Identifying Effective Test Cases through K-Means Clustering for Enhancing Regression Testing

Pang, Yu; Xue, Xiaozhen; Namin, Akbar Siami

doi:10.1109/icmla.2013.109

Cited by 34 publications

(13 citation statements)

References 11 publications

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“…In practice there are other ways to construct features, we would explore further in this direction. There exists some other classification techniques, like the ensemble methods we used in previous study [22] [23]. We would explore the possibility of applying them into vulnerable software component classification problem.…”

Section: Discussionmentioning

confidence: 98%

Predicting Vulnerable Software Components through N-Gram Analysis and Statistical Feature Selection

Pang

Xue

Namin

2015

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)

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Vulnerabilities need to be detected and removed from software. Although previous studies demonstrated the usefulness of employing prediction techniques in deciding about vulnerabilities of software components, the accuracy and improvement of effectiveness of these prediction techniques is still a grand challenging research question. This paper proposes a hybrid technique based on combining N-gram analysis and feature selection algorithms for predicting vulnerable software components where features are defined as continuous sequences of token in source code files, i.e., Java class file. Machine learning-based feature selection algorithms are then employed to reduce the feature and search space. We evaluated the proposed technique based on some Java Android applications, and the results demonstrated that the proposed technique could predict vulnerable classes, i.e., software components, with high precision, accuracy and recall.

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Section: Discussionmentioning

confidence: 98%

Predicting Vulnerable Software Components through N-Gram Analysis and Statistical Feature Selection

Pang

Xue

Namin

2015

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)

Self Cite

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“…Our work focuses on collective I/O scheduling, which is beneficial and critical to big data retrieval and analysis too. In the future, we would also apply machine learning algorithms [31,32] to further refine the scheduling.…”

Section: Data Organization and File Systemsmentioning

confidence: 99%

Hierarchical Collective I/O Scheduling for High-Performance Computing

2015

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The non-contiguous access pattern of many scientific applications results in a large number of I/O requests, which can seriously limit the data-access performance. Collective I/O has been widely used to address this issue. However, the performance of collective I/O could be dramatically degraded in today's high-performance computing systems due to the increasing shuffle cost caused by highly concurrent data accesses. This situation tends to be even worse as many applications become more and more data intensive. Previous research has primarily focused on optimizing I/O access cost in collective I/O but largely ignored the shuffle cost involved. Previous works assume that the lowest average response time leads to the best QoS and performance, while that is not always true for collective requests when considering the additional shuffle cost. In this study, we propose a new hierarchical I/O scheduling (HIO) algorithm to address the increasing shuffle cost in collective I/O. The fundamental idea is to schedule applications' I/O requests based on a shuffle cost analysis to achieve the optimal overall performance, instead of achieving optimal I/O accesses only. The algorithm is currently evaluated with the MPICH3 and PVFS2. Both theoretical analysis and experimental tests show that the proposed hierarchical I/O scheduling has a potential in addressing the degraded performance issue of collective I/O with highly concurrent accesses.

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“…It is easy to implement and apply this technique even on large data sets. Therefore, the k-mean clustering technique has been successfully applied in various area, ranging from statistics, data mining, and information technology [15]. However, in many real world clustering applications, the sizes of each cluster are known in advance due to the application requirements, in other words, ∑ =1 for each is fixed.…”

Section: B Balanced K-means Algorithmmentioning

confidence: 99%

A Clustering-Based Grouping Model for Enhancing Collaborative Learning

Pang

Xiao

Wang

et al. 2014

2014 13th International Conference on Machine Learning and Applications

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Groupwork 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 groupwork 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. 1 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.

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Identifying Effective Test Cases through K-Means Clustering for Enhancing Regression Testing

Cited by 34 publications

References 11 publications

Predicting Vulnerable Software Components through N-Gram Analysis and Statistical Feature Selection

Predicting Vulnerable Software Components through N-Gram Analysis and Statistical Feature Selection

Hierarchical Collective I/O Scheduling for High-Performance Computing

A Clustering-Based Grouping Model for Enhancing Collaborative Learning

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