發(fā)布時(shí)間：2018-04-12 13:09

  本文選題：測(cè)試用例生成 + 擴(kuò)展有限狀態(tài)機(jī)��； 參考：《南京大學(xué)》2015年博士論文

【摘要】：信息化技術(shù)的快速發(fā)展使得軟件系統(tǒng)的規(guī)模越來(lái)越大、更新?lián)Q代的速度越來(lái)越快,開(kāi)發(fā)過(guò)程中引入錯(cuò)誤的可能性也越來(lái)越大。為了盡可能地減少在軟件產(chǎn)品中的錯(cuò)誤,研究者和開(kāi)發(fā)者們提出了許多軟件質(zhì)量保障手段,然而到目前為止,軟件測(cè)試仍然是保證軟件質(zhì)量和可靠性的最重要也是最有效的方法,對(duì)于保證和提高軟件產(chǎn)品的質(zhì)量起著不可替代的重要作用,并逐漸受到人們?cè)絹?lái)越多的重視。軟件測(cè)試是一項(xiàng)耗時(shí)且開(kāi)銷(xiāo)巨大的工作,使用自動(dòng)化測(cè)試方法可以提高測(cè)試效率、減少測(cè)試代價(jià)。自動(dòng)化測(cè)試已經(jīng)成為當(dāng)前軟件測(cè)試發(fā)展的趨勢(shì),而其中一個(gè)關(guān)鍵就是自動(dòng)化測(cè)試用例生成。自動(dòng)化生成測(cè)試用例的一種常用方法是創(chuàng)建軟件的測(cè)試模型,并在模型的基礎(chǔ)上自動(dòng)化生成測(cè)試用例。擴(kuò)展有限狀態(tài)機(jī)(EFSM)模型以表達(dá)能力強(qiáng)、易于理解的特性使得在當(dāng)前的測(cè)試研究領(lǐng)域受到了更多的關(guān)注。在擴(kuò)展有限狀態(tài)機(jī)模型中,測(cè)試數(shù)據(jù)的生成還存在許多挑戰(zhàn),并且通常和路徑的可行性緊密相關(guān),目前已經(jīng)存在一些關(guān)于這方面的研究,但總體來(lái)說(shuō)基于EFSM模型的測(cè)試數(shù)據(jù)生成技術(shù)仍然很不成熟。此外,目前的研究中較少考慮最終的測(cè)試用例集優(yōu)化問(wèn)題,對(duì)于測(cè)試預(yù)言(Test Oracle)自動(dòng)化生成的研究也還相對(duì)較少。針對(duì)上述問(wèn)題,本文提出一種基于EFSM模型的自動(dòng)化測(cè)試用例生成方法(ATGEM)。為了解決不可行路徑問(wèn)題,我們提出了一種路徑可行性度量方法來(lái)預(yù)測(cè)路徑的可行性概率,使得在測(cè)試數(shù)據(jù)生成過(guò)程中盡可能避開(kāi)不可行路徑,提高生成的效率。為了生成能夠觸發(fā)可行路徑的測(cè)試數(shù)據(jù)及相關(guān)的預(yù)言信息,我們通過(guò)開(kāi)發(fā)可執(zhí)行模型來(lái)獲取運(yùn)行時(shí)反饋信息,在此基礎(chǔ)上提出了一種新的適應(yīng)度函數(shù)(Fitness Function),并利用分散搜索(Scatter Search, SS)算法在排序后的候選路徑集中挑選可行路徑并自動(dòng)生成相應(yīng)的測(cè)試用例�？蓤�(zhí)行模型定義了模型的動(dòng)態(tài)行為,并通過(guò)使用語(yǔ)義執(zhí)行使得靜態(tài)模型具有類(lèi)似動(dòng)態(tài)程序的執(zhí)行能力。利用可執(zhí)行模型可以獲得運(yùn)行時(shí)反饋信息計(jì)算適應(yīng)度函數(shù),就可以忽略不同數(shù)據(jù)類(lèi)型帶來(lái)的差異性問(wèn)題,使得該適應(yīng)度函數(shù)能夠使用在多種數(shù)據(jù)類(lèi)型中,因此適用范圍較廣�？蓤�(zhí)行模型帶來(lái)的另一個(gè)優(yōu)點(diǎn)是可以在模型動(dòng)態(tài)執(zhí)行過(guò)程中自動(dòng)化的生成預(yù)言信息。以往的研究表明,數(shù)量較少但較長(zhǎng)的測(cè)試用例優(yōu)于數(shù)量較多但較短的測(cè)試用例,更容易發(fā)現(xiàn)系統(tǒng)中的錯(cuò)誤。因此,在ATGEM方法還利用以上幾個(gè)子方法迭代來(lái)尋找一個(gè)具有較長(zhǎng)路徑和較小路徑數(shù)量的較優(yōu)可行路徑集合。在提出以上的路徑可行性度量方法時(shí),雖然已經(jīng)考慮了路徑長(zhǎng)度和可行性評(píng)估值的平衡問(wèn)題,但仍然不夠精確。由于路徑的長(zhǎng)度和可行性相互矛盾,找到較優(yōu)可行路徑集問(wèn)題可以轉(zhuǎn)化為一個(gè)優(yōu)化平衡的問(wèn)題。因此我們提出了一種基于多目標(biāo)優(yōu)化(Multi-objective Optimization)的新方法ATGEMmot,并提出了兩個(gè)對(duì)應(yīng)的適應(yīng)度函數(shù)來(lái)獲取Pareto前沿解集。為了驗(yàn)證提出的ATGEM和ATGEMmop方法,我們?cè)O(shè)計(jì)了一系列的實(shí)驗(yàn),將提出的兩種方法應(yīng)用于多個(gè)EFSM模型,用來(lái)檢驗(yàn)兩種方法中測(cè)試用例生成、路徑可行性度量方法和路徑集多目標(biāo)優(yōu)化方法的有效性及效率,并實(shí)際檢驗(yàn)?zāi)Ｐ椭械牟豢尚新窂降臄?shù)量,分析原因。實(shí)驗(yàn)結(jié)果表明ATGEM方法在路徑可行性度量、生成較優(yōu)可行路徑子集、生成測(cè)試數(shù)據(jù)和預(yù)言信息達(dá)到指定覆蓋方面是行之有效的。在測(cè)試數(shù)據(jù)生成方面,與現(xiàn)有方法相比,ATGEM方法在總體上具有更好的效率。ATGEMmop方法與ATGEM方法相比,除了時(shí)間效率上略有下降,其它指標(biāo)上都占有優(yōu)勢(shì),在生成具有較少數(shù)量、較長(zhǎng)路徑的較優(yōu)測(cè)試用例集方面具有更好的表現(xiàn)。
[Abstract]:The rapid development of information technology makes the software system more and more large scale, faster replacement, in the process of developing into the possibility of error is also growing. In order to minimize the error in the software product, researchers and developers have put forward a lot of software quality assurance means, yet so far still, software testing is to ensure software quality and reliability of the most important and effective method for ensuring and plays an irreplaceable role in improving the quality of software products, and gradually paid more and more attention. Software testing is a time-consuming and expensive work, the use of automated testing methods can improve the test efficiency, reduce test cost. Automated testing has become the development trend of software testing, and one of the key is the automated test case generation automatically. A common method of test case generation is to create software testing model, and automated test case generation based on the model. The extended finite state machine (EFSM) model with strong expression ability, easy to understand the characteristics of received more attention in the research field of the test. In the extended finite state machine model in the test data generation there are still many challenges, and usually the feasibility and path are closely related, there have been some studies on that, but overall, based on the test data of EFSM model generation technology is still immature. In addition, the current study is seldom considered in the final set of test cases for the optimization problem. Test Oracle (Test Oracle) on automatic generation is also relatively small. In view of the above problems, this paper presents a Cheng Fangfa automation test case based on EFSM model (ATG EM). In order to solve the infeasible path problem, we propose a feasible path metric method to predict the probability of the path in the test data generation process as far as possible to avoid the infeasible path, improve the generating efficiency. In order to test data generation can trigger the feasible path and the predicted information, we can feedback the execution model to get running through the development, this paper puts forward a new fitness function (Fitness Function), and the use of scatter search (Scatter Search SS) algorithm in the sorted candidate path chosen feasible path and automatically generate the corresponding test cases. The executable defines the dynamic behavior model by using semantic model, and makes the static execution model has similar dynamic program execution ability. The executable model can obtain runtime feedback information to calculate fitness Should the function, you can ignore the differences of different data types, the fitness function can be used in a variety of data types, so the application range is wide. Another advantage of the execution model is brought about by the generation can be automated in the dynamic model in the implementation of the predicted information. Previous studies have shown that the number of but fewer test cases than a large number of long but shorter test cases, easier to find errors in the system. Therefore, the sub iteration method in ATGEM method is also used for more than a few to find a better and feasible path set has a number of smaller and longer path path. The path feasibility in the above measure. Although the balance of the path length and the feasibility evaluation value has been considered, but it is still not accurate. Because of the length and the feasibility of the path are contradictory, find the better road Set size problem can be transformed into an optimization balance. We propose a multi-objective optimization based on ATGEMmot (Multi-objective Optimization) of the new method, and puts forward two corresponding fitness function to obtain the Pareto front solution set. ATGEM and ATGEMmop in order to validate the proposed approach, we designed a series of this experiment, two methods are applied to the multiple EFSM model, test case generation used to test two kinds of methods, the effectiveness and efficiency of the feasibility of the path metrics and path optimization method in multi targets, and the actual number of inspection, in the model of infeasible path analysis. The experimental results show that the ATGEM method in measurement the feasibility of the path generating optimal feasible path subset, generate test data and information to achieve the specified coverage predictions is effective. In the test data generation, and the existing methods Compared with the ATGEM method, the ATGEM method has better efficiency in general. Compared with the ATGEM method, the.ATGEMmop method has advantages over other indicators, and has better performance in generating better test suite with fewer number and longer path.

【學(xué)位授予單位】：南京大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2015
【分類(lèi)號(hào)】：TP311.53

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