No abstract

No abstract

No abstract

Feature modelling is a key technique for identifying common and variable features in software (software component families). The result of feature modelling is a feature model: a concise specification of product features and their relationships. Feature models have been proven to be useful for software variability modelling and management. However, there is a wide gap between feature models and program source code. Here we focus on reverse engineering of source code to feature models. We present a framework for the automated derivation of feature models from the existing software artefacts (components, libraries, etc.), which includes a formal description of a feature model, a program-feature relation meta-model, and a method for feature model generation based on feature dependency extraction and clustering. Feature models are generated in Feature Description Language (FDL) and as Prolog rules.

Internet can be misused by cyber criminals as a platform to conduct illegitimate activities (such as harassment, cyber bullying, and incitement of hate or violence) anonymously. As a result, authorship analysis of anonymous texts in Internet (such as emails, forum comments) has attracted significant attention in the digital forensic and text mining communities. The main problem is a large number of possible of authors, which hinders the effective identification of a true author. We interpret open class author attribution as a process of expert recommendation where the decision support system returns a list of suspected authors for further analysis by forensics experts rather than a single prediction result, thus reducing the scale of the problem. We describe the task formally and present algorithms for constructing the suspected author list. For evaluation we propose using a simple Winner-Takes-All (WTA) metric as well as a set of gain-discount model based metrics from the information retrieval domain (mean reciprocal rank, discounted cumulative gain and rank-biased precision). We also propose the List Precision (LP) metric as an extension of WTA for evaluating the usability of the suspected author list. For experiments, we use our own dataset of Internet comments in Lithuanian language and consider the use of language-specific (Lithuanian) lexical features together with general lexical features derived from English language. For classification we use one-class Support Vector Machine (SVM) classifier. The results of experiments show that the usability of open class author attribution can be improved considerably by using a set of language-specific lexical features together with general lexical features, while the proposed method can be used to reduce the number of suspected authors thus alleviating the work of forensic linguists.