Shafiq Ahmad

Bibliometric analysis for process capability research

Abstract Bibliometric research focuses on the analysis of the bibliographic indicators quantitatively. It is a useful way for classifying information according to different variables, including journals, institutions and countries. This paper presents a general overview of research conducted in the field of process capability using bibliometric indicators. The main advantage of the current study is that the bibliometric indicators provide a broad picture and identify some of the most influential research conducted in the area of process capability. The analysis is divided into key sections focused on relevant journals, research papers, authors, institutions and countries. Web of Science (WOS) databases is the source of data for carrying out this bibliometric analysis. The study reveals that W.L. Pearn and C.W. Wu. are the two most influential authors in process capability research. On the other hand, Journal of Quality Technology, and Quality and Reliability Engineering International are the two most influential journals for publishing process capability researches. Furthermore, Taiwan is found to be the most influential country, followed by the U.S.A. in the process capability research.

A Framework for Software Defect Prediction and Metric Selection

Automated software defect prediction is an important and fundamental activity in the domain of software development. However, modern software systems are inherently large and complex with numerous correlated metrics that capture different aspects of the software components. This large number of correlated metrics makes building a software defect prediction model very complex. Thus, identifying and selecting a subset of metrics that enhance the software defect prediction method’s performance are an important but challenging problem that has received little attention in the literature. The main objective of this paper is to identify significant software metrics, to build and evaluate an automated software defect prediction model. We propose two novel hybrid software defect prediction models to identify the significant attributes (metrics) using a combination of wrapper and filter techniques. The novelty of our approach is that it embeds the metric selection and training processes of software defect prediction as a single process while reducing the measurement overhead significantly. Different wrapper approaches were combined, including SVM and ANN, with a maximum relevance filter approach to find the significant metrics. A filter score was injected into the wrapper selection process in the proposed approaches to direct the search process efficiently to identify significant metrics. Experimental results with real defect-prone software data sets show that the proposed hybrid approaches achieve significantly compact metrics (i.e., selecting the most significant metrics) with high prediction accuracy compared with conventional wrapper or filter approaches. The performance of the proposed framework has also been verified using a statistical multivariate quality control process using multivariate exponentially weighted moving average. The proposed framework demonstrates that the hybrid heuristic can guide the metric selection process in a computationally efficient way by integrating the intrinsic characteristics from the filters into the wrapper and using the advantages of both the filter and wrapper approaches.

A pilot study investigating the association between sleep and cognitive function among adolescents

OBJECTIVES To investigate the relationship between sleep and cognitive function among adolescent subjects in Riyadh. METHODS The sample consisted of 98 (44% female) subjects aged 10-16 years. Each participant filled in a well-structured pre-coded questionnaire regarding demographic data, including a sleep questionnaire; cognitive function was assessed using the Cambridge Neuropsychological Automated Battery (CANTAB). The cognitive function outcome variables were response times in the attention-switching task (AST) and the percentage of correct answers in the pattern recognition memory (PRM) task. RESULTS There were significant differences in measures of AST-latency (p=0.005), AST-congruent (p=0.012), and AST-incongruent (p=0.009), while no significant difference was found in the PRM task score (p=0.336) within gender groups. There was a significant correlation between sleep and AST switching cost (0.277, p=0.006) and sleep and AST latency (0.188, p=0.063) across the group. CONCLUSION This study showed that gender differences in cognitive function were significant in the group of adolescents. Additionally, this study shows that insufficient sleep can impair attention and accuracy in adolescents.