Remark on Algorithm 992: An OpenGL- and C++-based Function Library for Curve and Surface Modeling in a Large Class of Extended Chebyshev Spaces

We provide a number of corrections to the software component that accompanied this Algorithm submission [3]. An updated version of the code is available from the ACM Collected Algorithms site [1].

A Model-Driven Approach for Solving the Software Component Allocation Problem

The underlying infrastructure paradigms behind the novel usage scenarios and services are becoming increasingly complex—from everyday life in smart cities to industrial environments. Both the number of devices involved and their heterogeneity make the allocation of software components quite challenging. Despite the enormous flexibility enabled by component-based software engineering, finding the optimal allocation of software artifacts to the pool of available devices and computation units could bring many benefits, such as improved quality of service (QoS), reduced energy consumption, reduction of costs, and many others. Therefore, in this paper, we introduce a model-based framework that aims to solve the software component allocation problem (CAP). We formulate it as an optimization problem with either single or multiple objective functions and cover both cases in the proposed framework. Additionally, our framework also provides visualization and comparison of the optimal solutions in the case of multi-objective component allocation. The main contributions introduced in this paper are: (1) a novel methodology for tackling CAP-alike problems based on the usage of model-driven engineering (MDE) for both problem definition and solution representation; (2) a set of Python tools that enable the workflow starting from the CAP model interpretation, after that the generation of optimal allocations and, finally, result visualization. The proposed framework is compared to other similar works using either linear optimization, genetic algorithm (GA), and ant colony optimization (ACO) algorithm within the experiments based on notable papers on this topic, covering various usage scenarios—from Cloud and Fog computing infrastructure management to embedded systems, robotics, and telecommunications. According to the achieved results, our framework performs much faster than GA and ACO-based solutions. Apart from various benefits of adopting a multi-objective approach in many cases, it also shows significant speedup compared to frameworks leveraging single-objective linear optimization, especially in the case of larger problem models.

Discriminating features-based cost-sensitive approach for software defect prediction

Correlated quality metrics extracted from a source code repository can be utilized to design a model to automatically predict defects in a software system. It is obvious that the extracted metrics will result in a highly unbalanced data, since the number of defects in a good quality software system should be far less than the number of normal instances. It is also a fact that the selection of the best discriminating features significantly improves the robustness and accuracy of a prediction model. Therefore, the contribution of this paper is twofold, first it selects the best discriminating features that help in accurately predicting a defect in a software component. Secondly, a cost-sensitive logistic regression and decision tree ensemble-based prediction models are applied to the best discriminating features for precisely predicting a defect in a software component. The proposed models are compared with the most recent schemes in the literature in terms of accuracy, area under the curve, and recall. The models are evaluated using 11 datasets and it is evident from the results and analysis that the performance of the proposed prediction models outperforms the schemes in the literature.

Software Metrics for Reusability of Component Based Software System: A Review

Component Based Software System (CBSS) have become most generalized and popular approach for developing reusable software applications. A software component has different important factors, but reusability is the most citing factor of any software component. Software components can be reused for the development of another software application, which further reduces the amount of time and effort of software development process. With the increase in the number of software components, requirement for identification of software metrics also increased for quantitative analysis of different aspects of components. Reusability depends on different factors and these factors have different impact on the reusability of software components. In this paper, study has been performed to identify the major reusability factors and software metrics for measuring those factors. From this research work, it will become easier to measure the reusability of software components, and software developers would be able to measure the degree of various features of any application which can be reused for developing other software applications. In this way, it would be easy and convenient to identify and compare the reusable software components and they could be reused in effective and efficient manner.