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In this work, we introduce a Deep Neural Network model for automated software categorization. The model is ableto form high-level concepts from low-level code tokens andto distinguish important features such as API calls and identifiersin order to support software categorization. Our empirical evaluationshows that DNN outperformed other machine learning approacheswith 15.9–36.4% higher accuracy in...
Prior research shows that directly applying phrase-based SMT on lexical tokens to migrate Java to C# produces much semantically incorrect code. A key limitation is the use of sequences in phrase-based SMT to model and translate source code with well-formed structures. We propose mppSMT, a divide-and-conquer technique to address that with novel training and migration algorithms using phrase-based SMT...
n-gram statistical language model has been successfully applied to capture programming patterns to support code completion and suggestion. However, the approaches using n-gram face challenges in capturing the patterns at higher levels of abstraction due to the mismatch between the sequence nature in n-grams and the structure nature of syntax and semantics in source code. This paper presents GraLan,...
With the rapid development of peer-to-peer networks, simulation is considered as a useful tool to demonstrate initial ideas before building prototypes with real network transmissions. In flow-based simulation models of peer-to-peer networks, the algorithm for rate allocation which computes the per-flow rate for several competing flows at each peer in a given network topology is a fundamental component...
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