本文關(guān)鍵詞： 軟件故障診斷 多故障程序 概率圖模型 測試用例依賴性 故障傳播 故障遮掩　出處：《大連海事大學(xué)》2016年博士論文　論文類型：學(xué)位論文

【摘要】：自動化的軟件故障診斷技術(shù)對于保證軟件質(zhì)量起著至關(guān)重要的作用。然而現(xiàn)有的故障診斷技術(shù)大多假設(shè)程序只存在一個故障,這種假設(shè)在實(shí)際的程序中是不現(xiàn)實(shí)的。相比單故障而言,多故障程序固有的不確定性會產(chǎn)生更多更復(fù)雜的問題,使得現(xiàn)有的故障診斷方法的效果并不理想。本文通過分析程序切片、基于統(tǒng)計(jì)的故障定位、基于模型的軟件調(diào)試以及概率圖模型診斷等軟件故障診斷技術(shù)的研究現(xiàn)狀,在形式化多故障程序診斷問題模型的基礎(chǔ)上,針對多故障程序本身固有的不確定性問題,例如測試用例依賴性、故障傳播以及故障遮掩等,重點(diǎn)研究基于擴(kuò)展概率圖模型的概率診斷方法,取得了以下研究成果：(1)通過對多故障程序的測試用例依賴、故障傳播和故障遮掩問題的分析,提出感染圖及其概率診斷方法(IGADER)將BARINEL技術(shù)推進(jìn)一步。感染圖利用感染連接的概念從依賴關(guān)系角度描述語句之間的相互作用,在此基礎(chǔ)上IGADER識別沖突、產(chǎn)生并鑒別候選診斷。為驗(yàn)證IGADER的有效性,采用不同規(guī)模的單故障和多故障程序進(jìn)行實(shí)驗(yàn),實(shí)驗(yàn)結(jié)果表明IGADER的診斷精度好于經(jīng)典的Tarantula、Och iai以及BARINEL等方法。(2)基于程序語句之間的控制依賴和數(shù)據(jù)依賴關(guān)系,用馬爾可夫覆蓋建立基于因果模型的二層貝葉斯網(wǎng)絡(luò)模型——概率因果圖(PCEG)。通過基于Noisy-or的“自頂向下”推理以及基于標(biāo)準(zhǔn)貝葉斯的“自底向上”推理,能夠有效捕捉(循環(huán))程序在控制流和數(shù)據(jù)流上的故障傳播。采用同樣的程序,不同大小的測試用例集進(jìn)行實(shí)驗(yàn),證明PCEG相比Tarantula、Ochiai以及BARINEL方法更能刻畫語句之間深層次的因果關(guān)系,對測試用例的敏感性較低,能夠控制循環(huán)語句導(dǎo)致的相似執(zhí)行信息對診斷精確性的負(fù)面影響。(3)針對軟件開發(fā)中存在的“虛假依賴”問題,提出擴(kuò)展隱馬爾可夫模型及其概率診斷方法EHMM。EHMM把程序特征看作是“隱”變量,對每個失敗測試用例建立一個隱馬爾可夫模型,再通過在一組隱馬爾可夫模型上的推理來對所有“隱”變量的狀態(tài)進(jìn)行分類,并對分類后狀態(tài)為faulty的變量,計(jì)算其可疑度作為診斷結(jié)果。為了驗(yàn)證EHMM的有效性,特別對包含一個故障、兩個故障以及三個故障的帶有“虛假依賴”的真實(shí)程序進(jìn)行實(shí)驗(yàn)設(shè)計(jì),結(jié)果表明EHMM方法在處理帶有“虛假依賴”的程序時,診斷結(jié)果要好于PCEG、IGADER、Tarantula以及Ochiai等方法。(4)診斷系統(tǒng)實(shí)現(xiàn)與應(yīng)用方面,本文設(shè)計(jì)了一個集成IGADER、PCEG以及EHMM等概率診斷方法的診斷系統(tǒng)PGDS。該系統(tǒng)能夠應(yīng)用于實(shí)際的多故障程序診斷問題以及學(xué)生學(xué)習(xí)的認(rèn)知能力診斷問題,并取得很好的效果。
[Abstract]:Automated software fault diagnosis technology plays an important role in ensuring software quality. However, most of the existing fault diagnosis techniques assume that there is only one fault in the program. This assumption is not realistic in a real program. The inherent uncertainty of a multi-fault program creates more complex problems than a single failure. Through analyzing program slice, fault location based on statistics, software debugging based on model and probability diagram model diagnosis, the present situation of software fault diagnosis technology is discussed. On the basis of formalizing the model of multi-fault program diagnosis, the inherent uncertainty problems of multi-fault program, such as test case dependency, fault propagation and fault masking, are discussed. The probabilistic diagnosis method based on extended probabilistic graph model is mainly studied. The following research results are obtained: 1) through the analysis of test case dependence, fault propagation and fault masking of multi-fault program, The infection diagram and its probabilistic diagnosis method (IGADERA) are proposed to push the BARINEL technology forward. The infection diagram uses the concept of infection connection to describe the interaction between sentences from the angle of dependency, and then IGADER identifies conflicts. To verify the effectiveness of IGADER, experiments are carried out using single-fault and multi-fault programs of different sizes. The experimental results show that the diagnostic accuracy of IGADER is better than that of classical methods such as Tarantula Och iai and BARINEL. A two-layer Bayesian network model based on causality model, probabilistic causality diagram (PCEG), is established by using Markov covering. Through "top-down" reasoning based on Noisy-or and "bottom-up" reasoning based on standard Bayes, Can effectively capture (cyclic) programs on the control flow and data flow of fault propagation. Using the same program, different sizes of test cases set for the experiment, It is proved that PCEG can depict deeper causality between statements and is less sensitive to test cases than the Tarantula Ochiai and BARINEL methods. The ability to control the negative impact of similar execution information on diagnostic accuracy caused by loop statements.) the problem of "false dependencies" in software development, An extended hidden Markov model and its probabilistic diagnosis method, EHMM.EHMM, are proposed. The program feature is regarded as a "hidden" variable, and a hidden Markov model is established for each failure test case. Then, by reasoning on a set of hidden Markov models, the states of all "hidden" variables are classified, and the suspicious degree of variables whose state is faulty after classification is calculated as the diagnostic result. In particular, a real program with "false dependency" including one fault, two faults and three faults is designed. The results show that the EHMM method is used to deal with a program with a "false dependency". The results of diagnosis are better than those of PCEG IGADERA Tarantula and Ochiai. 4) the realization and application of the diagnostic system. In this paper, a diagnosis system, PGDS, which integrates IGADERPCEG and EHMM probabilistic diagnosis methods, is designed. The system can be applied to practical multi-fault program diagnosis problems and students' learning cognitive ability diagnosis problems, and has achieved good results.
【學(xué)位授予單位】：大連海事大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2016
【分類號】：TP311.53

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