Accurate detection of software components that need to be exposed to additional verification and validation offers the path to high quality products while minimizing non essential software assurance expenditures. In this type of quality modeling we assume that software modules with known fault content developed in similar environment are available. Supervised learning algorithms are the traditional methods of choice for training on existing modules. The models are then used to predict fault content for newly developed software components prior to product release. However, one needs to realize that establishing whether a module contains a fault or not, only to be used for model training, can be expensive. The basic idea behind semi-supervised learning is to learn from a small number of software modules with known fault content and supplement model training with modules for which the fault information is not available, thus reducing the overall cost of quality assurance. In this study, we investigate the performance of semi-supervised learning for software fault prediction. A preprocessing strategy, multidimensional scaling, is embedded in the approach to reduce the dimensional complexity of software metrics used for prediction. Our results show that the dimension-reduction with semi-supervised learning algorithm preforms significantly better than one of the best performing supervised learning algorithm - random forest - in situations when few modules with known fault content are available. We compare our results with the published benchmarks and clearly demonstrate performance benefits.

Research in automated human gait recognition has largely focused on developing robust feature representation and matching algorithms. In this paper, we investigate the possibility of clustering gait patterns based on the features extracted by automated gait matchers. In this regard, a k-means based clustering approach is used to categorize the feature sets extracted by three different gait matchers. Experiments are conducted in order to determine if (a) the clusters of identities corresponding to the three matchers are similar, and (b) if there is a correlation between gait patterns within each cluster and physical attributes such as gender, body area, height, stride, and cadence. Results demonstrate that human gait patterns can be clustered, where each cluster is defined by identities sharing similar physical attributes. In particular, body area and gender are found to be the primary attributes captured by gait matchers to assess similarity between gait patterns. However, the strength of the correlation between clusters and physical attributes is different across the three matchers, suggesting that gait matchers "weight" attributes differently. The results of this study should be of interest to gait recognition and identification-at-a-distance researchers.

Accurate detection of fault prone modules offers the path to high quality software products while minimizing non essential assurance expenditures. This type of quality modeling requires the availability of software modules with known fault content developed in similar environment. Establishing whether a module contains a fault or not can be expensive. The basic idea behind semi-supervised learning is to learn from a small number of software modules with known fault content and supplement model training with modules for which the fault information is not available. In this study, we investigate the performance of semi-supervised learning for software fault prediction. A preprocessing strategy, multidimensional scaling, is embedded in the approach to reduce the dimensional complexity of software metrics. Our results show that the semi-supervised learning algorithm with dimension-reduction preforms significantly better than one of the best performing supervised learning algorithms, random forest, in situations when few modules with known fault content are available for training.

Graph-based learning provides a useful approach for modeling data in classification problems. In this modeling scenario, the relationship between labeled and unlabeled data impacts the construction and performance of classifiers, and therefore a semi-supervised learning framework is adopted. We propose a graph classifier based on kernel smoothing. A regularization framework is also introduced, and it is shown that the proposed classifier optimizes certain loss functions. Its performance is assessed on several synthetic and real benchmark data sets with good results, especially in settings where only a small fraction of the data are labeled.