Optimal testing-resource allocation with genetic algorithm for modular software systems

Dai, Yuanshun; Xie, Min; Poh, Kim-Leng; Yang, Bo

doi:10.1016/s0164-1212(02)00062-6

Cited by 73 publications

(53 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is essentially a function of reliability, and thus also a function of testing resources [11]- [13], [15]- [18]. Recent work on OTRAPs either proposed minimizing testing cost instead of maximizing reliability, or incorporated additional constraints on the testing cost (e.g., the reliability is maximized subject to given bounds of the testing cost and resource) [12], [13], [15]- [18].…”

Section: Nsga-ii Nondominated Sorting Genetic Algorithm IImentioning

confidence: 99%

“…Alternatively, evolutionary algorithms are meta-heuristics that possess the strong capability of global search, and are usually not very sensitive to initial solutions. Their effectiveness has been demonstrated on a large spectrum of problems in the reliability optimization field, such as resource management and task partition in grid systems [19]- [21], redundancy allocation [22], [23], reliability optimization of weighted voting systems [24], and OTRAPs [7]- [12], [15], [17], [18]. In particular, evolutionary algorithms have been reported to perform better than some other techniques on OTRAPs [7], [9]- [12], [15], [17], [18].…”

Section: Nsga-ii Nondominated Sorting Genetic Algorithm IImentioning

confidence: 99%

“…Instead, we may resort to balancing between reliability and cost, and seeking a good trade-off. Though some previous researchers did attempt to do so [12], they still solved the problem in a single-objective optimization manner, where the resource allocation scheme is optimized with respect to a weighted sum of reliability and testing cost. This might be inappropriate because reliability and cost are generally of different scales.…”

Section: Nsga-ii Nondominated Sorting Genetic Algorithm IImentioning

confidence: 99%

“…The testing cost for each module is defined as [12] ( 4) where , , and are constants that control the increment speed of the testing cost corresponding to the reliability in module . Correspondingly, the testing cost for the whole parallel-series modular software system is (5) where is the total testing cost for the groups of parallel modules, and is the total testing cost for the series modules.…”

Section: A Problem Formulations On Parallel-series Modular Software mentioning

confidence: 99%

See 3 more Smart Citations

Multi-Objective Approaches to Optimal Testing Resource Allocation in Modular Software Systems

2010

View full text Add to dashboard Cite

Abstract-Software testing is an important issue in software engineering. As software systems become increasingly large and complex, the problem of how to optimally allocate the limited testing resource during the testing phase has become more important, and difficult. Traditional Optimal Testing Resource Allocation Problems (OTRAPs) involve seeking an optimal allocation of a limited amount of testing resource to a number of activities with respect to some objectives (e.g., reliability, or cost). We suggest solving OTRAPs with Multi-Objective Evolutionary Algorithms (MOEAs). Specifically, we formulate OTRAPs as two types of multi-objective problems. First, we consider the reliability of the system and the testing cost as two objectives. Second, the total testing resource consumed is also taken into account as the third objective. The advantages of MOEAs over state-of-the-art single objective approaches to OTRAPs will be shown through empirical studies. Our study has revealed that a well-known MOEA, namely Nondominated Sorting Genetic Algorithm II (NSGA-II), performs well on the first problem formulation, but fails on the second one. Hence, a Harmonic Distance Based Multi-Objective Evolutionary Algorithm (HaD-MOEA) is proposed and evaluated in this paper. Comprehensive experimental studies on both parallel-series, and star-structure modular software systems have shown the superiority of HaD-MOEA over NSGA-II for OTRAPs.Index Terms-Multi-objective evolutionary algorithm, parallelseries modular software system, software engineering, software reliability, software testing, star-structure modular software system. ACRONYMS

show abstract

Section: Nsga-ii Nondominated Sorting Genetic Algorithm IImentioning

confidence: 99%

Section: Nsga-ii Nondominated Sorting Genetic Algorithm IImentioning

confidence: 99%

Section: Nsga-ii Nondominated Sorting Genetic Algorithm IImentioning

confidence: 99%

Section: A Problem Formulations On Parallel-series Modular Software mentioning

confidence: 99%

See 2 more Smart Citations

Multi-Objective Approaches to Optimal Testing Resource Allocation in Modular Software Systems

2010

View full text Add to dashboard Cite

show abstract

“…It is worth mentioning that the solution of the issue of reducing the time and expenses required in the software production process may be significantly facilitated by applying formal methods of mathematical modeling and operational research. The examples of such approach may be found, for example, in [3][4][6][7]15,21].…”

Section: Introductionmentioning

confidence: 99%

Method for assessing software reliability of the document management system using the RFID technology

et al. 2016

View full text Add to dashboard Cite

Abstract. The deliberations presented in this study refer to the method for assessing software reliability of the document management system, using the RFID technology. A method for determining the reliability structure of the discussed software, understood as the index vector for assessing reliability of its components, was proposed. The model of the analyzed software is the control transfer graph, in which the probability of activating individual components during the system's operation results from the so-called operational profile, which characterizes the actual working environment. The reliability structure is established as a result of the solution of a specific mathematical software task. The knowledge of the reliability structure of the software makes it possible to properly plan the time and financial expenses necessary to build the software, which would meet the reliability requirements. The application of the presented method is illustrated by the number example, corresponding to the software reality of the RFID document management system.

show abstract

A study of the modeling and analysis of software fault‐detection and fault‐correction processes

Xie

et al. 2006

Quality & Reliability Eng

Self Cite

103

View full text Add to dashboard Cite

Most of the models for software reliability analysis are based on reliability growth models which deal with the fault detection process. This is done either by assuming that faults are corrected immediately after being detected or the time to correct a fault is not counted. Some models have been developed to relax this assumption. However, unlike the fault-detection process, few published data sets are available to support the modeling and analysis of both the fault detection and removal processes. In this paper, some useful approaches to the modeling of both software fault-detection and faultcorrection processes are discussed. Further analysis on the software release time decision that incorporates both a fault-detection model and fault-correction model is also presented. This procedure is easy to use and useful for practical applications. The approach is illustrated with an actual set of data from a software development project.

show abstract

Optimal testing-resource allocation with genetic algorithm for modular software systems

Cited by 73 publications

References 24 publications

Multi-Objective Approaches to Optimal Testing Resource Allocation in Modular Software Systems

Multi-Objective Approaches to Optimal Testing Resource Allocation in Modular Software Systems

Method for assessing software reliability of the document management system using the RFID technology

A study of the modeling and analysis of software fault‐detection and fault‐correction processes

Contact Info

Product

Resources

About