The design of large-scale complex systems requires their analysis from multiple perspectives, often through the use of requirements models. Diversely located experts with different backgrounds (e.g., safety, security, performance) create such models using differ- ent requirements modeling languages. One open chal- lenge is how to align these models such that they cover the same parts of the domain. We propose a technique based on natural language processing (NLP) that ana- lyzes several models included in a project and provides suggestions to modelers based on what is represented in the models that analyze other concerns. Unlike techniques based on meta-model alignment, ours is flexible and language agnostic. We report the results of a focus group session in which experts from the air traffic management domain discussed our approach.

1. TOWARDS ALIGNING MULTI-
CONCERN MODELS VIA NLP
MODRE’17
F. Başak Aydemir Fabiano Dalpiaz

2. COLLABORATIVE MODEL-DRIVEN DEVELOPMENT
• MULTIPLE EXPERTS/MODELERS
• DIVERSE LOCATIONS
• DIFFERENT TIME-ZONES
• CONCERN SPECIFIC JARGON
• THE SAME DOMAIN
Towards Aligning Multi-Concern Models via NLP 2

3. CASE STUDY: AIR TRAFFIC MANAGEMENT
CONCERN LANGUAGE LOCATION DOMAIN ONTOLOGY
Safety Fault Trees United Kingdom AIRM*
Organization Goal Models Netherlands AIRM
… … … …
Towards Aligning Multi-Concern Models via NLP 3
*Air Traffic Management Information Model (www.airm.aero)
Example set up of a European air traffic management project:
• PACAS is a tool-supported process that fosters the active collaboration among
heterogeneous stakeholders
• Unlike traditional, informal enterprise architectures PACAS relies on
gamification and automated reasoning to formally align the multiple views

4. SOLUTION ARCHITECTURE
• WEB-BASED
• ASYNCHRONOUS
• COLLABORATIVE
• NLP-BASED
• (MODELING) LANGUAGE-AGNOSTIC
• DOMAIN ONTOLOGY
Towards Aligning Multi-Concern Models via NLP 4

5. INTERACTION BETWEEN MODELERS
Towards Aligning Multi-Concern Models via NLP 5

6. COMMIT A MODEL
Towards Aligning Multi-Concern Models via NLP 6

7. ANALYZING COMMITS
1. ELEMENT LABELS {AIR TRAFFIC CONTROLLER SUPERVISED} {SECTORS SPECIFIED}
2. NOUNS FROM THE LABELS {AIR, TRAFFIC, CONTROLLER, SECTOR}
3. KEEP TRACK OF NOUN FREQUENCIES
Towards Aligning Multi-Concern Models via NLP 7

8. INTERACTION BETWEEN MODELERS
Towards Aligning Multi-Concern Models via NLP 8

9. SUGGESTING CONCEPTS
1. MISSING NOUN {SECTOR}
2. RELATED CONCEPTS FROM DOMAIN ONTOLOGY {MINIMIM SECTOR ALTITUDE, SECTOR
CONFIGURATION PLAN, CONTROL SECTOR, ….}
3. FILTER RESULTS {MINIMUM SECTOR ALTITUDE, SECTOR CONFIGURATION PLAN}
Towards Aligning Multi-Concern Models via NLP 9

10. INTERACTION BETWEEN MODELERS
Towards Aligning Multi-Concern Models via NLP 10

11. EVALUATION
• A FOCUS GROUP DISCUSSION WITH ATM EXPERTS
• POSITIVE FEEDBACK FOR THE NLP-BASED SUPPORT
• DIVERSE PREFERENCES (NUMBER OF SUGGESTIONS, HIGH-LEVEL VS DETAILED SUGGESTIONS,
TIMING OF THE SUGGESTIONS)
• HEURISTICS NEEDED
• FINE TUNING NEEDED
• THE DIRECTION OF THE FEEDBACK SHOULD BE CONSIDERED IN SOME SETTINGS (E.G., SECURITY
MODEL FEEDS THEM ALL)
Towards Aligning Multi-Concern Models via NLP 11

12. QUESTIONS TO THE AUDIENCE
• ANY INTERESTING CASES ABOUT SOFTWARE DEVELOPMENT WHERE THE APPROACH CAN BE
USED?
• WHAT RISKS MAY THE APPROACH INTRODUCE FOR THE MODELING PROCESS?
Towards Aligning Multi-Concern Models via NLP 12

13. CONTACT US
Towards Aligning Multi-Concern Models via NLP 13
F. Başak Aydemir Fabiano Dalpiaz
f.b.aydemir@uu.nl f.dalpiaz@uu.nl
@aydemirfb @FabianoDalpiaz
This work has received funding from the SESAR Joint Undertaking grant agreement No 699306 under European
Union’s Horizon 2020 research and innovation programme.
www.pacasproject.eu
@pacasproject

14. IMPORTANT DATES
• ABSTRACTS SEPTEMBER 25, 2017
• PAPERS OCTOBER 2, 2017
• CONFERENCE MARCH 19-22, 2018
www.refsq.org/2018