本文關(guān)鍵詞：基于多目標粒子群算法的測試用例優(yōu)先級排序研究　出處：《西南大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 粒子群優(yōu)化算法 測試用例優(yōu)先級排序 多目標優(yōu)化 Additional策略

【摘要】：隨著社會信息化步伐的加快,計算機軟件在各行各業(yè)發(fā)揮的作用越來越大,軟件的質(zhì)量也受到越來越多的關(guān)注。軟件測試貫穿軟件的整個生命周期,是保證軟件質(zhì)量的重要方法和有效手段。由于客戶需求的變更,軟件規(guī)模的增大,軟件更新?lián)Q代速度的加快等原因使回歸測試用例集規(guī)模越來越龐大,也使回歸測試的工作量急劇增加。受人力、時間等回歸測試成本的限制,傳統(tǒng)的回歸測試方法很難勝任現(xiàn)在的回歸測試任務(wù)。因此,如何提高回歸測試效率成為了廣大研究人員關(guān)心的問題,其具有非常重要的研究價值和現(xiàn)實意義。研究人員就如何提高回歸測試效率提出了很多方法,主要包括測試用例選擇、測試用例集約簡和測試用例優(yōu)先級排序。其中,前兩種方法要對測試用例進行修改或刪除,這些不可逆的操作可能會遺漏能夠發(fā)現(xiàn)錯誤的測試用例。本文主要針對測試用例優(yōu)先級排序技術(shù)進行深入研究。通過分析當前國內(nèi)外測試用例優(yōu)先級排序的研究現(xiàn)狀,提出一種基于多目標粒子群算法的測試用例優(yōu)先級排序框架,并對如何克服粒子群優(yōu)化算法自身的不足進行了研究。以前的多目標測試用例優(yōu)先級排序大多是采用對多個優(yōu)化目標進行加權(quán)求和后再排序的方法,該方法權(quán)值分配受人的主觀思想影響大,不夠智能。基于進化算法進行搜索的方法比較可行,但進化算法本身的交叉和變異機制比較復(fù)雜,在遇到大規(guī)模問題時效率有所下降。本文設(shè)計了一種基于多目標粒子群算法的測試用例優(yōu)先級排序框架,選用APSC和EET作為優(yōu)化目標。針對多目標測試用例排序的具體情況,設(shè)計了粒子的編碼方式。由于標準粒子群優(yōu)化算法是基于連續(xù)空間進行搜索的,而二進制離散粒子群優(yōu)化算法的編碼方式和映射機制比較復(fù)雜,它們都不適合用于多目標測試用例排序,對此本文提出了一種在離散空間進行搜索的方法。在非支配排序方式上,為降低算法的時間復(fù)雜度,只選擇被支配數(shù)為0的解。粒子群優(yōu)化算法的優(yōu)點很多,但是它的不足之處是容易陷入局部最優(yōu)。對此,本文提出一種基于Additional策略的全局極值動態(tài)調(diào)整算法,從而引導(dǎo)粒子群朝著優(yōu)秀解的方向飛行。Additional策略是一種典型的貪心算法,它的特點是具有反饋機制。在更新全局極值之前,采用Additional策略的思想比較個體極值與全局極值的優(yōu)劣。即把已排序測試用例覆蓋的語句行去掉后再計算個體極值和全局極值的語句覆蓋率,然后結(jié)合執(zhí)行時間比較個體極值和全局極值的優(yōu)劣,選擇更好的作為新的全局極值。通過該方法動態(tài)地更新全局極值,引導(dǎo)粒子群的搜索方向,從而避免粒子群陷入局部最優(yōu)。最后選取SIR庫中的程序用于對比實驗。通過比較不同方法之間的Pareto最優(yōu)解集的分布和測試用例優(yōu)先級序列的APFD值,驗證了本文所提方法能夠獲得更優(yōu)的Pareto最優(yōu)解集和較高的錯誤檢測速率。
[Abstract]:With the acceleration of the pace of social informatization, computer software plays a more and more important role in various industries, and the quality of software is paid more and more attention. Software testing runs through the whole life cycle of software. It is an important method and effective means to guarantee the quality of software. Because of the change of customer demand, the increase of software scale and the speed of software renewal, the scale of regression test case set becomes larger and larger. Because of the limitation of manpower and time, the traditional regression test method is difficult to do the task of regression test. How to improve the efficiency of regression testing has become a concern of the majority of researchers, which has very important research value and practical significance. Researchers have put forward a lot of methods on how to improve the efficiency of regression testing. It mainly includes test case selection, test case reduction and priority ranking of test cases. Among them, the first two methods need to modify or delete test cases. These irreversible operations may omit test cases that can find errors. This paper mainly focuses on the test case priority ranking technology. Through the analysis of the current domestic and foreign test case priority ranking research. The status quo. A test case priority ranking framework based on multi-objective particle swarm optimization is proposed. And how to overcome the shortcomings of particle swarm optimization (PSO) algorithm is studied. Most of the previous multi-objective test cases priority ranking is based on the weighted summation of multiple optimization objectives before sorting. The method is not intelligent enough because of the influence of subjective thoughts. The search method based on evolutionary algorithm is feasible, but the crossover and mutation mechanism of evolutionary algorithm itself is complex. In this paper, we design a test case priority ranking framework based on multi-objective particle swarm optimization algorithm. APSC and EET are chosen as the optimization targets. According to the specific situation of multi-objective test case sorting, the particle coding method is designed. Because the standard particle swarm optimization algorithm is based on continuous space search. However, the binary discrete particle swarm optimization algorithm is not suitable for multi-objective test case sorting because of its complexity in coding and mapping mechanism. In this paper, we propose a search method in discrete space. In order to reduce the time complexity of the algorithm, we only choose the solution with the dominant number 0. The particle swarm optimization algorithm has many advantages. However, it is easy to fall into local optimum. In this paper, a global extremum dynamic adjustment algorithm based on Additional strategy is proposed. Thus, it is a typical greedy algorithm to guide the particle swarm to fly towards the direction of excellent solution. Its characteristic is that it has a feedback mechanism, before updating the global extremum. The idea of Additional strategy is used to compare the advantages and disadvantages of individual extremum and global extremum, that is to say, the sentence coverage rate of individual extremum and global extremum is calculated after the sentence lines covered by sorted test cases are removed. Then combining the execution time to compare the advantages and disadvantages of individual extremum and global extremum choose better as the new global extremum. Through this method the global extremum is dynamically updated to guide the search direction of PSO. Finally, the program in the SIR library is selected for the comparison experiment. By comparing the distribution of the Pareto optimal solution set among different methods and the AP of the test case priority sequence, we choose the program in the SIR library to compare the distribution of the Pareto optimal solution set and the AP of the test case priority sequence. FD value. It is verified that the proposed method can obtain better Pareto optimal solution set and higher error detection rate.
【學(xué)位授予單位】：西南大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP311.53;TP18

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