基于改進粒子群的組合測試用例生成技術研究
發(fā)布時間：2018-10-15 12:31
【摘要】：組合測試作為一種基于規(guī)約的軟件測試方法,旨在從待測軟件面臨的龐大組合空間中,選取少量但有效的測試用例,生成覆蓋程度高、揭錯能力強的測試用例集。但組合測試用例生成是NP難問題,需要在多項式時間內(nèi)求解組合問題,因此需要采用元啟發(fā)式搜索算法來解決該問題。相較于其他元啟發(fā)式搜索算法,粒子群算法在覆蓋表生成規(guī)模和執(zhí)行時間上更具有競爭力。本文系統(tǒng)回顧和總結了利用粒子群算法生成組合測試用例集的已有研究成果,針對可變力度組合測試問題和粒子群算法的參數(shù)選取問題,將改進的one-test-at-a-time策略和自適應粒子群算法相結合,提出了一種可處理任意覆蓋強度的組合測試用例生成方法。本文的主要研究工作和貢獻概括如下:(1)針對實際待測軟件中存在的約束問題,提出了一種類似于避免選擇策略的方法對約束條件預先處理,在生成測試用例前時對無效的約束組合進行剔除,在一定程度上縮減需覆蓋組合集的大小,避免了無效組合所引起的適應度值的誤差。(2)針對one-test-at-a-time策略組合選取問題,提出了兩種優(yōu)先級度量方法:覆蓋組合度量方法和因素取值度量方法,在生成單個測試用例的過程中,優(yōu)先選取了權值最大的組合用于單個測試用例的生成,避免了原始算法存在的隨機性和盲目性。(3)針對粒子群算法參數(shù)配置問題,分別對慣性權重、學習因子、種群大小和迭代次數(shù)4個參數(shù)進行合理的設定,使粒子群算法更加適用于覆蓋表的生成。對于慣性權重,根據(jù)粒子的優(yōu)劣對慣性權重進行自適應調整,以粒子與當前全局最優(yōu)解之間的距離作為粒子優(yōu)劣的評價標準;對于學習因子,提出了一種學習因子動態(tài)調整策略,使得學習因子隨著不同的迭代過程進行改變;對種群大小和迭代次數(shù)進行深入探討,針對組合集大小設定相應的取值。為驗證本文所提出的改進策略的有效性,采用MATLAB編程實現(xiàn)本文提出的改進算法與原始算法進行實驗對比,實驗結果證明改進的算法在生成測試用例集規(guī)模和算法執(zhí)行時間上具有一定的優(yōu)勢。
[Abstract]:As a kind of software testing method based on specification, combinatorial testing aims to select a small number of effective test cases from the huge combination space of the software to be tested, so as to generate a set of test cases with high coverage and strong error-detection ability. However, combinatorial test case generation is a NP problem, which needs to be solved in polynomial time. Therefore, meta-heuristic search algorithm is needed to solve the problem. Compared with other meta-heuristic search algorithms, PSO is more competitive in the scale and execution time of overlay table generation. This paper systematically reviews and summarizes the existing research results of generating combinatorial test case sets using particle swarm optimization algorithm, aiming at variable strength combinatorial testing problem and particle swarm optimization algorithm parameter selection problem. Combining the improved one-test-at-a-time strategy with the adaptive particle swarm optimization (APSO), a combined test case generation method, which can deal with arbitrary coverage strength, is proposed. The main research work and contributions of this paper are summarized as follows: (1) aiming at the constraint problems existing in the actual software to be tested, a method similar to avoiding the selection strategy is proposed to pre-process the constraint conditions. Before generating test cases, the invalid combination of constraints is eliminated, the size of the combination set to be covered is reduced to a certain extent, and the error of fitness caused by the invalid combination is avoided. (2) aiming at the problem of one-test-at-a-time policy combination selection, In this paper, two priority measurement methods are proposed: overlay combination measure method and factor value measure method. In the process of generating a single test case, the combination with the largest weights is selected first for the generation of a single test case. The randomness and blindness of the original algorithm are avoided. (3) aiming at the parameter assignment problem of particle swarm optimization, four parameters such as inertia weight, learning factor, population size and iteration times are set reasonably. PSO is more suitable for generating overlay table. For inertial weight, the inertia weight is adaptively adjusted according to the particle's merits and demerits, and the distance between particle and the current global optimal solution is taken as the evaluation criterion of particle's superiority and inferiority. A dynamic adjustment strategy of learning factors is proposed to change the learning factors with different iterative processes, and the population size and iteration times are discussed in depth, and the corresponding values are set for the size of the combination set. In order to verify the effectiveness of the improved strategy proposed in this paper, the improved algorithm proposed in this paper is implemented by MATLAB programming and compared with the original algorithm. The experimental results show that the improved algorithm has some advantages in generating the size of test case set and the execution time of the algorithm.
【學位授予單位】：浙江理工大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP311.53;TP18
本文編號：2272563
[Abstract]:As a kind of software testing method based on specification, combinatorial testing aims to select a small number of effective test cases from the huge combination space of the software to be tested, so as to generate a set of test cases with high coverage and strong error-detection ability. However, combinatorial test case generation is a NP problem, which needs to be solved in polynomial time. Therefore, meta-heuristic search algorithm is needed to solve the problem. Compared with other meta-heuristic search algorithms, PSO is more competitive in the scale and execution time of overlay table generation. This paper systematically reviews and summarizes the existing research results of generating combinatorial test case sets using particle swarm optimization algorithm, aiming at variable strength combinatorial testing problem and particle swarm optimization algorithm parameter selection problem. Combining the improved one-test-at-a-time strategy with the adaptive particle swarm optimization (APSO), a combined test case generation method, which can deal with arbitrary coverage strength, is proposed. The main research work and contributions of this paper are summarized as follows: (1) aiming at the constraint problems existing in the actual software to be tested, a method similar to avoiding the selection strategy is proposed to pre-process the constraint conditions. Before generating test cases, the invalid combination of constraints is eliminated, the size of the combination set to be covered is reduced to a certain extent, and the error of fitness caused by the invalid combination is avoided. (2) aiming at the problem of one-test-at-a-time policy combination selection, In this paper, two priority measurement methods are proposed: overlay combination measure method and factor value measure method. In the process of generating a single test case, the combination with the largest weights is selected first for the generation of a single test case. The randomness and blindness of the original algorithm are avoided. (3) aiming at the parameter assignment problem of particle swarm optimization, four parameters such as inertia weight, learning factor, population size and iteration times are set reasonably. PSO is more suitable for generating overlay table. For inertial weight, the inertia weight is adaptively adjusted according to the particle's merits and demerits, and the distance between particle and the current global optimal solution is taken as the evaluation criterion of particle's superiority and inferiority. A dynamic adjustment strategy of learning factors is proposed to change the learning factors with different iterative processes, and the population size and iteration times are discussed in depth, and the corresponding values are set for the size of the combination set. In order to verify the effectiveness of the improved strategy proposed in this paper, the improved algorithm proposed in this paper is implemented by MATLAB programming and compared with the original algorithm. The experimental results show that the improved algorithm has some advantages in generating the size of test case set and the execution time of the algorithm.
【學位授予單位】：浙江理工大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP311.53;TP18
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