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wiki:software:beuthbot:requirement-analysis [11.12.2019 14:24]
Christopher Lehmann 		wiki:software:beuthbot:requirement-analysis [23.01.2020 15:32] (aktuell)
Timo Bruns
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 ==== Use cases ==== ==== Use cases ====
 +
 +In the following we present three usecases in detail, which exemplarily describe our functional requirements.
  
 === Use case /F103/ === === Use case /F103/ ===
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 **Actor**: User **Actor**: User
  
-**Preconditions**: The chatbot is running+**Preconditions**: The chatbot, NLP component, gateway, registry and microservices are running
  
-**Basic flow**: The user writes a message to the bot via telegram. This message is processed and evaluated by the NLP component, then the message, including the evaluation of the NLP component, is persisted in the database and forwarded to a corresponding micro service, which then generates a response and sends it back.+**Basic flow**: The user writes a message to the bot via telegram. This message is processed and evaluated by the NLP component, then the message, including the evaluation of the NLP component, is persisted in the database and forwarded to a corresponding microservice, which then generates a response and sends it back.
  
 **Effects**: The user gets a reply from the chatbot, which refers to his message. **Effects**: The user gets a reply from the chatbot, which refers to his message.
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 === Use case /F200/ === === Use case /F200/ ===
  
-**Title**: User asks for today's menu of the Mensa+**Title**: User asks for today's menu of the mensa
    
-**Short description**: User sends a request to the chatbot that he would like to know what there is to eat in the cafeteria today.+**Short description**: User sends a request to the chatbot that he would like to know what there is to eat in the mensa today.
  
 **Actor**: User **Actor**: User
  
-**Preconditions**: The chatbot is running, the Mensa micro service is running, the gateway is running, the registry is running.+**Preconditions**: The chatbot, the NLP component, the Mensa micro service, the gateway and the registry are running.
  
-**Basic flow**: The user writes a message to the bot via telegram. The NLP component recognizes that the user wants to have today's menu of the Mensa. The evaluated message is forwarded to the Mensa micro service. The micro service reads out what is required and asks the OpenMensa API for the Mensaplan for the Beuth University of Applied Sciences. An answer is generated from the object which the microservice receives from the API and sent back to the user.+**Basic flow**: The user writes a message to the bot via telegram. The NLP component recognizes that the user wants to have today's menu of the mensa. The evaluated message is forwarded to the mensa microservice. The microservice reads out what is required and asks the OpenMensa API for the mensaplan for the Beuth University of Applied Sciences. An answer is generated from the object which the microservice receives from the API and sent back to the user.
  
-**Effects**: The user gets an answer from the chatbot containing today's menu of the refectory.+**Effects**: The user gets an answer from the chatbot containing today's menu of the mensa.
  
 === Use case /F300/ === === Use case /F300/ ===
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 **Preconditions**: The chatbot, NLP component, gateway, registry, and learning room service are running. **Preconditions**: The chatbot, NLP component, gateway, registry, and learning room service are running.
  
-**Basic flow**:  User writes to the chatbot that he wants to know which learning rooms there are. The system processes the message and forwards it to the learning room micro service. If the learning rooms have not yet been cached, the service uses web scraping to search for the required information on the corresponding website, generates a response from it and sends it to the user.+**Basic flow**:  User writes to the chatbot that he wants to know which learning rooms there are. The system processes the message and forwards it to the learning room microservice. If the learning rooms have not yet been cached, the service uses web scraping to search for the required information on the corresponding website, generates a response from it and sends it to the user.
  
 **Effects**: The user receives an answer from the chatbot containing the required information. **Effects**: The user receives an answer from the chatbot containing the required information.
 +<WRAP pagebreak></WRAP>
wiki/software/beuthbot/requirement-analysis.1576070670.txt.gz · Zuletzt geändert: 11.12.2019 14:24 von Christopher Lehmann

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