rep., CMU/SEI and Lancaster University (2005)īaeza-Yates, R., Ribeiro-Neto, B.: Modern Information Retrieval: The Concepts and Technology Behind Search. IEEE, Malaysia (2008)Īraujo, J., Baniassad, E., Clements, P., Moreira, A., Rashid, A., Tekinerdogan, B.: Early Aspects: The Current Landscape. In particular, the tool achieved a remarkable recall regarding the detection of crosscutting concern effects.Īli, B, Kasirun, Z.: Developing tool for crosscutting concern identification using nlp.
Violet uml editor composed of vs aggregate of manual#
The REAssistant tool has been evaluated with several case-studies, showing good results when compared to a manual identification of concerns and a third-party tool. Then, REAssistant allows analysts to query these annotations via concern-specific rules in order to identify all the effects of a given concern.
To enable the analysis of use cases, we apply a tandem of advanced NLP techniques (e.g, dependency parsing, semantic role labeling, and domain actions) built on the UIMA framework, which generates different annotations for the use cases. In this work, we describe an automated tool called REAssistant that supports the extraction of semantic information from textual use cases in order to reveal latent crosscutting concerns. Nonetheless, existing tools cannot always detect all the crosscutting effects of a given concern on different requirements sections, as this detection requires a semantic analysis of the text. Concern mining tools based on NLP techniques can help in this activity. Thus, analysts must carefully review requirements documents in order to identify key concerns and their effects. However, this format of requirements often keeps relevant concerns (e.g., performance, synchronization, data access, etc.) from the analyst’s view because their semantics are implicit in the text. Textual requirements are very common in software projects.
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