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Many fault-proneness prediction models have been proposed in literature to identify fault-prone code in software systems. Most of the approaches use fault data history and supervised learning algorithms to build these models. However, since fault data history is not always available, some approaches also suggest using semi-supervised or unsupervised fault-proneness prediction models. The HySOM model,...
Over time, a software system's code and its underlying design tend to decay steadily and, in turn, to complicate the system's maintenance. In order to address that phenomenon, many researchers tried to help engineers predict parts of a system that are most likely to create problems while or even before they are modifying the system. Problems that creep into a system may manifest themselves as bugs,...
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