Component Level Metrics Research Articles

Multi-Objective Application-Driven Approximate Design Method

Approximate Computing (AxC) paradigm aims at designing computing systems that can satisfy the rising performance demands and improve the energy efficiency. AxC exploits the gap between the level of accuracy required by the users, and the actual precision provided by the computing system, for achieving diverse optimizations. Various AxC techniques have been proposed so far in the literature at different abstraction levels from hardware to software. These techniques have been successfully utilized and combined to realize approximate implementations of applications in various domains (e.g. data analytic, scientific computing, multimedia and signal processing, and machine learning). Unfortunately, state-of-the-art approximation methodologies focus on a single abstraction level, such as combining elementary components (e.g., arithmetic operations) which are firstly approximated using component-level metrics and then combined to provide a good trade-off between efficiency and accuracy at the application level. This hinders the possibility for designers to explore different approximation opportunities, optimized for different applications and implementation targets. Therefore, we designed and implemented E- \mathbb IDEA, an automatic framework that provides an application-driven approximation approach to find the best approximate versions of a given application targeting different implementations (i.e., hardware and software). E- \mathbb IDEA compounds 1) a source-to-source manipulation tool and 2) an evolutionary search engine to automatically realize approximate application variants and perform a Design-Space Exploration (DSE). The latter results in a set of non-dominate approximate solutions in terms of trade-off between accuracy and efficiency. Experimental results validate the effectiveness and the flexibility of the approach in generating optimized approximate implementations of different applications, by using different approximation techniques and different accuracy/error metrics and for different implementation targets.

Probabilistic seismic assessment of non-ductile RC buildings retrofitted using pre-tensioned aramid fiber reinforced polymer belts

The current manuscript presents a probabilistic seismic assessment of non-ductile RC structures retrofitted by pre-tensioned aramid fiber reinforced polymers (AFRP). Three RC buildings with different heights (4-, 6- and 8-stories) are designed according to older construction practice and the poorly detailed columns of each model are then retrofitted using pre-tensioned AFRP belts. The numerical finite element models are developed in OpenSees using concentrated plastic hinge models that can capture shear weakness of original columns and deterioration of beams’ stiffness and strength. Incremental dynamic and nonlinear static analyses are performed to quantify structures’ performance in terms of both global- and component-level metrics. The structures’ global response is evaluated using fragility curves, mean annual frequency of collapse, and collapse margin ratios. Furthermore, statistical analyses are performed to obtain median inter-story drift distribution along the height of the structure, structural members’ ductility and dissipated energy under three different seismic hazard levels. The results indicate that retrofitting by pre-tensioned AFRP improves the structure’s global-level ductility and reduces the collapse probability significantly. Moreover, it can prevent weak story formation by engaging a larger number of stories in collapse mechanism. From a component-level perspective, pre-tensioned AFRP increases columns’ ductility and dissipated energy and enhances their performance, particularly at near-collapse-limit states.

Supporting the analyzability of architectural component models - empirical findings and tool support

This article discusses the understandability of component models that are frequently used as central views in architectural descriptions of software systems. We empirically examine how different component level metrics and the participants’ experience and expertise can be used to predict the understandability of those models. In addition, we develop a tool that supports applying the obtained empirical findings in practice. Our results show that the prediction models have a large effect size, which means that their prediction strength is of high practical significance. The participants’ experience plays an important role in the prediction but the obtained models are not as accurate as the models that use the component level metrics. The developed tools combine the DSL-based architecture abstraction approach with the obtained empirical findings. While the DSL-based architecture abstraction approach enables software architects to keep source code and architecture consistent, the metrics extensions enable them, while working with the DSL, to continuously judge and improve the analyzability of architectural component models based on the understandability of their individual components they create with the DSL. Provided metrics extensions can also help in assessing how much each architectural rule used to specify the DSL affects the understandability of a component which enables for instance finding the rules that contribute the most to a limited understandability. Finally, our approach supports change impact analysis, i.e., the identification of changes that affect different analyzability levels of the component models. We studied the applicability of our approach in a case study of an existing open source system.