發(fā)布時間：2018-04-12 13:09

  本文選題：測試用例生成 + 擴展有限狀態(tài)機　； 參考：《南京大學》2015年博士論文

【摘要】：信息化技術的快速發(fā)展使得軟件系統(tǒng)的規(guī)模越來越大、更新換代的速度越來越快,開發(fā)過程中引入錯誤的可能性也越來越大。為了盡可能地減少在軟件產品中的錯誤,研究者和開發(fā)者們提出了許多軟件質量保障手段,然而到目前為止,軟件測試仍然是保證軟件質量和可靠性的最重要也是最有效的方法,對于保證和提高軟件產品的質量起著不可替代的重要作用,并逐漸受到人們越來越多的重視。軟件測試是一項耗時且開銷巨大的工作,使用自動化測試方法可以提高測試效率、減少測試代價。自動化測試已經成為當前軟件測試發(fā)展的趨勢,而其中一個關鍵就是自動化測試用例生成。自動化生成測試用例的一種常用方法是創(chuàng)建軟件的測試模型,并在模型的基礎上自動化生成測試用例。擴展有限狀態(tài)機(EFSM)模型以表達能力強、易于理解的特性使得在當前的測試研究領域受到了更多的關注。在擴展有限狀態(tài)機模型中,測試數據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,目前已經存在一些關于這方面的研究,但總體來說基于EFSM模型的測試數據生成技術仍然很不成熟。此外,目前的研究中較少考慮最終的測試用例集優(yōu)化問題,對于測試預言(Test Oracle)自動化生成的研究也還相對較少。針對上述問題,本文提出一種基于EFSM模型的自動化測試用例生成方法(ATGEM)。為了解決不可行路徑問題,我們提出了一種路徑可行性度量方法來預測路徑的可行性概率,使得在測試數據生成過程中盡可能避開不可行路徑,提高生成的效率。為了生成能夠觸發(fā)可行路徑的測試數據及相關的預言信息,我們通過開發(fā)可執(zhí)行模型來獲取運行時反饋信息,在此基礎上提出了一種新的適應度函數(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生成相應的測試用例�？蓤�(zhí)行模型定義了模型的動態(tài)行為,并通過使用語義執(zhí)行使得靜態(tài)模型具有類似動態(tài)程序的執(zhí)行能力。利用可執(zhí)行模型可以獲得運行時反饋信息計算適應度函數,就可以忽略不同數據類型帶來的差異性問題,使得該適應度函數能夠使用在多種數據類型中,因此適用范圍較廣�？蓤�(zhí)行模型帶來的另一個優(yōu)點是可以在模型動態(tài)執(zhí)行過程中自動化的生成預言信息。以往的研究表明,數量較少但較長的測試用例優(yōu)于數量較多但較短的測試用例,更容易發(fā)現系統(tǒng)中的錯誤。因此,在ATGEM方法還利用以上幾個子方法迭代來尋找一個具有較長路徑和較小路徑數量的較優(yōu)可行路徑集合。在提出以上的路徑可行性度量方法時,雖然已經考慮了路徑長度和可行性評估值的平衡問題,但仍然不夠精確。由于路徑的長度和可行性相互矛盾,找到較優(yōu)可行路徑集問題可以轉化為一個優(yōu)化平衡的問題。因此我們提出了一種基于多目標優(yōu)化(Multi-objective Optimization)的新方法ATGEMmot,并提出了兩個對應的適應度函數來獲取Pareto前沿解集。為了驗證提出的ATGEM和ATGEMmop方法,我們設計了一系列的實驗,將提出的兩種方法應用于多個EFSM模型,用來檢驗兩種方法中測試用例生成、路徑可行性度量方法和路徑集多目標優(yōu)化方法的有效性及效率,并實際檢驗模型中的不可行路徑的數量,分析原因。實驗結果表明ATGEM方法在路徑可行性度量、生成較優(yōu)可行路徑子集、生成測試數據和預言信息達到指定覆蓋方面是行之有效的。在測試數據生成方面,與現有方法相比,ATGEM方法在總體上具有更好的效率。ATGEMmop方法與ATGEM方法相比,除了時間效率上略有下降,其它指標上都占有優(yōu)勢,在生成具有較少數量、較長路徑的較優(yōu)測試用例集方面具有更好的表現。
[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.

【學位授予單位】：南京大學
【學位級別】：博士
【學位授予年份】：2015
【分類號】：TP311.53

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本文編號：1739882