E
Ebrahim Bagheri
Researcher at Ryerson University
Publications - 247
Citations - 6309
Ebrahim Bagheri is an academic researcher from Ryerson University. The author has contributed to research in topics: Software product line & Feature model. The author has an hindex of 26, co-authored 220 publications receiving 4873 citations. Previous affiliations of Ebrahim Bagheri include University of British Columbia & Nielsen Holdings N.V..
Papers
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Proceedings ArticleDOI
A detailed analysis of the KDD CUP 99 data set
TL;DR: A new data set is proposed, NSL-KDD, which consists of selected records of the complete KDD data set and does not suffer from any of mentioned shortcomings.
Journal ArticleDOI
Assessing the maintainability of software product line feature models using structural metrics
Ebrahim Bagheri,Dragan Gašević +1 more
TL;DR: Results obtained from the controlled experiment support the idea that useful prediction models can be built for the purpose of evaluating feature model maintainability using early structural metrics.
Journal Article
Evolutionary Search-Based Test Generation for Software Product Line Feature Models
TL;DR: This paper will show through the use of several publicly-available product line feature models that the proposed approach is able to generate test suites of O(n) size complexity as opposed to O(2n) while at the same time form a suitable tradeoff balance between error coverage and feature coverage in its generated test suites.
Journal ArticleDOI
Mining user interests over active topics on social networks
TL;DR: Comparison with state-of-the-art baselines on a real-world Twitter dataset demonstrates the effectiveness of the proposed graph-based link prediction schema in inferring users’ interests in terms of perplexity and in the context of retweet prediction application.
Book ChapterDOI
Evolutionary search-based test generation for software product line feature models
TL;DR: In this paper, an evolutionary testing approach based on Genetic Algorithms is proposed to explore the configuration space of a software product line feature model in order to automatically generate test suites, which is able to generate test suite of O(n) size complexity as opposed to O(2n).