本文關(guān)鍵詞： 粒子群算法 自動(dòng)化測(cè)試 測(cè)試用例 嵌入式軟件 數(shù)據(jù)流依賴　出處：《浙江大學(xué)》2017年碩士論文　論文類(lèi)型：學(xué)位論文

【摘要】：隨著應(yīng)用軟件的需求和規(guī)模不斷增大,自動(dòng)化測(cè)試早已變成軟件測(cè)試的主流趨勢(shì)。傳統(tǒng)的人工生成測(cè)試用例的方法產(chǎn)生的用例較少,且耗時(shí)耗力,需要高水平且經(jīng)驗(yàn)豐富的測(cè)試人員來(lái)保證代碼覆蓋率。在軟件的快速迭代過(guò)程中,手工的生成方法往往會(huì)影響軟件測(cè)試的進(jìn)度。然而由于待測(cè)程序的多樣性和測(cè)試用例的不確定性,如何高效地自動(dòng)生成覆蓋率高、盡量精簡(jiǎn)的測(cè)試用例集,這成為一項(xiàng)技術(shù)上的難點(diǎn)。而由于測(cè)試用例的生成為不可判定性問(wèn)題,很多搜索算法都存在著固有的缺陷與不足。粒子群算法具有簡(jiǎn)單易用、計(jì)算迅速、魯棒性好的優(yōu)點(diǎn),但是傳統(tǒng)的粒子群算法也存在著容易陷入局部極值、出現(xiàn)早熟收斂或停滯等現(xiàn)象的缺點(diǎn)。本文針對(duì)程序中的數(shù)據(jù)流依賴關(guān)系,結(jié)合提出的進(jìn)化性結(jié)構(gòu)測(cè)試系統(tǒng),對(duì)粒子群算法進(jìn)行了四個(gè)方面的改進(jìn):粒子群初始化、鄰域拓?fù)�、參�?shù)設(shè)置和混合策略。對(duì)算法的主要的改進(jìn)包括采用非均勻隨機(jī)分布的方式來(lái)初始化粒子群,結(jié)合全面學(xué)習(xí)粒子算法來(lái)對(duì)鄰域進(jìn)行拓?fù)?采取收縮因子而非慣性系數(shù)的速度更新方法,以及算法各項(xiàng)相關(guān)參數(shù)的取值設(shè)置。最后做實(shí)驗(yàn)對(duì)比改進(jìn)后的粒子群算法與作為基準(zhǔn)的隨機(jī)算法,證明本文的算法確實(shí)在測(cè)試用例的生成效率以及生成用例集的精簡(jiǎn)程度上有了一定的提升。然后將本文設(shè)計(jì)的測(cè)試系統(tǒng)應(yīng)用于實(shí)驗(yàn)室的一個(gè)綜合測(cè)試驗(yàn)證平臺(tái)中,結(jié)合基于場(chǎng)景的測(cè)試方法,對(duì)某DSP的CAN通信程序進(jìn)行測(cè)試用例的自動(dòng)生成,最終獲得了覆蓋率高且較為精簡(jiǎn)的測(cè)試用例集。
[Abstract]:With the increasing demand and scale of application software, automated testing has become the mainstream trend of software testing. High level and experienced testers are needed to ensure code coverage. During the rapid iteration of the software. Manual generation methods often affect the progress of software testing. However, due to the diversity of test programs and the uncertainty of test cases, how to generate test cases with high coverage and concise test cases efficiently and automatically. This has become a technical difficulty. However, because the test case generation is indeterminate, many search algorithms have inherent defects and shortcomings. Particle swarm optimization algorithm is simple and easy to use. The advantages of good robustness, but the traditional particle swarm optimization algorithm also has some shortcomings such as easy to fall into local extremum, premature convergence or stagnation, etc. This paper aims at the data stream dependency in the program. Combined with the proposed evolutionary structure testing system, the particle swarm optimization algorithm is improved in four aspects: particle swarm initialization, neighborhood topology. Parameter setting and hybrid strategy. The main improvements of the algorithm include the use of non-uniform random distribution to initialize the particle swarm optimization, combined with the comprehensive learning particle algorithm to carry out the topology of the neighborhood. The velocity updating method of shrinkage factor instead of inertia coefficient is adopted, and the value setting of the relevant parameters of the algorithm is set. Finally, the improved particle swarm optimization algorithm is compared with the random algorithm as a benchmark. It is proved that the algorithm in this paper has a certain improvement in the efficiency of test case generation and the simplification of generating use case set. Then, the test system designed in this paper is applied to a comprehensive test verification platform in the laboratory. . Combined with the test method based on the scenario, the test cases of a DSP CAN communication program are generated automatically, and a set of test cases with high coverage and relatively simple coverage is obtained.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TP311.53;TP18

【參考文獻(xiàn)】

相關(guān)期刊論文 前4條

1 殷永峰;劉斌;姜同敏;;基于UML的嵌入式軟件測(cè)試用例生成方法研究[J];計(jì)算機(jī)應(yīng)用研究;2008年10期

2 ;Testing Result Statistics-Based Rapid Testing Method for Safety-Critical System[J];Journal of Electronic Science and Technology of China;2008年03期

3 薛明志;左秀會(huì);鐘偉才;劉靜;;正交微粒群算法[J];系統(tǒng)仿真學(xué)報(bào);2005年12期

4 單錦輝,姜瑛,孫萍;軟件測(cè)試研究進(jìn)展[J];北京大學(xué)學(xué)報(bào)(自然科學(xué)版);2005年01期

，

本文編號(hào)：1467986