A flexible method to estimate the software development effort based on the classification of projects and localization of comparisons

The estimation of software development effort has been centralized mostly on the accuracy of estimates through dealing with heterogeneous datasets regardless of the fact that the software projects are inherently complex and uncertain. In particular, Analogy Based Estimation (ABE), as a widely accepted estimation method, suffers a great deal from the problem of inconsistent and non-normal datasets because it is a comparison-based method and the quality of comparisons strongly depends on the consistency of projects. In order to overcome this problem, prior studies have suggested the use of weighting methods, outlier elimination techniques and various types of soft computing methods. However the proposed methods have reduced the complexity and uncertainty of projects, the results are not still convincing and the methods are limited to a special domain of software projects, which causes the generalization of methods to be impossible. Localization of comparison and weighting processes through clustering of projects is the main idea behind this paper. A hybrid model is proposed in which the software projects are divided into several clusters based on key attributes (development type, organization type and development platform). A combination of ABE and Particle Swarm Optimization (PSO) algorithm is used to design a weighting system in which the project attributes of different clusters are given different weights. Instead of comparing a new project with all the historical projects, it is only compared with the projects located in the related clusters based on the common attributes. The proposed method was evaluated through three real datasets that include a total of 505 software projects. The performance of the proposed model was compared with other well-known estimation methods and the promising results showed that the proposed localization can considerably improve the accuracy of estimates. Besides the increase in accuracy, the results also certified that the proposed method is flexible enough to be used in a wide range of software projects.     

A uniform approach for access control and business models with explicit rule realization

Access control is an important part of security in software, such as business applications, since it determines the access of users to objects and operations and the constraints of this access. Business and access control models are expressed using different representations. In addition, access control rules are not generally defined explicitly from access control models. Even though the business model and access control model are two separate modeling abstractions, they are inter-connected as access control is part of any business model. Therefore, the first goal is to add access control models to business models using the same fundamental building blocks. The second goal is to use these models and define general access control rules explicitly from these models so that the connection between models and their realizations are also present. This paper describes a new common representation for business models and classes of access control models based on the Resource–Event–Agent (REA) modeling approach to business models. In addition, the connection between models and their represented rules is clearly defined. We present a uniform approach to business and access control models. First, access control primitives are mapped onto REA-based access control patterns. Then, REA-based access control patterns are combined to define access control models. Based on these models, general access control rules are expressed in Extended Backus–Naur Form.     

Improved position tracking performance of a pneumatic actuator using a fuzzy logic controller with velocity,system lag and friction compensation

In this paper a new control algorithm for position tracking of sinusoidal trajectories is introduced to improve the accuracy of a pneumatic actuator. A proportional valve is used to control the position of a rodded cylinder through a fuzzy logic (FL) approach with added velocity feedback (VF), f system lag compensator (LC) and friction compensator (FC). Parameters of the FL controller and the VF and FC compensators are tuned off-line manually. Parameters for the LC compensator are obtained by an on-line algorithm. Experimental results show that the proposed FL+VF+LC+FC controller is able to significantly improve tracking performance. The contribution of each component towards overall performance is carefully documented. The results compare favourably with the performance obtained with more advanced model-based algorithms as reported in the literature. The simpler non-model based nature of the controller together with the careful documentation of the algorithm and system performance, means that other researchers can readily evaluate the controller for their own applications.

     

Robust nonlinear control schemes for finite-time tracking objective of a 5-DOF robotic exoskeleton

This paper investigates the finite-time robust tracking problem for a 5-DOF (degrees of freedom) upper-limb exoskeleton robot subjected to parametric uncertainties, unmodelled dynamics, and unknown human efforts. By developing the non-singular terminal sliding mode control approach, three innovative schemes of robust torques are proposed to steer configuration variables (angular displacements of joints) of the 5-DOF robotic exoskeleton to the reference trajectories within adjustable finite times. Based on mathematical analysis, it is proven that all suggested schemes of control inputs (input torques) accomplish and provide the mentioned tracking objective accurately. In addition, several new formulas (in the form of inequalities) are derived to determine and tune the needed finite times for achieving the tracking objective. Finally, three numerical examples are given to show the appropriate performance and the acceptable effectiveness of the proposed finite-time robust control schemes.     

TS-based sampled-data model predictive controller for continuous-time nonlinear systems

This paper proposes a new fuzzy model predictive control approach for continuous-time nonlinear systems in terms of linear matrix inequalities (LMIs). The proposed approach is based on the Takagi–Sugeno fuzzy modeling, a quadratic Lyapunov function, and a sampled-data parallel distributed compensation controller with constant sampling time. The goal is designing the sampled-data controller such that at each sampling time, the stability of the closed-loop system is guaranteed and an infinite horizon cost function is minimised. The main advantage of the proposed approach is to eliminate the approximations induced from discretizing the original system and cost function upper bound minimisation. Consequently, a lower bound of the cost function is obtained and the performance of the proposed model predictive controller is improved compared to the recently published papers in the same field of interest. In addition, the Euclidean norm constraint of the control input vector is derived in terms of LMIs. To illustrate the merits of the proposed approach, the proposed technique is applied to a continuous stirred tank reactor system.     

Recognition of control chart patterns using an intelligent technique

Control chart patterns (CCPs) are important statistical process control tools for determining whether a process is run in its intended mode or in the presence of unnatural patterns. Automatic recognition of abnormal patterns in control charts has seen increasing demands nowadays in the manufacturing processes. This paper presents a novel hybrid intelligent method for recognition of common types of CCP. The proposed method includes three main modules: the feature extraction module, the classifier module and optimization module. In the feature extraction module, a proper set of the shape features and statistical features is proposed as the efficient characteristic of the patterns. In the classifier module multilayer perceptron neural network and support vector machine (SVM) are investigated. In support vector machine training, the hyper-parameters have very important roles for its recognition accuracy. Therefore, in the optimization module, improved bees algorithm is proposed for selecting of appropriate parameters of the classifier. Simulation results show that the proposed algorithm has very high recognition accuracy.     

IssuePlayer: An extensible framework for visual assessment of issue management in software development projects

This article presents a software visualization framework which can help project managers and team leaders in overseeing issues and their management in software development. To automate the framework, a dashboard tool called IssuePlayer is developed. The tool is used to study the trends in which different types of issues (e.g., bugs, support requests) are submitted, handled and piled up in software projects and use that information to identify process symptoms, e.g., the times when the code maintenance team is not responsive enough. The interactive nature of the tool enables identification of the team members who have not been as active as they were expected to be in such cases. The user can play, pause, rewind and forward the issue management histories using the tool. The tool is empirically evaluated by two industrial partners in North America and Europe. The survey and qualitative feedback support the usefulness and effectiveness of the tool in assessing the issue management processes and the performance of team members. The tool can be used complementarily in parallel with textual information provided by issue management tools (e.g., BugZilla) to enable team leaders to conduct effective and successful monitoring of issue management in software development projects.     

Cournot games with linear regression expectations in oligopolistic markets

In this paper, a Cournot game in an oligopolistic market with incomplete information is considered. The market consists of some producers that compete for getting higher payoffs. For optimal decision making, each player needs to estimate its rivals’ behaviors. This estimation is carried out using linear regression and recursive weighted least-squares method. As the information of each player about its rivals increases during the game, its estimation of their reaction functions becomes more accurate. Here, it is shown that by choosing appropriate regressors for estimating the strategies of other players at each time-step of the market and using them for making the next step decision, the game will converge to its Nash equilibrium point. The simulation results for an oligopolistic market show the effectiveness of the proposed method.     

Decision support for moving from a single product to a product portfolio in evolving software systems

Successful software systems continuously evolve to accommodate ever-changing needs of customers. Accommodating the feature requests of all the customers in a single product increases the risks and costs of software maintenance. A possible approach to mitigate these risks is to transition the evolving software system (ESS) from a single system to a portfolio of related product variants, each addressing a specific customers’ segment. This evolution should be conducted such that the extent of modifications required in ESS's structure is reduced. The proposed method COPE+ uses preferences of customers on product features to generate multiple product portfolios each containing one product variant per segment of customers. Recommendations are given to the decision maker to update the product portfolios based on structural analysis of ESS. Product portfolios are compared with the ESS using statechart representations to identify the level of similarity in their behaviors. A proof of concept is presented by application to an open-source text editing system. Structural and behavioral analysis of candidate portfolios helped the decision maker to select one portfolio out of three candidates.     

A novel intelligent particle swarm optimization algorithm for solving cell formation problem

The formation of manufacturing cells forms the backbone of designing a cellular manufacturing system. In this paper, we present a novel intelligent particle swarm optimization algorithm for the cell formation problem. The proposed solution method benefits from the advantages of particle swarm optimization algorithm (PSO) and self-organization map neural networks by combining artificial individual intelligence and swarm intelligence. Numerical examples demonstrate that the proposed intelligent particle swarm optimization algorithm significantly outperforms PSO and yields better solutions than the best solutions existed in the literature of cell formation. The application of the proposed approach is examined in a case problem where real data is utilized for cell reconfiguration of an actual company involved in agricultural manufacturing sector.