Support vector machines for regression and applications to software quality prediction

Software metrics are the key tool in software quality management. In this paper, we propose to use support vector machines for regression applied to software metrics to predict software quality. In experiments we compare this method with other regression techniques such as Multivariate Linear Regression, Conjunctive Rule and Locally Weighted Regression. Results on benchmark dataset MIS, using mean absolute error, and correlation coefficient as regression performance measures, indicate that support vector machines regression is a promising technique for software quality prediction. In addition, our investigation of PCA based metrics extraction shows that using the first few Principal Components (PC) we can still get relatively good performance.

Aspects of the in vitro bioactivity and antimicrobial properties of Ag+- and Zn2+-exchanged 11 Å tobermorites

11 Å tobermorite, Ca5Si6O16(OH)2 · 4H2O, is a layer lattice ion exchange mineral whose potential as a carrier for Ag+ and Zn2+ ions in antimicrobial, bioactive formulations has not yet been explored. In view of this, the in vitro bioactivity of Ag+- and Zn2+-exchanged 11 Å tobermorites and their bactericidal action against S. aureus and P.aeruginosa are reported. The in vitro bioactivity of the synthetic unsubstituted tobermorite phase was confirmed by the formation of bone-like hydroxycarbonate apatite (HCA) on its surface within 48 h of contact with simulated body fluid. The substitution of labile Ag+ ions into the tobermorite lattice delayed the onset of HCA-formation to 72 h; whereas, the Zn2+-substituted phase failed to elicit an HCA-layer within 14 days. Both Ag+- and Zn2+-exchanged tobermorite phases were found to exhibit marked antimicrobial action against S. aureus and P.aeruginosa, two common pathogens in biomaterial-centred infections.