Feature selections for effectively localizing faulty events in GUI applications - Xue, Xiaozhen; Pang, Yulei; Namin, Akbar Siami (International Conference on Machine Learning and Applications, 2014)
Due to the complex causality of failure and the special characteristics of test cases, the faults in GUI (Graphic User Interface) applications are difficult to localize. This paper adapts feature selection algorithms to localize GUI-related faults in a given program. Features are defined as the subsequences of events executed. By employing statistical feature ranking techniques, the events can be ranked by the suspiciousness of events being responsible to exhibit faulty behavior. The features defined in a given source code implementing (event handle) the underlying event are then ranked in suspiciousness order. The evaluation of the proposed technique based on some open source Java projects verified the effectiveness of this feature selection based fault localization technique for GUI applications. © 2014 IEEE.
10 citations (Crossref) [2023-10-31] tex.type: Proceedings paper
Keywords: Artificial intelligence; Fault localization; Feature extraction; Feature selection algorithm; GUI; GUI applications; Graphic user interface; Graphical user interfaces; Java programming language; Learning systems; Open source software; Open sources; Source codes; Statistical features; Test case; User interfaces; dept.mat; faults localization; feature selection

A clustering-based grouping model for enhancing collaborative learning - Pang, Yulei; Xiao, Feiya; Wang, Huaying; Xue, Xiaozhen (International Conference on Machine Learning and Applications, 2014)
Group work is widely used in tertiary institutions due to the considerable advantages of collaborative learning. Previous studies indicated that the group diversity had positive influence on the group work achievement. Therefore, how to achieve diversity within a group effectively and automatically is an interesting question. In this paper we propose a novel clustering-based grouping model. The proposed technique first employs balanced K-means algorithm to divide the students into several size-balanced clusters, such that the students within the same cluster are more similar (in some sense) to each other than to those in other clusters, then adopts one-sample-each-cluster strategy to construct the groups. We evaluated the proposed technique based on two small-scale case studies. The result observed may indicate that the clustering-based grouping model is feasible and effective. © 2014 IEEE.
9 citations (Crossref) [2023-10-31] tex.type: Proceedings paper
Keywords: Artificial intelligence; Case-studies; Collaborative learning; Group diversity; Grouping model; K-means; Learning systems; Novel clustering; Tertiary institutions; balanced K-means; collaborative learning; dept.mat; group diversity; k-Means algorithm