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摘要:軟件模塊缺陷預(yù)測(cè)技術(shù)在分析軟件質(zhì)量、平衡軟件成本方面起著重要的作用。2005年以來(lái)，支持向量機(jī)(SVM)開(kāi)始應(yīng)用到軟件模塊缺陷預(yù)測(cè)領(lǐng)域。由于軟件模塊缺陷度量數(shù)據(jù)集存在不平衡和噪聲等問(wèn)題，標(biāo)準(zhǔn)的支持向量機(jī)建立的預(yù)測(cè)模型的預(yù)測(cè)結(jié)果并不理想，本文主要對(duì)面向軟件模塊缺陷的支持向量機(jī)學(xué)習(xí)算法做了較為深入的研究，以此提高預(yù)測(cè)性能。本文的主要工作如下： 1. 對(duì)一種已有的基于模糊支持向量機(jī)的類(lèi)不平衡學(xué)習(xí)方法(FSVM_CIL)進(jìn)行研究。并將其應(yīng)用到了軟件模塊缺陷預(yù)測(cè)問(wèn)題上。與標(biāo)準(zhǔn)的支持機(jī)相比，F(xiàn)SVM_CIL在分類(lèi)器性能上有所改進(jìn)。 2. 提出基于模糊支持向量機(jī)和欠抽樣的類(lèi)不平衡學(xué)習(xí)算法(FSVM_CIL_RUS)。該算法將FSCM_CIL算法和隨機(jī)欠抽樣的算法相結(jié)合，在利用FSVM_CIL算法建立缺陷預(yù)測(cè)模型之前，先采用隨機(jī)欠抽樣的技術(shù)，平衡訓(xùn)練數(shù)據(jù)集的正負(fù)類(lèi)分布。在軟件模塊缺陷度量數(shù)據(jù)集上進(jìn)行實(shí)驗(yàn)，結(jié)果表明FSVM_CIL_RUS算法能夠有效地提高預(yù)測(cè)性能。 3. 提出基于模糊支持向量機(jī)的類(lèi)不平衡集成學(xué)習(xí)算法(FSVM_CIL_RBBag)。該算法將FSVM_CIL算法和集成學(xué)習(xí)方法相結(jié)合，利用FSVM_CIL建立基分類(lèi)器并進(jìn)行有效的集成，以此提高預(yù)測(cè)性能。在軟件模塊缺陷度量數(shù)據(jù)集上進(jìn)行實(shí)驗(yàn)，結(jié)果表明FSVM_CIL_RBBag算法是有效可行的。 關(guān)鍵字：支持向量機(jī)，缺陷預(yù)測(cè)，數(shù)據(jù)抽樣，集成學(xué)習(xí)
Abstract:Defect prediction techniques for software modules play an important role in software quality analysis and balancing software cost. Since 2005, support vector machine (SVM) has been applied into the area of defect prediction for software modules. Due to the software modules defect metric datasets have the characteristics, such as class imbalance and noise, the prediction models based on the normal SVM can’t get satisfactory results. Therefore, in this paper, we make a relatively in-depth study on support vector machine for predicting software module defects. The main works of this paper are as follows. 1. Study the existing Fuzzy Support Vector Machines for Class Imbalance Learning (FSVM_CIL) algorithm, and use it to build software module defect prediction models. Compared with normal SVM, FSVM_CIL has a best result on the prediction performance. 2. Propose an algorithm called FSCM_CIL_RUS. This algorithm combines the FSVM_CIL algorithm with random under sampling algorithm. Before building software module defect prediction models using FSVM_CIL, we balance the datasets using random under sampling. Experimental results on two software module defect metrics datasets show the effectiveness of the newly proposed algorithm. 3. Propose an ensemble algorithm called FSVM_CIL_RBBag. This algorithm combines the FSVM_CIL algorithm with roughly balanced bagging algorithm. Using FSVM_CIL algorithm to build base classifiers, and then we ensemble the base classifiers to improve the prediction performance. Experimental results on two software module defect metrics datasets validate the performance of proposed algorithm. Keywords: support vector machine (SVM), defect prediction, data sampling, Ensemble Learning
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