Tells Your User Story Graphically
This program reads a text file (.txt, .csv, etc.) containing User Stories and outputs a Manchester Ontology file. As of yet, each line may only contain a single User Story.
Optionally, statistics about the User Story set can be output.
The main dependency for the program is its Natural Language Processor (NLP) spaCy. To run the program, you need:
- Python >= 3.4
- spaCy >= 0.93 (currently under development using v0.98)
- NumPy >= 1.10.1
- Pandas >= 0.17.1
- Jinja2 >= 2.8
Running the program can only be done from the command line. With the program main directory as current directory, run the program by executing:
python run.py <INPUT FILE> [<arguments>]
The most important arguments is INPUT FILE to specify the location of the text input file. The table below provides an overview of the currently implemented arguments.
| Argument | Required? | Description |
|---|---|---|
INPUT FILE |
Yes | Specify the file name of the User Story input file |
| Argument | Description |
|---|---|
-h, --help |
Show a help message and exit |
-n SYSTEM_NAME, --name SYSTEM_NAME |
Specify a name for your system |
-u, --print_us |
Print additional information per User Story |
-o, --print_ont |
Print the output ontology in the terminal |
--prolog |
Output prolog arguments to a .pl file. Combine with --link to reason about user stories |
--version |
Display the program's version number and exit |
| Argument | Description |
|---|---|
-s, --statistics |
Show statistics for the User Story set and output these in .csv files |
| Argument | Description | Type | Default |
|---|---|---|---|
-p, --per_role |
Create an additional conceptual model per role | N/A | |
-l, --link |
Link all ontology classes to their respective User Story for usage in the set analysis | N/A | |
-t THRESHOLD |
Set the threshold for the selected classes | FLOAT | 1.0 |
-b BASE_WEIGHT |
Set the base weight | INT | 1 |
-wfr WEIGHT_FUNC_ROLE |
Weight of functional role | FLOAT | 1.0 |
-wmo WEIGHT_MAIN_OBJ |
Weight of main object | FLOAT | 1.0 |
-wffm WEIGHT_FF_MEANS |
Weight of noun in free form means | FLOAT | 0.7 |
-wffe WEIGHT_FF_ENDS |
Weight of noun in free form ends | FLOAT | 0.5 |
-wcompound WEIGHT_COMPOUND |
Weight of nouns in compound compared to head | FLOAT | 0.66 |
python run.py example_stories.txt -n "TicketSystem" -u
The classes in the program are based on the following conceptual model:
The Reader starts by reading the input file line by line and generates a list of sentences. These sentences are then enriched using Natural Language Processing, adding Part-of-Speech tags, dependencies, named entity recognition, etc. Subsequently, the StoryMiner uses these enriched sentences to create UserStory objects. The User Story objects contain all the information that could be mined from the sentence. These are then used to attach weight to each term in the User Story, creating Term-by-US Matrix in the Matrix class. The Constructor then constructs patterns out of each user story, using the Term-by-US Matrix to attach a weight to each token. The Constructor forms a model for an ontology, which is then used by the Generator to generate a Manchester Ontology file (.omn) and optionally a Prolog file (.pl). Finally, these files are printed to an actual file by the Writer in the '/ontologies' and '/prolog' folders respectively.