1:16 Y.Zhou et al. 因月 用 】 是n trntNrE 川 走 +3 围 用 超 1 ACM Transactions on Software Engineering and Methodology,Vol.27,No. 1,Article 1.Pub.date:April 2018.1:16 Y. Zhou et al. Table 2. Continued Project characteristics Key modeling components (challenges) covered Performance evaluation context Study Year Topic #Source projects (releases) #Target projects (releases) Languages in source and target projects Privatize data Homogenize features Filter instances Balance classes Transform distributions Select features Target training data Application scenario Main performance indicators Test data avail? Against SSM? Select for comp.? Turhan et al. [105] 2009 Fine-grained prediction 10 25 C/C++/Java Yes Ranking E2(70) Not No Cruz et al. [10] 2009 Distribution transforma￾tion 1 2 Java Yes Yes Classification Z* Not No Khoshgoftaar et al.[48] 2009 Multi-dataset classifier ensemble 7 7 C/C++/Java Yes Yes Classification NECM Partial Yes Watanabe et al. [110] 2008 Applicability of cross languages 2 2 C++/Java Yes Classification Recall, Precision Not No Nagappan et al. [74] 2006 Utility of complexity metrics 5 5 C++/C# Yes Yes Ranking S Not No Nagappan et al. [73] 2006 Utility of pro￾cess/product metrics 1 1 C/C++ Yes Yes Ranking S Not No Thongmak et al. [103] 2003 Utility of design metrics 1 1 N/A Yes Classification Accuracy Not No Briand et al. [9] 2002 Applicability of cross projects 2 2 Java Yes Both Corr., Comp., CECM Not Yes No TL: transfer Learning, CIL: class imbalance learning, SSM: simple size model. RCEC: represents the raw cost-effectiveness curve in a module-based Alberg diagram, in which the x-axis is the cumulative modules inspected and the y-axis is the cumulative defects found. CECM: Cost-effective curve in a module-based Alberg diagram (the x-axis is the cumulative number of the modules selected from the module ranking and the y-axis is the cumulative number of defects found in the selected modules). ACM Transactions on Software Engineering and Methodology, Vol. 27, No. 1, Article 1. Pub. date: April 2018