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.

‘The trouble with Year 10’: 13 – 16 year old school students’ attitudes to higher education

This study looked at the impact of Widening Participation interventions on the attitudes of young people towards higher education. A total of 2731 adolescents aged 13–16 years completed a self-report measure of their attitudes to higher education, general and academic self concept and identification with school, family and peers. This was matched with data on the students’ academic attainment and social backgrounds. As expected, attainment scores were significantly positively correlated with take up of Widening Participation activities aimed at increasing participation in higher education, attitudes towards going to university and academic motivation. However, attainment was negatively correlated with perceptions of family attending university and identification with family. Regression analyses found that perceptions of family views about attending university were not a predictor of taking part in Widening Participation activities but were a predictor of attitudes towards higher education. Students in Year 10 aged 14–15 were significantly more negative on most factors than either older or younger students.