【摘要】：測試數(shù)據(jù)作為軟件測試中的核心因素,其生成效率高低直接影響著軟件測試的效果,本文主要對測試數(shù)據(jù)的自動生成方法進(jìn)行研究�；ǘ涫诜鬯惴ㄗ鳛橐环N具有良好尋優(yōu)能力的新型智能算法,因其參數(shù)簡單、容易實(shí)現(xiàn),已被成功應(yīng)用于各種多目標(biāo)優(yōu)化問題中,本文研究將此算法應(yīng)用于測試數(shù)據(jù)自動生成領(lǐng)域,首先針對其缺陷提出一系列改進(jìn)措施,再通過大量實(shí)驗(yàn)驗(yàn)證改進(jìn)后的算法在測試數(shù)據(jù)自動生成中的優(yōu)越性。本文工作內(nèi)容與創(chuàng)新點(diǎn)主要包括以下幾個方面:(1)針對基本花朵授粉算法搜索速度較慢、尋優(yōu)精度不高和在中后期容易陷入局部極值的缺陷,從對算法參數(shù)進(jìn)行調(diào)整和引入其他智能算法進(jìn)行混合兩大方向?qū)净ǘ涫诜鬯惴右愿倪M(jìn),提出一種自適應(yīng)混合花朵授粉算法,首先引入粒子群算法,利用粒子群算法在搜索初期階段收斂精度高與速度快的優(yōu)勢獲得一組質(zhì)量較優(yōu)的解作為花朵授粉算法的初始解來繼續(xù)實(shí)施迭代尋優(yōu)操作;其次,提出一個警衛(wèi)函數(shù)來對反映種群的離散程度;最后采取一種自適應(yīng)機(jī)制對解更新,自適應(yīng)機(jī)制包括自適應(yīng)柯西變異與自適應(yīng)步長因子兩部分,根據(jù)當(dāng)前種群的離散程度大小以及解的位置狀態(tài)自適應(yīng)地進(jìn)行尋優(yōu)搜索,從而提高尋優(yōu)能力。(2)對基本花朵授粉算法應(yīng)用于測試數(shù)據(jù)生成上的理論依據(jù)進(jìn)行分析,在此基礎(chǔ)上研究如何將本文提出的自適應(yīng)混合花朵授粉算法應(yīng)用于測試數(shù)據(jù)的自動生成中,建立基于自適應(yīng)混合花朵授粉算法的測試數(shù)據(jù)生成模型,同時提出了一種改進(jìn)的適應(yīng)度函數(shù)構(gòu)造方法,通過分支被覆蓋的難易程度不同來對每條分支分配相對應(yīng)的權(quán)重參數(shù)值,以更準(zhǔn)確地反映分支的覆蓋情況,從而進(jìn)一步提高測試數(shù)據(jù)的生成效率。(3)最后對本文提出的自適應(yīng)混合花朵授粉算法在測試數(shù)據(jù)自動生成中的可行性與效率性進(jìn)行驗(yàn)證,選取4種復(fù)雜程度不同的典型測試程序,借助MATLAB平臺對其實(shí)現(xiàn)測試數(shù)據(jù)自動生成,同已經(jīng)應(yīng)用于測試數(shù)據(jù)自動生成領(lǐng)域的另外兩種智能算法相比較,從平均耗費(fèi)時間、平均迭代次數(shù)和平均分支覆蓋比例3項(xiàng)數(shù)據(jù)指標(biāo)進(jìn)行對比分析。
[Abstract]:As the core factor in software testing, test data generation efficiency directly affects the effect of software testing. In this paper, the automatic generation method of test data is studied. As a new intelligent algorithm with good optimization ability, flower pollination algorithm has been successfully applied to various multi-objective optimization problems because of its simple parameters and easy implementation. This paper studies the application of this algorithm in the field of automatic generation of test data. Firstly, a series of improvement measures are put forward in view of its defects, and then the superiority of the improved algorithm in automatic generation of test data is verified by a large number of experiments. The contents and innovations of this paper mainly include the following aspects: (1) aiming at the defects of the basic flower pollination algorithm, the search speed is slow, the searching accuracy is not high and the local extremum is easy to fall into in the middle and late period. An adaptive hybrid flower pollination algorithm is proposed by improving the basic flower pollination algorithm by adjusting the parameters of the algorithm and introducing other intelligent algorithms into the hybrid algorithm. Firstly, the particle swarm optimization algorithm is introduced. Based on the advantages of high convergence accuracy and high speed in the initial stage of searching, a group of better quality solutions are obtained as the initial solution of the flower pollination algorithm to continue the iterative optimization operation. Secondly, a security function is proposed to reflect the discrete degree of the population. Finally, an adaptive mechanism is adopted to update the solution. The adaptive mechanism consists of two parts: adaptive Cauchy mutation and adaptive step size factor. According to the size of the population dispersion and the location of the solution, the adaptive search is carried out adaptively. In order to improve the ability of optimization. (2) the theoretical basis of basic flower pollination algorithm applied to test data generation is analyzed. On this basis, we study how to apply the adaptive hybrid flower pollination algorithm to the automatic generation of test data, and establish a test data generation model based on adaptive hybrid flower pollination algorithm. At the same time, an improved fitness function construction method is proposed to assign the corresponding weight parameter values to each branch according to the difficulty and ease of each branch, so as to reflect the coverage of each branch more accurately. In order to further improve the efficiency of test data generation. (3) finally, the feasibility and efficiency of adaptive hybrid flower pollination algorithm in automatic generation of test data are verified. Four typical test programs with different degrees of complexity are selected to generate test data automatically with the help of MATLAB platform. Compared with the other two intelligent algorithms that have been applied in the field of automatic generation of test data, the average time consumption is obtained. The average iteration times and the average branch coverage ratio were compared and analyzed.
【學(xué)位授予單位】：江西理工大學(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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