A novel composite model approach to improve software quality prediction

Context:How can quality of software systems be predicted before deployment? In attempting to answer this question, prediction models are advocated in several studies. The performance of such models drops dramatically, with very low accuracy, when they are used in new software development environments or in new circumstances.ObjectiveThe main objective of this work is to circumvent the model generalizability problem. We propose a new approach that substitutes traditional ways of building prediction models which use historical data and machine learning techniques.MethodIn this paper, existing models are decision trees built to predict module fault-proneness within the NASA Critical Mission Software. A genetic algorithm is developed to combine and adapt expertise extracted from existing models in order to derive a “composite” model that performs accurately in a given context of software development. Experimental evaluation of the approach is carried out in three different software development circumstances.ResultsThe results show that derived prediction models work more accurately not only for a particular state of a software organization but also for evolving and modified ones.ConclusionOur approach is considered suitable for software data nature and at the same time superior to model selection and data combination approaches. It is then concluded that learning from existing software models (i.e., software expertise) has two immediate advantages; circumventing model generalizability and alleviating the lack of data in software-engineering.     

Design and optimization of LNA topologies with image rejection filters

An important problem in designing RFIC in CMOS technology is the parasitic elements of passive and active devices that complicate design calculations. This article presents three LNA topologies including cascode, folded cascade, and differential cascode and then introduces image rejection filters for low‐side and high‐side injection. Then, a new method for design and optimization of the circuits based on a Pareto‐based multiobjective genetic algorithm is proposed. A set of optimum device values and dimensions that best match design specifications are obtained. The optimization method is layout aware, parasitic aware, and simulation based. Circuit simulations are carried out based on TSMC 0.18 μm CMOS technology by using Hspice. © 2010 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2010.     

Shape optimized design of microwave dielectric resonators by level‐set and topology gradient methods

The finite element method is coupled with the topology gradient (TG) and level‐set (LS) methods for optimizing the shape of microwave components using a computer‐aided design model. On the one hand, the LS approach is based on the classical shape derivative; while on the other hand, the TG method is precisely designed for introducing new perturbations in the optimization domain. These two approaches, which consist in minimizing a cost function related to the component behavior, are first described. Regarding given electrical specifications, these techniques are applied to optimize the distribution of ceramic parts of a dual‐mode resonator in order to improve its behavior. The optimized dielectric resonators result in a wide spurious‐free stop band. A comparison between classical and optimized dual mode resonator is presented. Theoretical results are then validated by careful measurements. © 2009 Wiley Periodicals, Inc. Int J RF and Microwave CAE 2010.     

Implementation of speed controller for rotary hydraulic motor based on LS-SVM

The performance of a model-based control system depends strongly on the accuracy of the process model used. LS-SVM is a powerful method for modeling nonlinear systems. The main objective of this paper is to implement a conventional controller based on LS-SVM model for hydraulic motor. An off-line model is first identified based on LS-SVM, then via simulation tests the parameters of the discrete PI-Controller and its velocity-form are obtained then the controller parameters are applied experimentally for the hydraulic motor as a speed controller. The system performance has been evaluated; results show good performance over a wide range of operating conditions and load disturbances.     

Fast,effective heuristics for the 0–1 multi-dimensional knapsack problem

The objective of the multi-dimensional knapsack problem (MKP) is to find a subset of items with maximum value that satisfies a number of knapsack constraints. Solution methods for MKP, both heuristic and exact, have been researched for several decades. This paper introduces several fast and effective heuristics for MKP that are based on solving the LP relaxation of the problem. Improving procedures are proposed to strengthen the results of these heuristics. Additionally, the heuristics are run with appropriate deterministic or randomly generated constraints imposed on the linear relaxation that allow generating a number of good solutions. All algorithms are tested experimentally on a widely used set of benchmark problem instances to show that they compare favourably with the best-performing heuristics available in the literature.     

Universal integral controllers for minimum-phase nonlinear systems

We consider a single-input-single-output (SISO) nonlinear system that has a well-defined normal form with asymptotically stable zero dynamics. Using only knowledge of the relative degree and the sign of the high-frequency gain, we design an output feedback integral controller that asymptotically regulates the output to a bounded time-varying reference signal with a constant limit. We give regional as well as semi-global results. We also show that, for relative degree one and two systems, the proposed integral controller reduces to the classical PI and PID controllers, respectively     

Method of imaging low density lipoproteins by atomic force microscopy

This short paper reports a simple method to image low density lipoproteins (LDL) using atomic force microscopy (AFM). This instrument allows imaging of biological samples in liquid and presents the advantage of needing no sample preparation such as staining or fixation that may affect their general structure. Dimensions (diameter and height) of individual LDL particles were successfully measured. AFM imaging revealed that LDL have a quasi-spherical structure on the x and y axis with an oblate spheroid structure in the z axis (i.e., height). LDLs were found to have an average diameter of 23 +/- 3 nm. The obtained mean height was 10 +/- 2 nm.     

Incorporating Software Visualization in the Design of Intelligent Diagnosis Systems for User Programming

Program diagnosis systems were developed to help users solve programming problems. By providing guidence on errors and misconceptions, these systems can help the users in writing programs and understanding their dynamic behavior. Features of software visualization which aim at providing visual and concrete depictions to the abstractions and operations of programs have also shown to be making programs more understandable. The main theme of this paper is to asses the usefulness of incorporating features of software visualization into the design of program diagnosis systems intended for novices. We report an empirical evaluation to assess the effectiveness of supporting visualization features during problem solving. The system used in the evaluation integrates visualzation and immediacy features and supports a model-tracing based approach to program diagnosis. Unlike other similar systems, our prototype system supports a more flexible style of interaction by increasing the grain size of diagnosis to a complete programming statement. The evaluation reported here seems to suggest that when supported with visualization features, systems for program diagnosis tend to be more effective in helping the users during problem solving.     

A variable neighborhood descent heuristic for the problem of makespan minimisation on unrelated parallel machines with setup times

The NP-hard problem of scheduling jobs on unrelated parallel machines, in the presence of machine-dependent and sequence-dependent setup times, with the objective of minimizing the makespan, is considered. A variable neighborhood descent search algorithm, hybridized with mathematical programming elements, is presented and its performance is evaluated on a large set of benchmark problem instances. The extensive computational experiments show that the proposed algorithm outperforms previously proposed methods in terms of solution quality as well as computation time.