Zsuzsanna Marian

Learn More
This paper addresses the problem of software defect detection, an important problem which helps to improve the software systems’ maintainability and evolution. In order to detect defective entities within a software system, a self-organizing feature map is proposed. The trained map will be able to identify, using unsupervised learning, if a software module(More)
In this paper, we are approaching, from a machine learning perspective, the problem of automatically detecting defective software entities (classes and methods) in existing software systems, a problem of major importance during software maintenance and evolution. In order to improve the internal quality of a software system, identifying faulty entities such(More)
This paper focuses on the problem of defect prediction, a problem of major importance during software maintenance and evolution. It is essential for software developers to identify defective software modules in order to continuously improve the quality of a software system. As the conditions for a software module to have defects are hard to identify,(More)
Detecting defective entities from existing software systems is a problem of great importance for increasing both the software quality and the efficiency of software testing related activities. We introduce in this paper a novel approach for predicting software defects using fuzzy decision trees. Through the fuzzy approach we aim to better cope with noise(More)
We have considered the task to discover similarities in modern music by applying methods of Formal Concept Analysis, specifically those of Contextual Topology. For this, we investigate the Music Genome, a project started on the 6th of January 2000 by a group of musicians and music-loving technologists who came together with the idea of creating the most(More)
  • 1