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Optimal Testing Resource Allocation for modular software systems based-on Multi-objective Evolutionary Algorithms with Effective Local Search Strategy

Optimal Testing Resource Allocation for Modular Software Sys...

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IEEE 2nd Workshop on Memetic Computing (MC)

作者： Yu Shuaishuai Fei Dong Bin Li USTC Dep Elect Sci & Technol Hefei Peoples R China

ISBN: (纸本)9781467358910

software testing is a very important part in software projects. As a key issue in software testing, Optimal Testing Resource Allocation Problems (OTRAPs) have drawn more and more attention recently. Along with the rapid increasing of the scale and complexity of software systems, the problems become more and more difficult to solve. Although some single objective optimization approaches had been used to solve such problems, quite a number of flaws were observed with these approaches, such as trapping into local optima, high computational complexity and few available optimal solutions. In this paper, to solve the problem of few available optimal solutions, an effective local search (ELS) is introduced into two effective multi-objective evolutionary algorithms: Nondominated Sorting Genetic Algorithm II (NSGA-II) and Harmonic Distance Based Multi-objective Evolutionary Algorithm (HaD-MOEA), advantages of this strategy over pure multi-objective approaches are testified on two OTRAPs with parallel-series modular software systems. To deal with the problem of high computational complexity, the proposed ELS is also embedded into another effective multi-objective algorithm, Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D) to solve OTRAPs. Comprehensive experimental studies show the better performance over the state-of-the-art multi-objective approaches for OTRAPs.

关键词： Multi-objective evolutionary algorithm parallel-series modular software system effective local search (ELS) NSGA-II MOEA/D software testing reliability and testing cost

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Multi-Objective Approaches to Optimal Testing Resource Allocation in modular software systems

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IEEE TRANSACTIONS ON RELIABILITY 2010年第3期59卷 563-575页

作者： Wang, Zai Tang, Ke Yao, Xin Univ Sci & Technol China Sch Comp Sci & Technol NICAL Hefei 230027 Anhui Peoples R China Univ Birmingham Sch Comp Sci CERCIA Birmingham B15 2TT W Midlands England

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.

关键词： Multi-objective evolutionary algorithm parallel-series modular software system software engineering software reliability software testing star-structure modular software system

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A Multi-objective Approach to Testing Resource Allocation in modular software systems

A Multi-objective Approach to Testing Resource Allocation in...

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IEEE Congress on Evolutionary Computation

作者： Wang, Zai Tang, Ke Yao, Xin Univ Sci & Technol China Dept Comp Sci & Technol Nat Inspired Computat & Applicat Lab Hefei 230027 Anhui Peoples R China

ISBN: (纸本)9781424418220

Nowadays, as the software systems become increasingly large and complex, the problem of allocating the limited testing-resource during the testing phase has become more and more difficult. In this paper, we propose to solve the testing-resource allocation problem (TRAP) using multi-objective evolutionary algorithms. Specifically, we formulate TRAP as two multi-objective problems. First, we consider the reliability of the system and the testing cost as two objectives. In the second formulation, the total testing-resource consumed is also taken into account as the third goal. Two multi-objective evolutionary algorithms, Non-dominated Sorting Genetic Algorithm II (NSGA2) and Multi-Objective Differential Evolution Algorithms (MODE), are applied to solve the TRAP in the two scenarios. This is the first time that the TRAP is explicitly formulated and solved by multi-objective evolutionary approaches. Advantages of our approaches over the state-of-the-art single-objective approaches are demonstrated on two parallel-series modular software models.

关键词： software Reliability Multi-Objective Evolutionary Algorithm parallel-series modular software system

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