Abstract

Refactoring is a set of techniques used to enhance the quality of code by restructuring existing code/design without changing its behavior. Refactoring tools can be used to detect specific code smells, propose relevant refactorings, and in some cases automate the refactoring process. However, usage of refactoring tools in industry is still relatively low. One of the major reasons being the veracity of the detected code smells, especially smells that aren’t purely syntactic in nature. We conduct an empirical study on some refactoring tools and evaluate the correctness of the code smells they identify. We analyze the level of confidence users have on the code smells detected by the tools and discuss some issues with such tools.

Abstract

Product based organizations have diverse product offerings that meet various business needs. The products are in turn integrated to create integrated product suites. Rigorous product engineering is a must for creation of high quality integrated software products. Adequate measures must be taken to improve quality of the integrated product before every release of its module or sub-product. It is hard to imagine upgrading an integrated software product with unidentified defects prior to its release. In this paper, we share our observations on implementing a defect dependency metric to identify the dependency of a defect over a real-time industry defect dataset of an integrated software product. This defect dependency metric was captured and analyzed during release cycle (s) to avoid surprise issues post product launch.

Abstract

Motivated by the explosion of Big Data analytics, performance improvements in low-power (wimpy) systems and the increasing energy efficiency of GPUs, this paper presents a time–energy performance analysis of MapReduce on heterogeneous systems with GPUs. We evaluate the time and energy performance of three MapReduce applications with diverse resource demands on a Hadoop–CUDA framework. As executing these applications on heterogeneous systems with GPUs is challenging, we introduce a novel lazy processing technique which requires no modifications to the underlying Hadoop framework. To analyze the impact of heterogeneity, we compare the heterogeneous CPU+GPU with the homogeneous CPU-only execution across three systems with diverse characteristics, (i) a traditional high-performance (brawny) Intel i7 system hosting a discrete 640-core Nvidia GPU of the latest Maxwell generation, (ii) a wimpy platform consisting of a quad-core ARM Cortex-A9 hosting the same discrete Maxwell GPU, and (iii) a wimpy platform integrating four ARM Cortex-A15 cores and 192 Nvidia Kepler GPU cores on the same chip. These systems encompass both intra-node heterogeneity with discrete GPUs and intra-chip heterogeneity with integrated GPUs. Our measurement-based performance analysis highlights the following results. For compute-intensive workloads, the brawny heterogeneous system achieves speedups of up to 2.3 and reduces the energy usage by almost half compared to the brawny homogeneous system. As expected, for applications where data transfers dominate the execution time, heterogeneity exhibits worse time–energy performance compared to homogeneous systems. For such applications, the heterogeneous wimpy A9 system with discrete GPU uses around 14 times the energy of homogeneous A9 system due to both system resource imbalances and high power overhead of the discrete GPU. However, comparing among heterogeneous systems, the wimpy A15 with integrated GPU uses the lowest energy across all workloads. This allows us to establish an execution time equivalence ratio between a single brawny node and multiple wimpy nodes. Based on this equivalence ratio, the wimpy nodes exhibit energy savings of two-thirds while maintaining the same execution time. This result advocates the potential usage of heterogeneous wimpy systems with integrated GPUs for Big Data analytics.

Abstract

Hybrid programming model is becoming increasingly popular for HPC applications as it has the dual-advantage of exploiting inter-node distributed-memory scalability and intra-node shared-memory performance in a cluster system. One of the key challenges for energy efficient execution of hybrid programs is to determine time and energy efficient hardware configurations among a large system configuration space. Given a hybrid program with an execution time deadline and an energy budget, we propose a measurement-based analytical modelling approach to determine these system configurations. In contrast to current approaches, we model both inter and intra-node resource overlaps, memory contention among cores within a node and network contention across multiple nodes. The model invalidated against direct measurement using five representative HPC applications on Intel Xeon and ARM clusters having diverse time-energy performance. We show that a Pareto frontier consisting of optimal configurations exist for a hybrid program running on homogeneous clusters. To further optimize the Pareto frontier, we introduce a new metric, useful computation ratio (UCR) to quantify the degree of resource contentions and communication overheads in an execution. We discuss how UCR and Pareto-optimal configurations can be used in conjunction by system's designers to gain further insights into system resource imbalances, and how application developers can further fine-tune their hybrid programs

Abstract

Purpose: To model the modifiable risk factors by using the logistic regression (LR) and artificial neural network (ANN) models for prediction of progression of Age-related Macular Degeneration (AMD) and cross-validate these models for their predictive accuracies in a population in South India. Methods: The data (N= 3,723) were analyzed from Andhra Pradesh Eye Disease Study (APEDS) on participants aged≥ 40 years. Sub-population data from this sample were drawn by using Random under Sampling (RUS)(n= 213) and combination of RUS and Random over Sampling (ROS)(n= 1420) techniques. The modifiable and non-modifiable risk factors which were elicited as part of the study were used to derive the LR based risk score models and the model fit was assessed using bootstrap method for internal validity. The ANN model was built for three sets of data using the multi-layer feed-forward back propagation …

Abstract

Extreme learning machine (ELM) as a new learning approach has shown its good generalization performance in regression and classification applications. Clustering analysis is an important tool to explore the structure of data and has been employed in many disciplines and applications. In this paper, we present a method that builds on ELM projection of input data into a high-dimensional feature space and followed by unsupervised clustering using artificial bee colony (ABC) algorithm. While ELM projection facilitates separability of clusters, a metaheuristic technique such as ABC algorithm overcomes problems of dependence on initialization of cluster centers and convergence to local minima suffered by conventional algorithms such as K-means. The proposed ELM-ABC algorithm is tested on 12 benchmark data sets. The experimental results show that the ELM-ABC algorithm can effectively improve the …

Abstract

Voltage noise can lead to various errors such as dynamic and permanent, which are directly associated to circuit-level reliability issues. Variability in process parameters directly affects the probability of failures associated to voltage noise. Yet, the evaluation of the probability of failures by SPICE level Monte Carlo simulation is prohibitively time-consuming. This work proposes a technique to characterize the input noise and process variations in order to estimate failure probability in a logic circuit path composed of combinational cells and registers. The method allows to correctly estimate the order of magnitude of the probability of failures and to evidence the influence of process variations, while reaching >104 speedup versus SPICE.

Abstract

Aging phenomena, on top of process variations along with temperature and supply voltage variations, translate into complex degradation effects on the required performance and yield of nanoscale circuits. The proposed paper focuses on the development of mathematically optimal circuit sizing for yield maximization on the case study of a CMOS full adder circuit. The final cell design is robust against NBTI aging effects, impact of statistical (global and mismatch) and operating variation of temperature and supply voltage. Monte Carlo analysis has been carried out to verify the estimated yields. The demonstrated technique can be extended to a library of optimally designed digital cells

Abstract

Perception of temporal duration is subjective and is influenced by factors such as attention and context. For example, unexpected or emotional events are often experienced as if time subjectively expands, suggesting that the amount of information processed in a unit of time can be increased. Time dilation effects have been measured with an oddball paradigm in which an infrequent stimulus is perceived to last longer than standard stimuli in the rest of the sequence. Likewise, time compression for the oddball occurs when the duration of the standard items is relatively brief. Here, we investigated whether the amount of information processing changes when time is perceived as distorted. On each trial, an oddball stimulus of varying numerosity (1–14 items) and duration was presented along with standard items that were either short (70 ms) or long (1050 ms). Observers were instructed to count the number of dots within the oddball stimulus and to judge its relative duration with respect to the standards on that trial. Consistent with previous results, oddballs were reliably perceived as temporally distorted: expanded for longer standard stimuli blocks and compressed for shorter standards. The occurrence of these distortions of time perception correlated with perceptual processing; i.e. enumeration accuracy increased when time was perceived as expanded and decreased with temporal compression. These results suggest that subjective time distortions are not epiphenomenal, but reflect real changes in sensory processing. Such short-term plasticity in information processing rate could be evolutionarily advantageous in optimizing perception and action …

Abstract

This study reports dynamic functional network connectivity (dFNC) analysis on time courses of putative empathy networks—cognitive, emotional, and motor—and the default mode network (DMN) identified from independent components (ICs) derived by the group independent component analysis (ICA) method. The functional magnetic resonance imaging (fMRI) data were collected from 15 subjects watching movies of three genres, an animation (S1), Indian Hindi (S2), and a Hollywood English (S3) movie. The hypothesis of the study is that empathic engagement in a movie narrative would modulate the activation with the DMN. The clippings were individually rated for emotional expressions, context, and empathy self-response by the fMRI subjects post scanning and by 40 participants in an independent survey who rated at four time intervals in each clipping. The analysis illustrates the following: (a) the ICA method