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Context: Software development effort estimation (SDEE) is the process of predicting the effort required to develop a software system. In order to improve estimation accuracy, many researchers have proposed machine learning (ML) based SDEE models (ML models) since 1990s. However, there has been no attempt to analyze the empirical evidence on ML models in a(More)
Software development cost overruns often induce project managers to cut down manpower cost at the expense of software quality. Accurate effort estimation is beneficial to the prevention of cost overruns. Analogy-based effort estimation predicts the effort of a new project by using the information of its similar historical projects, where the similarity is(More)
The models developed to date for knowledge base embedding are all based on the assumption that the relations contained in knowledge bases are binary. For the training and testing of these embedding models, multi-fold (or n-ary) relational data are converted to triples (e.g., in FB15K dataset) and interpreted as instances of binary relations. This paper(More)
Rapid industrialization in the past few decades has necessitated the ever increasing demand for newer technologies leading to the dramatic development of sophisticated software for cost estimation and is expected to grow manifold in the forthcoming years. The improper understanding of software requirements has often resulted in inaccurate cost estimation.(More)
An earthquake-forecasting attempt is presented in this work via combining the weights and structure policy (WASP) and addition-subtraction frequency (ASF) algorithms. Specifically, based on the application of three-layer feedforward neuronets equipped with WASP algorithm, further using ASF algorithm, this work attempts to forecast a Japan earthquake with Mj(More)
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