D. Antoniou

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Publications (3)0 Total impact

  • Article: Context-similarity based hotlinks assignment: Model, metrics and algorithm
    D. Antoniou, J. Garofalakis, C. Makris, Y. Panagis, E. Sakkopoulos
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    ABSTRACT: Enhancing web browsing experience is an open issue frequently dealt using hotlinks assignment between webpages, shortcuts from one node to another. Our aim is to provide a novel, more efficient approach to minimize the expected number of steps needed to reach expected pages when browsing a website. We present a randomized algorithm, which combines the popularity of the webpages, the website structure, and for the first time to the best authors’ knowledge, the similarity of context between pages in order to suggest the placement of suitable hotlinks. We verify experimentally that users need less page transitions to reach expected information pages when browsing a website, enhanced using the proposed algorithm.
    Data & Knowledge Engineering.
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    Article: A data mining methodology for evaluating maintainability according to ISO/IEC-9126 software engineering–product quality standard
    P Antonellis, D Antoniou, Y Kanellopoulos, C Makris, E Theodoridis, C Tjortjis, N Tsirakis
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    ABSTRACT: This paper presents ongoing work on using data mining to evaluate a software system's maintainability according to the ISO/IEC-9126 quality standard. More specifically it proposes a methodology for knowledge acquisition by integrating data from source code with the expertise of a software system's evaluators A process for the extraction of elements from source code and Analytical Hierarchical Processing for assigning weights to these data are provided; K-Means clustering is then applied on these data, in order to produce system overviews and deductions. The methodology is evaluated on Apache Geronimo, a large Open Source Application Server; results are discussed and conclusions are presented together with directions for future work.
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    Article: Clustering for Monitoring Software Systems Maintainability Evolution
    P. Antonellis, D. Antoniou, Y. Kanellopoulos, C. Makris, E. Theodoridis, C. Tjortjis, N. Tsirakis
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    ABSTRACT: This paper presents ongoing work on using data mining clustering to support the evaluation of software systems' maintainability. As input for our analysis we employ software measurement data extracted from Java source code. We propose a two-steps clustering process which facilitates the assessment of a system's maintainability at first, and subsequently an in-cluster analysis in order to study the evolution of each cluster as the system's versions pass by. The process is evaluated on Apache Geronimo, a J2EE 1.4 open source Application Server. The evaluation involves analyzing several versions of this software system in order to assess its evolution and maintainability over time. The paper concludes with directions for future work.
    Electronic Notes in Theoretical Computer Science.
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