Software Engineering Commons^™

Dynamic Data Race Detection And Healing, Du Li

School of Computing: Dissertations, Theses, and Student Research

Perpetual availability is an important operational goal in today's computer systems. However, achieving this goal is challenging because modern software systems contain faults that can cause them to fail. For example, multi-threading is widely used in modern software to fully utilize the computing capability of multicore processors. However, employing multi-threading can lead to concurrency faults such as deadlock and data race that are notoriously difficult to to isolate, detect, and repair.Data races, which involves two concurrent accesses to the same data where at least one is a write, are the most common concurrency faults.

As our first step, we investigate …

Hardware-Software Co-Design, Acceleration And Prototyping Of Control Algorithms On Reconfigurable Platforms, Desta Kumsa Edosa

UNLV Theses, Dissertations, Professional Papers, and Capstones

Differential equations play a significant role in many disciplines of science and engineering. Solving and implementing Ordinary Differential Equations (ODEs) and partial Differential Equations (PDEs) effectively are very essential as most complex dynamic systems are modeled based on these equations. High Performance Computing (HPC) methodologies are required to compute and implement complex and data intensive applications modeled by differential equations at higher speed. There are, however, some challenges and limitations in implementing dynamic system, modeled by non-linear ordinary differential equations, on digital hardware. Modeling an integrator involves data approximation which results in accuracy error if data values are not considered …

Fame, Soft Flock Formation Control For Collective Behavior Studies And Rapid Games Development, Choon Sing Ho, Yew-Soon Ong, Xianshun Chen, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

We present FAME, a comprehensive C# software library package providing soft formation control for large flocks of agents. While many existing available libraries provide means to create flocks of agent equipped with simple steering behavior, none so far, to the best of our knowledge, provides an easy and hassle free approach to control the formation of the flock. Here, besides the basic flocking mechanisms, FAME provides an extensive range of advanced features that gives enhanced soft formation control over multiple flocks. These soft formation features include defining flocks in any user-defined formation, automated self-organizing agent within formation, manipulating formation shape …

Semi-Automated Verification Of Defense Against Sql Injection In Web Applications, Kaiping Liu, Hee Beng Kuan Tan, Lwin Khin Shar

Research Collection School Of Computing and Information Systems

Recent reports reveal that majority of the attacks to Web applications are input manipulation attacks. Among these attacks, SQL injection attack malicious input is submitted to manipulate the database in a way that was unintended by the applications' developers is one such attack. This paper proposes an approach for assisting to code verification process on the defense against SQL injection. The approach extracts all such defenses implemented in code. With the use of the proposed approach, developers, testers or auditors can then check the defenses extracted from code to verify their adequacy. We have evaluated the feasibility, effectiveness, and usefulness …

Ibinhunt: Binary Hunting With Inter-Procedural Control Flow, Jiang Ming, Meng Pan, Debin Gao

Research Collection School Of Computing and Information Systems

Techniques have been proposed to find the semantic differences between two binary programs when the source code is not available. Analyzing control flow, and in particular, intra-procedural control flow, has become an attractive technique in the latest binary diffing tools since it is more resistant to syntactic, but non-semantic, differences. However, this makes such techniques vulnerable to simple function obfuscation techniques (e.g., function inlining) attackers any malware writers could use. In this paper, we first show function obfuscation as an attack to such binary diffing techniques, and then propose iBinHunt which uses deep taint and automatic input generation to find …

Diffusion Of Software Features: An Exploratory Study, Ferdian Thung, David Lo, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

New features are frequently proposed in many software libraries. These features include new methods, classes, packages, etc. These features are utilized in many open source and commercial software systems. Some of these features are adopted very quickly, while others take a long time to be adopted. Each feature takes much resource to develop, test, and document. Library developers and managers need to decide what feature to prioritize and what to develop next. As a first step to aid these stakeholders, we perform an exploratory study on the diffusion or rate of adoption of features in Java Development Kit (JDK) library. …

Scalable Malware Clustering Through Coarse-Grained Behavior Modeling, Mahinthan Chandramohan, Hee Beng Kuan Tan, Lwin Khin Shar

Research Collection School Of Computing and Information Systems

Anti-malware vendors receive several thousand new malware (malicious software) variants per day. Due to large volume of malware samples, it has become extremely important to group them based on their malicious characteristics. Grouping of malware variants that exhibit similar behavior helps to generate malware signatures more efficiently. Unfortunately, exponential growth of new malware variants and huge-dimensional feature space, as used in existing approaches, make the clustering task very challenging and difficult to scale. Furthermore, malware behavior modeling techniques proposed in the literature do not scale well, where malware feature space grows in proportion with the number of samples under examination. …

Action Disambiguation Analysis Using Normalized Google-Like Distance Correlogram, Qianru Sun, Hong Liu

Research Collection School Of Computing and Information Systems

Classifying realistic human actions in video remains challenging for existing intro-variability and inter-ambiguity in action classes. Recently, Spatial-Temporal Interest Point (STIP) based local features have shown great promise in complex action analysis. However, these methods have the limitation that they typically focus on Bag-of-Words (BoW) algorithm, which can hardly discriminate actions’ ambiguity due to ignoring of spatial-temporal occurrence relations of visual words. In this paper, we propose a new model to capture this contextual relationship in terms of pairwise features’ co-occurrence. Normalized Google-Like Distance (NGLD) is proposed to numerically measuring this co-occurrence, due to its effectiveness in semantic correlation analysis. …

Automatic Generation Of Provably Correct Embedded Systems, Shang-Wei Lin, Yang Liu, Pao-Ann Hsiung, Jun Sun, Jin Song Dong

Research Collection School Of Computing and Information Systems

With the demand for new and complicated features, embedded systems are becoming more and more difficult to design and verify. Even if the design of a system is verified, how to guarantee the consistency between the design and its implementation remains a big issue. As a solution, we propose a framework that can help a system designer to model his or her embedded system using a high-level modeling language, verify the design of the system, and automatically generate executable software codes whose behavior semantics are consistent with that of the high-level model. We use two case studies to demonstrate the …

An Empirical Study Of Bugs In Machine Learning Systems, Ferdian Thung, Shaowei Wang, David Lo, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

Many machine learning systems that include various data mining, information retrieval, and natural language processing code and libraries have being used in real world applications. Search engines, internet advertising systems, product recommendation systems are sample users of these algorithm intensive code and libraries. Machine learning code and toolkits have also been used in many recent studies on software mining and analytics that aim to automate various software engineering tasks. With the increasing number of important applications of machine learning systems, the reliability of such systems is also becoming increasingly important. A necessary step for ensuring reliability of such systems is …

Tournament-Based Teaching, Shannon Christopher Boesch, Sandra Boesch

Research Collection School Of Computing and Information Systems

Over the past two years we have collaborated to develop a process and set of online games to enable additional feedback to both students and instructors in a classroom setting. We have named the resulting process Tournament-based Teaching due to the extensive use of tournament-based feedback for groups and individuals throughout course delivery. Tournament-based Teaching enables individualized and peer-based learning in a classroom setting and provides additional motivation for students to prepare for classroom sessions. It also provides feedback to instructors, which can be leveraged to provide better schedule classroom sessions.

More Anti-Chain Based Refinement Checking, Ting Wang, Songzheng Song, Jun Sun, Yang Liu, Jin Song Dong, Xinyu Wang, Shanping Li

Research Collection School Of Computing and Information Systems

Refinement checking plays an important role in system verification. It establishes properties of an implementation by showing a refinement relationship between the implementation and a specification. Recently, it has been shown that anti-chain based approaches increase the efficiency of trace refinement checking significantly. In this work, we study the problem of adopting anti-chain for stable failures refinement checking, failures-divergence refinement checking and probabilistic refine checking (i.e., a probabilistic implementation against a non-probabilistic specification). We show that the first two problems can be significantly improved, because the state space of the product model may be reduced dramatically. Though applying anti-chain for …

An Analytical And Experimental Comparison Of Csp Extensions And Tools, Ling Shi, Yang Liu, Jun Sun, Jin Song Dong, Gustavo Carvalho

Research Collection School Of Computing and Information Systems

Communicating Sequential Processes (CSP) has been widely applied to modeling and analyzing concurrent systems. There have been considerable efforts on enhancing CSP by taking data and other system aspects into account. For instance, CSP M combines CSP with a functional programming language whereas CSP# integrates high-level CSP-like process operators with low-level procedure code. Little work has been done to systematically compare these CSP extensions, which may have subtle and substantial differences. In this paper, we compare CSP M and CSP# not only on their syntax, but also operational semantics as well as their supporting tools such as FDR, ProB, and …

Poster Abstract: Towards Crowd-Aware Sensing Platform For Metropolitan Environments, Saumay Pushp, Chulhong Min, Youngki Lee, Chi Harold Liu, Junehwa Song

Research Collection School Of Computing and Information Systems

In this paper, we propose an in-situ Crowd-aware Sensing Platform, called "CrowdMon", which envisions the cooperation among mobile users in highly crowded urban areas such as metro and square. CrowdMon establishes a spontaneous connection from co-located users in a semantic proximity and enables them to share contextual information such as location, ambient music, and mood of places. To the best of our knowledge, CrowdMon is the first attempt to support crowd-aware services at a platform level. We show interesting use cases of CrowdMon and an initial system design to realize the crowd-based context sharing.

Apps And Apple Devices: Productive Icloud Uses, Cyrus Ford Zarganj

Library Faculty Presentations

This program introduces useful apps for librarians who have two or more Apple devices such as an iPhone, iPad, or MacBook. You can learn how to use the iCloud, sync devices together, and about other useful apps for librarians to work on their projects from different places with different Apple devices without carrying files.

Emulation-Based Software Protection, William B. Kimball, Rusty O. Baldwin

AFIT Patents

A method of emulation-based page granularity code signing comprising the steps of: copying guest operating system instructions and associated hash message authentication codes and/or digital signatures of each guest operating instruction from an untrusted guest operating system memory into a trusted host operating system memory; recomputing the hash message authentication codes using a secret key in the trusted host operating system memory; maintaining the secret key in the trusted host operating system memory and inaccessible by the untrusted guest operating system instructions; translating each guest operating system instruction that has a valid hash message authentication code to a set of …

A Conceptual Framework For Measuring The Quality Aspect Of Mobile Learning, Luiz Fernando Capretz, Abdalha Ali, Abdelkader Ouda

Electrical and Computer Engineering Publications

With the continuing growth of mobile phones outpacing that of desktops and laptops, mobile phones are the new personal computer. These devices started becoming increasingly sophisticated and extremely powerful. In addition to making phone calls it have the capabilities to perform variety functions, one of the most important functions using it as a classroom tool. However, mobile phone applications must be designed and developed with respect to different technologies skills, learning capabilities and language proficiency in order to be accepted by wide audiences (e.g., students, instructors). However, there is much work that has been done to measure the quality of …

Neural Modeling Of Episodic Memory: Encoding, Retrieval, And Forgetting, Wenwen Wang, Budhitama Subagdja, Ah-Hwee Tan, Janusz A. Starzyk

Research Collection School Of Computing and Information Systems

This paper presents a neural model that learns episodic traces in response to a continuous stream of sensory input and feedback received from the environment. The proposed model, based on fusion Adaptive Resonance Theory (fusion ART) network, extracts key events and encodes spatio-temporal relations between events by creating cognitive nodes dynamically. The model further incorporates a novel memory search procedure, which performs parallel search of stored episodic traces continuously. Combined with a mechanism of gradual forgetting, the model is able to achieve a high level of memory performance and robustness, while controlling memory consumption over time. We present experimental studies, …

Model Checking Software Architecture Design, Jiexin Zhang, Yang Liu, Jing Sun, Jin Song Dong, Jun Sun

Research Collection School Of Computing and Information Systems

Software Architecture plays an essential role in the high level description of a system design. Despite its importance in the software engineering practice, the lack of formal description and verification support hinders the development of quality architectural models. In this paper, we present an automated approach to the modeling and verification of software architecture designs using the Process Analysis Toolkit (PAT). We present the formal syntax of the Wright# architecture description language together with its operational semantics in Labeled Transition System (LTS). A dedicated model checking module for Wright# is implemented in the PAT verification framework based on the proposed …

Automatic Defect Categorization, Ferdian Thung, David Lo, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

Defects are prevalent in software systems. In order to understand defects better, industry practitioners often categorize bugs into various types. One common kind of categorization is the IBM’s Orthogonal Defect Classification (ODC). ODC proposes various orthogonal classification of defects based on much information about the defects, such as the symptoms and semantics of the defects, the root cause analysis of the defects, and many more. With these category labels, developers can better perform post-mortem analysis to find out what the common characteristics of the defects that plague a particular software project are. Albeit the benefits of having these categories, for …