How to use oRus?

Data

oRus takes user stories need to follow the original template of:

I as a <who>, want to <what>, so that <why>

Stories must be written in a text file, with one story per line. Having caps or punctuation doesn’t affect the result. User stories can only be processed in English language. The following is a fragment of a file:

As a manager, I want to read working hours per employee from our database, so that we are working with current assignments.
As a manager, I want to use the current job assignments from our database, so that we use the most recent data.
As a manager, I want to read employee salaries from our HR database, so that we use up-to-date values.

Processing

The format of your user stories determines how much they can be processed:

Software engineering stories (traditional ones) can only be split into who-what-why, using the function readStories().
Operational Research stories must also follow the keywords and structure discussed in [2]. As a result, you can divide stories into who-what-why, and then further into type of element (input, output, objective, constraint or scenario), and perform further analysis.

There are two options for processing the user stories for Operational Research. The first one is a completely automated process, that can be called as follows:

# CASE 1: DOING EVERYTHING AT ONCE

# You can call this function and do the whole process at a single time
# This returns two dataframes as a list, and also generates the excel report, and a pretty markdown report
stories <- analyseStories("myTextFile.txt", groupsNumber = 7, topGroups = 1, 
                          sheetFilePath = paste0(tempdir(), "myExcelReport.xlsx"),
                          reportFilePath = paste0(tempdir(), "myPrettyReport.html"),
                          outputType = "html_document")

The second allows you to analyse the stories, and then generate the report independently. The following is a code example:

# CASE 2: FIRST PROCESSING, THEN REPORT

# Otherwise, you can only get the dataframe
stories2 <- analyseStories("myTextFile.txt", groupsNumber = 7, topGroups = 1)

# And you can write the reports later
reportStories(stories2, sheetFilePath = paste0(tempdir(), "myExcelReport.xlsx"),
              reportFilePath = paste0(tempdir(), "myPrettyReport.html"),
              outputType = "html_document")

You can also ignore custom words when topic modelling, by using the function createIgnoreWords().

- Data
- Processing