In this paper we present a proposal for a novel approach to facilitating transparent interoperability among reverse engineering tools. We characterize the architectural and operational characteristics of reverse engineering tools and demonstrate that many similarities exist among them. Taking full advantage of these similarities, we outline an approach for creating a domain ontology of operational and representational concepts for a given set of tools. A special adapter is proposed that makes use of this ontology to facilitate transparent interoperability among them.
Predictive models can be used in the detection of fault prone modules using source code metrics as inputs for the classifier. However, there exist numerous structural measures that capture different aspects of size, coupling and complexity. Identifying a metric subset that enhances the performance for the predictive objective would not only improve the model but also provide insights into the structural properties that lead to problematic modules. Another difficulty in building predictive models comes from unbalanced datasets, which are common in empirical software engineering as a majority of the modules are not likely to be faulty. Oversampling attempts to overcome this deficiency by generating new training instances from the faulty modules. We present the results of applying searchbased metric selection and oversampling to three NASA datasets. For these datasets, oversampling results in the largest improvement. Metric subset selection was able to reduce up to 52% of the metrics without decreasing the predictive performance gained with oversampling.
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