno Miranda, Supun Dissanayake, Marcelo D'Amorim, Christoph Treude, Antonia Bertolino Oct 2020

What Is The Vocabulary Of Flaky Tests?, Gustavo Pinto, Breno Miranda, Supun Dissanayake, Marcelo D'Amorim, Christoph Treude, Antonia Bertolino

Research Collection School Of Computing and Information Systems

Flaky tests are tests whose outcomes are non-deterministic. Despite the recent research activity on this topic, no effort has been made on understanding the vocabulary of flaky tests. This work proposes to automatically classify tests as flaky or not based on their vocabulary. Static classification of flaky tests is important, for example, to detect the introduction of flaky tests and to search for flaky tests after they are introduced in regression test suites. We evaluated performance of various machine learning algorithms to solve this problem. We constructed a data set of flaky and non-flaky tests by running every test case, …

Go to article

The Impact Of Dynamics Of Collaborative Software Engineering On Introverts: A Study Protocol, Ingrid Nunes, Christoph Treude, Fabio Calefato Oct 2020

The Impact Of Dynamics Of Collaborative Software Engineering On Introverts: A Study Protocol, Ingrid Nunes, Christoph Treude, Fabio Calefato

Research Collection School Of Computing and Information Systems

Background: Collaboration among software engineers through face-to-face discussions in teams has been promoted since the adoption of agile methods. However, these discussions might demote the contribution of software engineers who are introverts, possibly leading to sub-optimal solutions and creating work environments that benefit extroverts. Objective: We aim to evaluate whether providing software engineers with time to work individually and reason about a collective problem is a setting that makes introverts more comfortable to interact and contribute more, ultimately leading to better solutions. Method: We plan to conduct a between-subjects study, with teams in a control group that design a software …

Go to article

Inheritance Details In Scala, Raffi Khatchadourian Sep 2020

Inheritance Details In Scala, Raffi Khatchadourian

Open Educational Resources

No abstract provided.

Go to article

An Enhancement Of Age And Gender Classification Accuracy With Hybrid Handcrafted And Deep Features Using Hierarchical Extreme Learning Machine, Mohammad Javidan Darugar Sep 2020

An Enhancement Of Age And Gender Classification Accuracy With Hybrid Handcrafted And Deep Features Using Hierarchical Extreme Learning Machine, Mohammad Javidan Darugar

Student Works (2020-2029)

Age and gender classification are some of the essential algorithms that have many use cases in our everyday life. For example, in robotics, field robots can interact with a human base on their gender in data analysis, to have statistics about age and gender of audiences in social events, YouTube video analysis, and many other applications. In this research, we have addressed limitations in deep neural networks, which by overcoming this limitation, we can gain better accuracy and performance. Our study has several other possible applications which are not limited only to age and gender classification. This dissertation is about …

Go to article

Analytic Provenance For Software Reverse Engineers, Wayne C. Henry Sep 2020

Analytic Provenance For Software Reverse Engineers, Wayne C. Henry

Theses and Dissertations

Reverse engineering is a time-consuming process essential to software-security tasks such as malware analysis and vulnerability discovery. During the process, an engineer will follow multiple leads to determine how the software functions. The combination of time and possible explanations makes it difficult for the engineers to maintain a context of their findings within the overall task. Analytic provenance tools have demonstrated value in similarly complex fields that require open-ended exploration and hypothesis vetting. However, they have not been explored in the reverse engineering domain. This dissertation presents SensorRE, the first analytic provenance tool designed to support software reverse engineers. A …

Go to article

Cats Are Not Fish: Deep Learning Testing Calls For Out-Of-Distribution Awareness, David Berend, Xiaofei Xie, Lei Ma, Lingjun Zhou, Yang Liu, Chi Xu, Jianjun Zhao Sep 2020

Cats Are Not Fish: Deep Learning Testing Calls For Out-Of-Distribution Awareness, David Berend, Xiaofei Xie, Lei Ma, Lingjun Zhou, Yang Liu, Chi Xu, Jianjun Zhao

Research Collection School Of Computing and Information Systems

As Deep Learning (DL) is continuously adopted in many industrial applications, its quality and reliability start to raise concerns. Similar to the traditional software development process, testing the DL software to uncover its defects at an early stage is an effective way to reduce risks after deployment. According to the fundamental assumption of deep learning, the DL software does not provide statistical guarantee and has limited capability in handling data that falls outside of its learned distribution, i.e., out-of-distribution (OOD) data. Although recent progress has been made in designing novel testing techniques for DL software, which can detect thousands of …

Go to article

Wait For It: Identifying 'On-Hold' Self-Admitted Technical Debt, Rungroj Maipradit, Christoph Treude, Hideaki Hata, Kenichi Matsumoto Sep 2020

Wait For It: Identifying 'On-Hold' Self-Admitted Technical Debt, Rungroj Maipradit, Christoph Treude, Hideaki Hata, Kenichi Matsumoto

Research Collection School Of Computing and Information Systems

Self-admitted technical debt refers to situations where a software developer knows that their current implementation is not optimal and indicates this using a source code comment. In this work, we hypothesize that it is possible to develop automated techniques to understand a subset of these comments in more detail, and to propose tool support that can help developers manage self-admitted technical debt more effectively. Based on a qualitative study of 333 comments indicating self-admitted technical debt, we first identify one particular class of debt amenable to automated management: on-hold self-admitted technical debt (on-hold SATD), i.e., debt which contains a condition …

Go to article

Marble: Model-Based Robustness Analysis Of Stateful Deep Learning Systems, Xiaoning Du, Yi Li, Xiaofei Xie, Lei Ma, Yang Liu, Jianjun Zhao Sep 2020

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

Research Collection School Of Computing and Information Systems

State-of-the-art deep learning (DL) systems are vulnerable to adversarial examples, which hinders their potential adoption in safetyand security-critical scenarios. While some recent progress has been made in analyzing the robustness of feed-forward neural networks, the robustness analysis for stateful DL systems, such as recurrent neural networks (RNNs), still remains largely uncharted. In this paper, we propose Marble, a model-based approach for quantitative robustness analysis of real-world RNN-based DL systems. Marble builds a probabilistic model to compactly characterize the robustness of RNNs through abstraction. Furthermore, we propose an iterative refinement algorithm to derive a precise abstraction, which enables accurate quantification of …

Go to article

Dct: An Scalable Multi-Objective Module Clustering Tool, Ana Paula M. Tarchetti, Luis Henrique Vieira Amaral, Marcos C. Oliveira, Rodrigo Bonifacio, Gustavo Pinto, David Lo Sep 2020

Dct: An Scalable Multi-Objective Module Clustering Tool, Ana Paula M. Tarchetti, Luis Henrique Vieira Amaral, Marcos C. Oliveira, Rodrigo Bonifacio, Gustavo Pinto, David Lo

Research Collection School Of Computing and Information Systems

Maintaining complex software systems is a timeconsuming and challenging task. Practitioners must have a general understanding of the system’s decomposition and how the system’s developers have implemented the software features (probably cutting across different modules). Re-engineering practices are imperative to tackle these challenges. Previous research has shown the benefits of using software module clustering (SMC) to aid developers during re-engineering tasks (e.g., revealing the architecture of the systems, identifying how the concerns are spread among the modules of the systems, recommending refactorings, and so on). Nonetheless, although the literature on software module clustering has substantially evolved in the last 20 …

Go to article

The Impact Of Automated Feature Selection Techniques On The Interpretation Of Defect Models, Jirayus Jiarpakdee, Chakkrit Tantithamthavorn, Christoph Treude Sep 2020

The Impact Of Automated Feature Selection Techniques On The Interpretation Of Defect Models, Jirayus Jiarpakdee, Chakkrit Tantithamthavorn, Christoph Treude

Research Collection School Of Computing and Information Systems

The interpretation of defect models heavily relies on software metrics that are used to construct them. Prior work often uses feature selection techniques to remove metrics that are correlated and irrelevant in order to improve model performance. Yet, conclusions that are derived from defect models may be inconsistent if the selected metrics are inconsistent and correlated. In this paper, we systematically investigate 12 automated feature selection techniques with respect to the consistency, correlation, performance, computational cost, and the impact on the interpretation dimensions. Through an empirical investigation of 14 publicly-available defect datasets, we find that (1) 94–100% of the selected …

Go to article

Accelerating All-Sat Computation With Short Blocking Clauses, Yueling Zhang, Geguang Pu, Jun Sun Sep 2020

Accelerating All-Sat Computation With Short Blocking Clauses, Yueling Zhang, Geguang Pu, Jun Sun

Research Collection School Of Computing and Information Systems

The All-SAT (All-SATisfiable) problem focuses on finding all satisfiable assignments of a given propositional formula, whose applications include model checking, automata construction, and logic minimization. A typical ALL-SAT solver is normally based on iteratively computing satisfiable assignments of the given formula. In this work, we introduce BASOLVER, a backbone-based All-SAT solver for propositional formulas. Compared to the existing approaches, BASOLVER generates shorter blocking clauses by removing backbone variables from the partial assignments and the blocking clauses. We compare BASOLVER with 4 existing ALL-SAT solvers, namely MBLOCKING, BC, BDD, and NBC. Experimental results indicate that although finding all the backbone variables …

Go to article

How (Not) To Find Bugs: The Interplay Between Merge Conflicts, Co-Changes, And Bugs, Luis Amaral, Marcos C. Oliveira, Welder Luz, José Fortes, Rodrigo Bonifacio, Daniel Alencar, Eduardo Monteiro, Gustavo Pinto, David Lo Sep 2020

How (Not) To Find Bugs: The Interplay Between Merge Conflicts, Co-Changes, And Bugs, Luis Amaral, Marcos C. Oliveira, Welder Luz, José Fortes, Rodrigo Bonifacio, Daniel Alencar, Eduardo Monteiro, Gustavo Pinto, David Lo

Research Collection School Of Computing and Information Systems

Context: In a seminal work, Ball et al. [1] investigate if the information available in version control systems could be used to predict defect density, arguing that practitioners and researchers could better understand errors "if [our] version control system could talk". In the meanwhile, several research works have reported that conflict merge resolution is a time consuming and error-prone task, while other contributions diverge about the correlation between co-change dependencies and defect density. Problem: The correlation between conflicting merge scenarios and bugs has not been addressed before, whilst the correlation between co-change dependencies and bug density has been only investigated …

Go to article

Fasts: A Satisfaction-Boosting Bus Scheduling Assistant (Demo), Momo Song, Zhifeng Bao, Baihua Zheng, Zhiyong Peng Sep 2020

Fasts: A Satisfaction-Boosting Bus Scheduling Assistant (Demo), Momo Song, Zhifeng Bao, Baihua Zheng, Zhiyong Peng

Research Collection School Of Computing and Information Systems

In this paper, we demonstrate a satisfaction-boosting bus scheduling assistant called FASTS, which assists users to find an optimal bus schedule. FASTS performs bus scheduling based on the constraints specified by the user in either a coarse-grained or a fine-grained manner, supports different explorations with a varying number of constraints, and provides analysis to quantify the performance of bus schedules and presents the results in a visually pleasing way. We demonstrate FASTS using real-world bus routes (396 routes) and one-week bus touch-on/touch-off records (28 million trip records) in Singapore.

Go to article

Urban Scale Trade Area Characterization For Commercial Districts With Cellular Footprints, Yi Zhao, Zimu Zhou, Xu Wang, Tongtong Liu, Zheng Yang Sep 2020

Urban Scale Trade Area Characterization For Commercial Districts With Cellular Footprints, Yi Zhao, Zimu Zhou, Xu Wang, Tongtong Liu, Zheng Yang

Research Collection School Of Computing and Information Systems

Understanding customer mobility patterns to commercial districts is crucial for urban planning, facility management, and business strategies. Trade areas are a widely applied measure to quantify where the visitors are from. Traditional trade area analysis is limited to small-scale or store-level studies, because information such as visits to competitor commercial entities and place of residence is collected by labour-intensive questionnaires or heavily biased location-based social media data. In this article, we propose CellTradeMap, a novel district-level trade area analysis framework using mobile flow records (MFRs), a type of fine-grained cellular network data. We show that compared to traditional cellular data …

Go to article

Persona Perception Scale: Development And Exploratory Validation Of An Instrument For Evaluating Individuals' Perceptions Of Personas, Joni Salminen, Joao M. Santos, Haewoon Kwak, Jisun An, Soon-Gyo Jung Sep 2020

Persona Perception Scale: Development And Exploratory Validation Of An Instrument For Evaluating Individuals' Perceptions Of Personas, Joni Salminen, Joao M. Santos, Haewoon Kwak, Jisun An, Soon-Gyo Jung

Research Collection School Of Computing and Information Systems

Although used in many domains, the evaluation of personas is difficult due to the lack of validated measurement instruments. To tackle this challenge, we propose the Persona Perception Scale (PPS), a survey instrument for evaluating how individuals perceive personas. We develop the scale by reviewing relevant literature from social psychology, persona studies, and Human-Computer Interaction to find relevant constructs and items for measuring persona perceptions. Following initial pilot testing, we conduct an exploratory validation of the scale with 412 respondents and find that the constructs and items of the scale perform satisfactorily for deployment. The research has implications for both …

Go to article

Smart Contract Repair, Xiao Liang Yu, Omar Al-Bataineh, David Lo, Abhik Roychoudhury Sep 2020

Smart Contract Repair, Xiao Liang Yu, Omar Al-Bataineh, David Lo, Abhik Roychoudhury

Research Collection School Of Computing and Information Systems

Smart contracts are automated or self-enforcing contracts that can be used to exchange assets without having to place trust in third parties. Many commercial transactions use smart contracts due to their potential benefits in terms of secure peer-to-peer transactions independent of external parties. Experience shows that many commonly used smart contracts are vulnerable to serious malicious attacks, which may enable attackers to steal valuable assets of involving parties. There is, therefore, a need to apply analysis and automated repair techniques to detect and repair bugs in smart contracts before being deployed. In this work, we present the first general-purpose automated …

Go to article

An Empirical Study Of The Dependency Networks Of Deep Learning Libraries, Junxiao Han, Shuiguang Deng, David Lo, Chen Zhi, Jianwei Yin, Xin Xia Sep 2020

An Empirical Study Of The Dependency Networks Of Deep Learning Libraries, Junxiao Han, Shuiguang Deng, David Lo, Chen Zhi, Jianwei Yin, Xin Xia

Research Collection School Of Computing and Information Systems

Deep Learning techniques have been prevalent in various domains, and more and more open source projects in GitHub rely on deep learning libraries to implement their algorithms. To that end, they should always keep pace with the latest versions of deep learning libraries to make the best use of deep learning libraries. Aptly managing the versions of deep learning libraries can help projects avoid crashes or security issues caused by deep learning libraries. Unfortunately, very few studies have been done on the dependency networks of deep learning libraries. In this paper, we take the first step to perform an exploratory …

Go to article

Group Instance: Flexible Co-Location Resistant Virtual Machine Placement In Iaas Clouds, Vu Duc Long, Nguyen Binh Duong Ta Sep 2020

Group Instance: Flexible Co-Location Resistant Virtual Machine Placement In Iaas Clouds, Vu Duc Long, Nguyen Binh Duong Ta

Research Collection School Of Computing and Information Systems

This paper proposes and analyzes a new virtual machine (VM) placement technique called Group Instance to deal with co-location attacks in public Infrastructure-as-a-Service (IaaS) clouds. Specifically, Group Instance organizes cloud users into groups with pre-determined sizes set by the cloud provider. Our empirical results obtained via experiments with real-world data sets containing million of VM requests have demonstrated the effectiveness of the new technique. In particular, the advantages of Group Instance are three-fold: 1) it is simple and highly configurable to suit the financial and security needs of cloud providers, 2) it produces better or at least similar performance compared …

Go to article

First Prev Next Last