Abstract

Researchers have supported the idea of gamification to enhance students' interest in activities like code reviews, change management, knowledge management, issue tracking, etc. which might otherwise be repetitive and monotonous. We performed an experimental study consisting of nearly 180+ participants to measure the impact of gamification on code review process using 5 different code review tools, including one gamified code review instance from our extensible architectural framework. We assess the impact of gamification based on the code smells and bugs identified in a gamified and non-gamified environment as per code inspection report. Further, measurement and comparison of the quantity and usefulness of code review comments was done using machine learning techniques.

Abstract

In India, a large number of engineering undergraduates are adopting eLearning as it provides access to best faculty and reduces concerns on inadequate physical infrastructure at colleges. Virtual Labs is a Government of India eLearning initiative containing simulation and remote triggered labs for engineering students. Virtual Labs developed over a period of 6 years is used by more than a million undergraduate students across nine engineering disciplines. The software used for developing these experiments requires substantial effort for maintenance due to deprecation, compatibility, etc. We propose a targeted crowdsourcing approach for maintenance of Virtual Labs with sustainable quality. The targeted crowdsourcing involves the large number of engineering students who are also the major stakeholders of these labs. Our quality enhancement using crowdsourcing approach was validated for 14 labs and …

Abstract

In this paper, we present a novel approach to extend the concepts of time sharing and preemption to GPGPU computational space. Our technique is applicable to any batch GPGPU program written in Compute Unified Device Architecture (CUDA) API provided for C/C++ programming languages. It also gives the user freedom to use different scheduling algorithms to schedule the GPGPU programs satisfying specific system level agreements. Our easy-to-use and minimal API makes it very easy for users to run their programs using the GPUScheduler.

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.