Optimal software testing-resource allocation with operational profile: computational aspects

In general terms, an optimization problem consists in selecting from among a set of feasible alternatives one which is optimal according to a given criterion of optimality. Economic problems are typically of this nature since economics—theory as well as practical applications e.g. to industrial planning problems—is largely about optimal allocation of given or scarce resources. The first stage of a rational procedure for solving a problem of economic optimization is to determine the set of alternatives to choose from, the feasible region; the next stage is to establish a criterion of economic optimality in the form of a preference function (objective function). The optimal allocation can then be determined by comparing the values of the preference function associated with the feasible alternatives, the optimal allocation being that alternative for which the preference function is a maximum (or minimum).

Refactoring, the process of improving the design of existing code by changing its internal structure without affecting its external behavior, tends to improve software quality by improving design, improving readability, and reducing bugs. There are many different refactoring methods, each having a particular purpose and effect. Consequently, the effect of refactorings on software quality attribute may vary. Software testing is an external software quality attributes that takes lots of time and effort to make sure that the software performs as intended. In this paper, we propose a classification of refactoring methods based on their measurable effect on software testing effort. This, in turn, helps the software developers decide which refactoring methods to apply in order to optimize a software system with regard to the testing effort.

Effort estimation of software testing plays a vital role in the effective completion of testing. In particular, software test effort estimation in defense projects is not an easy and simple phenomenon, owing to internal and external factors. This study aims to investigate the relationships between software test metrics used in the industry and software testing efforts. A method for estimating the testing effort is proposed using a novel set of software testing metrics that have not been used in any previously proposed software test effort estimation methods. In this study, 15 completed software projects of a CMMI Level-3 certified defense industry company were analyzed. The results of the empirical study show that the proposed method with the given metrics provides acceptable prediction quality, with Pred (0.25) and Pred (0.30) values equal to 0.867. We obtained plausible results and demonstrated that our newly proposed metrics can be safely used for software test effort estimation.

In the upcoming fifth-generation (5G) wireless communication system, the massive MIMO and mmWave technologies will play a key role to improve the system performance. With the rapid development of the network communications, optimizing radio resources of wireless communication has been one of the challenging issues in 5G networks. In order to effectively improve network performance, an efficient resource allocation and power optimization algorithm is necessary. In this paper, we study joint resource allocation and power optimization (JRAPO) problem in multiuser mmWave massive MIMO system, which target at increasing energy efficiency (EE) and decreasing power consumption of systems. First, the problem of JRAPO is formulated as a multivariate mixed integer nonlinear programming problem. Second, because the non-convexity of this problem, we transformed it into a convex optimization problem by relaxing the resource block (RB) allocation indicator. Finally, we used Lagrangian dual decomposition algorithm and water-filling scheme to resolve this joint optimization problem. Simulation results demonstrated the effectiveness of the proposed algorithm.

An evolutionary algorithm-based optimal allocation method of wind resources under the background of carbon neutralization is proposed in order to better achieve the goal of energy conservation and emission reduction under the background of carbon neutralization, aiming at the current unreasonable allocation of wind resources. The evaluation model of balanced wind resource allocation is designed, and the evaluation index of optimal wind resource allocation is constructed using the evolutionary algorithm. The optimal allocation path of wind energy resources is chosen to achieve the goal of reasonable wind energy resource allocation. Finally, simulation experiments show that using an evolutionary algorithm to solve the problem of poor energy allocation and achieve the research goal, the optimal allocation method of wind energy resources under the background of carbon neutralization can effectively solve the problem of poor energy allocation.

The QoS based Resource Allocation Model (Q-RAM) proposed by R. Rajkumar et al. (1998) presented an analytical approach for satisfying multiple quality of service dimensions in a resource constrained environment. Using this model, available system resources can be apportioned across multiple applications such that the net utility that accrues to the end users of those applications is maximized. We present several practical solutions to allocation problems that were beyond the limited scope of Q-RAM. We show that the Q-RAM problem of finding the optimal resource allocation to satisfy multiple QoS dimensions is NP hard. We then present a polynomial solution for this resource allocation problem which yields a solution within a provably fixed and short distance from the optimal allocation. Secondly, Q-RAM dealt mainly with the problem of apportioning a single resource to satisfy multiple QoS dimensions. We study the converse problem of apportioning multiple resources to satisfy a single QoS dimension. In practice, this problem becomes complicated, since a single QoS dimension perceived by the user can be satisfied using different combinations of available resources. We show that this problem can be formulated as a mixed integer programming problem that can be solved efficiently to yield an optimal resource allocation. We also present the run times of these optimizations to illustrate how these solutions can be applied in practice. A good understanding of these solutions will yield insights into the general problem of apportioning multiple resources to satisfy simultaneously multiple QoS dimensions of multiple concurrent applications.

This paper presents an empirical research to the relations and dependencies between the fields of software product management and software project management in product software companies. By carrying out interviews and an online survey, we answer several current business issues on the leadership roles of product and project managers within the organizational structure, and provide a definition of software products and software projects based on their dependencies.

This paper investigates the optimal cash crop combination at a small-scale cash crop farm in Vhembe District, Limpopo Province, South Africa in which green maize, cabbages, tomatoes, spinach, mustard, butternut and sweet potatoes are grown. To get optimum farm outputs, decisions on crop combination and operational activities in crop production are crucial. Proper farm planning and resource allocation play a significant role in optimising farm revenues. It was observed that the farmer used traditional methods of allocating resources, which lead to a less profitable crop mix. In view of this, in this study, linear programming model was formulated using data collected from a farm concerning the past crop combinations and allocation of resources in crop planning and production to determine the best crop combination that maximizes net income given limited resources such as land, labour, capital, and others. The simplex method of linear programming works by first locating a feasible solution and then relocating to any vertex of the feasible set that improves the cost function. Eventually, a point is reached beyond which no further movement improves the cost function. The results of the developed linear programming model were compared to past farming practices based on experience, leading to the conclusion that crops and limited resources were not optimally allocated. The results clearly demonstrate the optimal crop combination and allocation of scarce resources that the farm could have considered to yield maximum returns. It is observed that the proposed linear programming model is appropriate for finding the optimal land allocation criteria for the cash crops in the study area. The optimal crop mix from the linear programming two phase simplex method show that the farmer should grow the following crop mix, 1.16 ha of green mealies, 2.64 ha of cabbages, 0.8 ha of tomatoes, 1.2 ha of mustard, 0.4 ha spinach, 0.4 ha of butternut and 0.4 ha of sweet potatoes with the gross income of R 740 800. The linear programming model resulted in a 37.8 percent increase in profit margin. Based on the results obtained from this study, it is recommended that the small-scale cash crop farmer should invest more in producing crops that give maximum profit. MATLAB software was used to determine the optimal values of the decision variables. Key words: Cash crops allocation, crop combination, net income, linear programming (LP), resource allocation.

Search-based software engineering (SBSE) consists of the application of metaheuristic search techniques—such as genetic algorithms, simulated annealing, hill climbing, and tabu search—to software engineering problems. This idea is based on the observation that some software engineering activities can be thought of as optimization problems, and on the fact that techniques providing exact solutions are, very often, unable to adequately scale to large, real-world software engineering problems. Define fitness functions to guide an automated search for solutions, rather than attempting to construct solutions from scratch by hand. SBSE moves the engineer up the abstraction chain, from the tedious and expensive role of detailed solution construction, to the potentially more productive and more cost-effective area of fitness function design. This approach has been repeatedly demonstrated to have successful applications across the software engineering life cycle from requirements and project planning to maintenance and testing. This special issue focuses on applications of search SBSE to the maintenance phase of the life cycle. After software testing 2, software maintenance is the most widely studied application area for SBSE research. Work on SBSE applications to problems in software maintenance can be traced back to the pioneering work of the Mancoridis et al. 3, which led to the development of the Bunch tool for software re-modularization 4. In the past five years there has been a significant increase in SBSE applications to software maintenance and evolution. Search-based techniques have been applied to post-development code improvement problems, such as object-oriented refactoring 5, 6, and imperative language slicing and transformation 7. Other applications are related to software engineering economics and project management problems, such as release planning 8-12, effort, and cost estimation 13-16, and project staffing and planning 17-20. Last, but not the least, SBSE has been applied to deal with service-oriented architectures, for example for services QoS-aware composition 21, service-level agreement negotiation 22, and testing 23. A complete overview of SBSE with a discussion of future research trends can be found in the recent International Conference on Software Engineering (ICSE) ‘Future of Software Engineering’ paper 24. This issue of the Journal of Software Maintenance and Evolution: Research and Practice features three outstanding papers, dealing with different aspects of software maintenance and evolution: release planning and staffing, re-factoring, and component substitution. The paper ‘Optimized Staffing for Product Releases—Focused Search and its Application at Chartwell Technologies’ by Puneet Kapur, An Ngo-The, Günther Ruhe, and Andrew Smith presents an approach for optimizing the staffing for product releases. The problem they solve can be viewed as part of the more general release planning problem, which assigns features to releases such that technical, resource, risk, and budget constraints are met. In particular, the genetic search-based approach the authors propose aims at assigning human resources to software development tasks, taking them from a pool of developers having different levels of skills. The applicability of the proposed approach is demonstrated with an industrial case study. The paper ‘Search-Based Refactoring: an Empirical Study’ by Mark O'Keffe and Mel Ó Cinnéide presents an approach to automatically re-factor, using search-based techniques, object-oriented systems that undergo repeated addition of functionality. Such addition can, very often, deteriorate the quality of the system's underlying design. Their re-factoring is guided by the QMOOD quality model 25, and the paper compares different optimization techniques, such as Genetic Algorithms, Simulated Annealing, and Multiple Ascent Hill-Climbing. The results of experiments carried out on five software systems suggest that the latter technique outperforms the others. The paper ‘Search-based Many-to-one Component Substitution’ by Nicolas Desnos, Marianne Huchard, Guy Tremblay, Christelle Urtado, and Sylvain Vauttier presents a search-based approach that allows the replacement of an obsolete component with a composition of multiple components. When a component becomes obsolete, the approach searches for a minimal assembly of other components, exposing their functionality by means of ports that preserves the behavior of the original component. The proposed approach has been evaluated on a set of randomly generated components. We hope that readers will enjoy this special issue and find the selected papers insightful and inspiring. We would like to thank all the authors who presented papers for this special issue: there were many good quality papers, but unfortunately we could accept only three of them. Last, but not the least, we would like to thank the reviewers who provided timely, detailed and constructive feedbacks to the authors. Without their precious help this special issue would not have been possible.

Crowdsensing offers an efficient approach to meet the demand in large scale sensing applications. In crowdsensing, it is of great interest to find the optimal task allocation, which is challenging since sensing tasks with different requirements of quality of sensing are typically associated with specific locations and mobile users are constrained by time budgets. We show that the allocation problem is NP hard. We then focus on approximation algorithms, and devise an efficient local ratio based algorithm (LRBA). Our analysis shows that the approximation ratio of the aggregate rewards obtained by the optimal allocation to those by LRBA is 5. This reveals that LRBA is efficient, since a lower (but not tight) bound on the approximation ratio is 4. We also discuss about how to decide the fair prices of sensing tasks to provide incentives since mobile users tend to decline the tasks with low incentives. We design a pricing mechanism based on bargaining theory, in which the price of each task is determined by the performing cost and market demand (i.e., the number of mobile users who intend to perform the task). Extensive simulation results are provided to demonstrate the advantages of our proposed scheme.

In this paper, we discuss the optimal allocation problem in a multi-level accelerated life testing experiment under time (Type-I) censoring when an extreme-value regression model is used for statistical analysis. We derive the expected Fisher information and the asymptotic variance-covariance matrix of the maximum likelihood estimators. Three optimality criteria are considered and the corresponding optimal allocations are determined. Under Type-I censoring, because the optimal allocations are depending on the model parameters, the sensitivity of the optimal allocations due to mis-specification of the model parameters is studied. A numerical example is used to illustrate the methodologies developed in this paper.

We address the problem of joint path selection and rate allocation in multipath wireless streaming, in order to optimize a media specific quality of service. We leverage on the existence of multiple parallel wireless services, in order to enhance the received video quality at a wireless client. An optimization problem is proposed, aimed at minimizing a video distortion metric based on sequence-dependent parameters, and transmission channel characteristics, for a given wireless network infrastructure. Even if joint optimal path selection and rate allocation is in general an NP complete problem, an in-depth analysis of the media distortion evolution allows defining a low complexity optimal streaming strategy, under reasonable network assumptions. In particular, we show that a greedy allocation of rates along paths with increasing error probability leads to an optimal solution. We argue that a network path should not be chosen for transmission, unless all other available paths with lower error probability have been chosen. Moreover, the chosen paths should be used at their maximum end-to-end bandwidth. These results are demonstrated for both independent network paths, and non-disjoint channel segments, in generic network topologies. Simulation results showed that the optimal rate allocation carefully trades off total encoding/transmission rate, with the end-to-end transmission error probability and the number of chosen paths. In many cases, the optimal rate allocation provides more than 20% improvement in received video quality, compared to heuristic-based algorithms.

Many research works have been developed for stereo image compression purpose where most of them aim at encoding a reference image, a residual one and a disparity map. While the disparity field is often losslessly encoded, we are mainly interested in this paper in the bit allocation problem between the reference and residual images. Generally, the bit allocation is expressed as an optimization problem which involves the computation of the operational rate-distortion (RD) functions for all the wavelet subbands and for different quantization steps. However, this strategy is computationally intensive. To solve this problem, we consider the uniform scalar quantization of the wavelet subbands of both images modeled by a Generalized Gaussian distribution. Thanks to recent approximations of the entropy and distortion functions, we develop an optimal and fast bit allocation method. The obtained results confirm the efficiency of the proposed bit allocation method in the context of stereo image coding.

Optimal effort allocation in stratified, large area trawl surveys, with application to shrimp surveys in the Barents Sea

Computer systems play an increasingly important role in modern enterprises. Such systems represent a major investment, and their performance has major effects on profitability, utilization and user satisfaction. Much effort is therefore directed at the efficient management of these systems, and this paper is a step in this direction. Specifically, we consider the optimal allocation of programs and data to various storage devices in order to minimize the expected operational costs, subject to capacity and timing constraints. This problem is one of the many optimization problems that the system management faces, and its solution, though suboptimal for the whole system, might contribute considerably to the overall system's performance and costs. An effective procedure for the optimal (storage) allocation is presented, whose application to an actual large-scale operating system resulted in high (relative) savings. This procedure, with minor modifications, might be applied to many allocation problems which face users and management of computer systems.