Mohammed Zagane

Work place: University of Oran 1 Ahmed Ben Bella, Oran, Algeria



Research Interests: Computer Networks, Computer Architecture and Organization, Computer systems and computational processes, Software Engineering, Information Security, Network Security


Mohammed Zagane Holds a magister degree in computer science from the higher school of computer science, Algiers, Algeria. He is currently, assistant professor for the computer science department at Mascara University, Mascara, Algeria, and researcher in RIIR laboratory at University of Oran 1 Ahmed Ben Bella, Oran, Algeria.  His research interests include: software engineering, computer security, machine and deep learning.

Author Articles
Evaluating and Comparing Size, Complexity and Coupling Metrics as Web Applications Vulnerabilities Predictors

By Mohammed Zagane Mustapha Kamel Abdi

DOI:, Pub. Date: 8 Jul. 2019

Most security and privacy issues in software are related to exploiting code vulnerabilities. Many studies have tried to find the correlation between the software characteristics (complexity, coupling, etc.) quantified by corresponding code metrics and its vulnerabilities and to propose automatic prediction models that help developers locate vulnerable components to minimize maintenance costs. The results obtained by these studies cannot be applied directly to web applications because a web application differs in many ways from a non-web application: development, use, etc. and a lot of evaluation of these conclusions has to be made. The purpose of this study is to evaluate and compare the vulnerabilities prediction power of three types of code metrics in web applications.  There are a few similar studies that targeted non-web application and to the best of our knowledge, there are no similar studies that targeted web applications. The results obtained show that unlike non-web applications where complexity metrics have better vulnerability prediction power, in web applications the metrics that give better prediction are the coupling metrics with high recall (> 75%) and fewer costs in terms of inspection (<25%).

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