Extraction of System States from Natural Language Requirements
| Autor: | Florian Pudlitz, Florian Brokhausen, Andreas Vogelsang |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
system states
business.industry Computer science named-entity recognition natural language requirements Software requirements specification Machine learning computer.software_genre Field (computer science) Task (project management) Natural language requirements Software 006 Spezielle Computerverfahren state extraction Task analysis Software system Artificial intelligence ddc:004 business computer ddc:006 004 Datenverarbeitung Informatik Natural language |
| Zdroj: | RE |
| DOI: | 10.14279/depositonce-8717.2 |
| Popis: | In recent years, simulations have proven to be an important means to verify the behavior of complex software systems. The different states of a system are monitored in the simulations and are compared against the requirements specification. So far, system states in natural language requirements cannot be automatically linked to signals from the simulation. However, the manual mapping between requirements and simulation is a time-consuming task. Named-entity Recognition is a sub-task from the field of automated information retrieval and is used to classify parts of natural language texts into categories. In this paper, we use a self-trained Named-entity Recognition model with Bidirectional LSTMs and CNNs to extract states from requirements specifications. We present an almost entirely automated approach and an iterative semi-automated approach to train our model. The automated and iterative approach are compared and discussed with respect to the usual manual extraction. We show that the manual extraction of states in 2,000 requirements takes nine hours. Our automated approach achieves an F1-score of 0.51 with 15 minutes of manual work and the iterative approach achieves an F1-score of 0.62 with 100 minutes of work. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|