Abstract

Large organizations have diverse product offerings to meet various business needs. To increase revenue, its common these days to offer software products as integrated product suite (s) rather than individual products. Creating and maintaining high quality software products within the integrated product suite requires rigorous product engineering methods. The sheer size of products and dependencies involved tend to raise unidentified defects that may become critical post product upgrades or after every release cycle. It is difficult to track such defects and its widespread across underlying sub-products. In this paper, we present a model based approach to study the defect dependency in large scale integrated software products to avoid surprise defects after product release. To validate the approach, we have applied it on some pilot projects in industry.

Abstract

Bug tracking tools are vital for managing bugs in any open source as well as proprietary commercial projects. Considering the significance of using an appropriate bug tracking tool, we assess the features offered by 31 open source bug tracking tools and their significance of usage in open source projects. We have categorized these tools into different classes based on their features. We have also conducted a developer survey by working with open source software practitioners to understand the effectiveness of these tools in their day-to-day software development. We also explored StackOverFlow-a developer Q & A forum to understand the developer experiences and challenges while using open source bug tracking tools. Our observations generated encouraging results that can used as a recommendation guide for open source software community to choose the best bug tracking tool based on their functional needs. Additionally, we have identified few features that are needed but not offered by most of these bug tracking tools.

Abstract

1Ontologies have emerged as a common way of representing knowledge. Recently, people with minimal domain background or ontology engineering are developing ontologies, leading to a corpus of informal and under-evaluated ontologies. Existing ontology evaluation approaches require rigorous application of formal methods and knowledge of domain experts that can be cumbersome or tedious. We propose a lightweight approach for evaluating sufficiency of ontologies based on Natural Language Processing techniques. The approach consists of verifying the extent of coverage of concepts and relationships of ontologies against words in domain corpus. As a case study, we applied our approach to evaluate sufficiency of ontology in two example domains - Education (Curriculum) and Security (Phishing). We show that our approach yields promising results, is less effort intensive and is comparable with existing evaluation methods.

Abstract

Hines matrices arise in the simulations of mathematical models describing initiation and propagation of action potentials in a neuron. In this work, we exploit the structural properties of Hines matrices and design a scalable, linear work, recursive parallel algorithm for solving a system of linear equations where the underlying matrix is a Hines matrix, using the Exact Domain Decomposition Method (EDD). We give a general form for representing a Hines matrix and use the general form to prove that the intermediate matrix obtained via the EDD has the same structural properties as that of a Hines matrix. Using the above observation, we propose a novel decomposition strategy called fine decomposition which is suitable for a GPU architecture. Our algorithmic approach R-FINE-TPT based on fine decomposition outperforms the previously known approach in all the cases and gives a speedup of 2.5x on average for a variety of input neuron morphologies. We further perform experiments to understand the behaviour of R-FINE-TPT approach and show its robustness. We also employ a machine learning technique called linear regression to effectively guide recursion in our algorithm.

Abstract

Motivated by the increasing importance of edge computing and improvements in heterogeneous low-power, wimpy systems, we present a novel time-energy-cost analysis of wimpy edge computing in comparison with traditional brawny cloud computing. For this analysis, we use a brawny heterogeneous Amazon EC2 cloud instance with GPU and two wimpy heterogeneous systems represented by Jetson TK1 and Jetson TX1. As the paradigm shift to edge computing is due to the challenges of Big Data processing, we select six representative MapReduce applications with applicability in IoT edge computing. Using our time-energy-cost analysis of both wimpy edge compute nodes and cloud computing systems with GPUs, we present several key insights. Firstly, we advocate the usage of heterogeneous systems with GPU on both edge and cloud since they provide time-energy savings of up to 70% for compute-intensive applications. Secondly, we establish an equivalence ratio between one brawny cloud instance and multiple wimpy edge nodes that achieve the same or better time performance. Based on this equivalence ratio, we show that using wimpy systems as edge computing devices saves cost compared to using traditional cloud computing. Lastly, counter-to-intuition, our analysis shows that the latest Jetson TX1 system exhibits worse time-cost performance compared to the older Jetson TK1 system. This result stems from lower operating core clock frequency and lower instructions-per-cycle of Jetson TX1's GPU on some compute-intensive applications.

Abstract

With an increasing number of cybersecurity attacks due to insider threats, it is important to identify different attack mechanisms and quantify them to ease threat mitigation. We propose a discrete-event simulation model to study the impact of unintentional insider threats on the overall system security by representing time-varying human behavior using two parameters, user vulnerability and user interactions. In addition, the proposed approach determines the futuristic impact of such behavior on overall system health. We illustrate the ease of applying the proposed simulation model to explore several ”what-if” analysis for an example enterprise system and derive the following useful insights, (i) user vulnerability has a bigger impact on overall system health compared to user interactions, (ii) the impact of user vulnerability depends on the system topology, and (ii) user interactions increases the overall system vulnerability due to the increase in the number of attack paths via credential leakage.

Abstract

The transition to a “smart city” necessitates an increase in interdependencies between critical infrastructures and information technologies. Moreover, such interdependencies are across multiple domains. However, these interdependencies expose critical infrastructures to cybersecurtiy threats. Furthermore, the availability of domain-specific simulators everywhere motivates the need for federation of interoperable cybersecurity and cyberphysical testbeds to validate cybersecurity threat resiliency. This paper presents some key issues and challenges in accomplishing such a federation of testbeds. While there are multiple modeling and simulation approaches in specific domains, none of these works address the challenges of federating across multiple domains such as federation between cyberphysical testbed and cybersecurity testbed to enable validation of cybersecurity resilience. We outline a reference architecture, DEFT (feDerate tEstbeds For cybersecuriTy) with design considerations that stem from the key issues highlighted.

Abstract

In this paper, we consider a wireless co-operative network involving two transceiver nodes whose communication is aided by energy-constrained two-way relay nodes. Each relay node employs a non-regenerative relaying protocol. The transceiver nodes can simultaneously transmit information and energy and the relay nodes harvest the energy from the received signal and use the harvested energy to amplify and retransmit the signal to the destination. The relay nodes implement power- splitting (PS) scheme to apportion the received signal power to the information processing and the energy harvesting units. We address the problem of sum-rate maximization under constraints on total transmit power and harvested energy. We propose an optimal resource allocation and relay selection scheme as well as a reduced-complexity suboptimal scheme. The performance of the proposed solutions are illustrated by numerical simulations.

Abstract

In this paper, we consider a cooperative wireless network involving two transceiver nodes whose communication is assisted by energy-constrained two-way amplify-and-forward (AF) relay nodes. The transceiver nodes can simultaneously transmit information and energy, and the relay nodes harvest the energy from the received signal and use the harvested energy to amplify and retransmit the received signal to the transceiver nodes. We consider a power splitting-based relaying (PSR) protocol as well as a time switching-based relaying (TSR) protocol to effect simultaneous extraction of information and harvesting of energy at the relay. We address the problem of sum-rate maximization under constraints on the total transmit power and the harvested energy in both scenarios. In order to solve this problem, we propose an optimal resource allocation and relay selection scheme. The performance of the proposed PSR and TSR protocols is illustrated by numerical simulations.

Abstract

In this paper, we consider the problem of hybrid multi-input multi-output (MIMO) transceiver design for a non regenerative hybrid two-way amplify-forward (AF) relay based millimeter wave (mmWave) communication system. We propose two low complexity mmWave system designs based on the quality of available channel state information (CSI). We first propose the design of transceivers and a two- way AF relay, by minimizing sum-mean square error (SMSE) under the constraint of total relay transmit power while assuming the perfect channel knowledge. We later extend it to a robust design under the assumption that the transceivers posses only imperfect information about the channel state, where the CSI error is assumed to follow the Gaussian distribution. In both the designs, reduced hardware complexity (introduced due to large number of antenna elements in mmWave system) is achieved by analog-digital hybrid processing by using orthogonal matching pursuit (OMP)-based sparse signal processing. We present numerical results on the performance of both the designs over various dictionaries. The comparison results show that robust design is resilient to the presence of CSI errors. Furthermore, we also demonstrate the convergence of both the proposed algorithms to a limit even though global convergence is hard to prove due to non convex nature of overall optimization problem.