1. Combining techniques for software quality classification: An integrated decision network approach Ruben de Jong

2. Author Name: Nan-Hsing Chiu. Ching Yun University (Zhongli City, Taiwan) – Assistant professor. – Chairman of Department of Information Management. Specializes in: – Artificial Intelligence. – Software Engineering. – Engineering.

3. Software module quality Determine likelihood whether a software module contains faults. Software quality classification models – Takes feature values. – Output fault proneness (fp) or not fault proneness (nfp). How to combine these models? – Some models perform better on certain modules. – Simply take the average result?

4. Integrated decision network Feature values are used input for software quality classification models. – Models each get a result (fp or nfp).

5. Integrated decision network Particle swarm optimization is applied to search the optimal combination of results. – If the summed end value exceeds a certain threshold, the module is fp, otherwise it is nfp.

7. Related literature Software quality classification models: – Classification and Regression Trees (CART) model (Khoshgoftaar, Allen, Jones, & Hudepohl, 2000). – SPRINT model (Khoshgoftaar & Seliya, 2002). – Artificial Neural Networks (ANN) model (Thwin & Quah, 2005). Basis for integrated decision network. Multiple models are better than a single model (Jeffery et al., 2001).

8. Related literature Relying on a single model poses a risk (Macdonell & Shepperd, 2003). Early attempts at combining had a limited scope (Schapire, 1999). Combination through decision tree classifiers performed better than alternatives (Bouktif, Sahraoui, & Kégl, 2002).

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10. Questions?