Effort Used to Create Domain-Specific Modeling Languages
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The document discusses the effort and benefits of creating domain-specific modeling (DSM) languages, specifically in relation to automation systems for fish farms and IoT devices. It highlights significant productivity improvements, such as a five-fold increase compared to standard development methods and outlines the modest effort required for development, averaging around 10 man-days across various industry cases. The study emphasizes the role of tools in influencing development effort and suggests future research directions for further exploration in this area.
Effort Used to Create Domain-Specific Modeling Languages
- 1. 1 Juha-Pekka Tolvanen, Steven Kelly jpt@metacase.com Effort Used to Create Domain- Specific Modeling Languages
- 2. 2 Agenda Introduction and motivation Research method – Conduct case studies – Review industry reports Results Reflection and other work done Conclusions and discussion
- 3. 3 Real world example: Fish farm hardware: sensors, actuators, cabling functionality: lights, feeding, monitoring, etc. Persistent data UI Communication Deployment and operations: install, maintain
- 4. 4 DSM solution for developing fish farm automation systems
- 5. 5 Industry experiences on productivity increase with DSM "5-fold productivity increase when compared to standard development methods" "The quality is clearly better, simply because the modeling language rules out errors" "makes development significantly faster and easier than the old manual coding practices"
- 6. 6 Effort to create a DSM solution? DSM brings significant improvements to: – Development speed – Product quality – Participation of subject matter experts – Code quality – Ease of introducing new personnel, etc. But require an investment – How big this is? 0 10 20 30 40 50 60 70 80 90 100 0 1 2 3 4 5 6 Cost Repetition Current DSM {
- 7. 7 Research question: How big is the effort to create a Domain-Specific Modeling language?
- 8. 8 DSM solution consist of: Metamodel Constraints Notation Generators Tooling and tool-chain + Training material, guides, tutorials etc.
- 9. 9 Two research methods applied: 1. Case study – Create complete modeling solutions for two domains – To collect data on the effort in detail – Implementation by authors 2. Analyze industry cases – Reports on creating DSM solutions by and for industry • Not done by authors – To get "real-world" view, having at least one generator • If several generators only those providing data per generator included All cases done with MetaEdit+: No difference from tools
- 10. 10 On case studies Recorded data on the effort: – Timestamps from metamodel – Generator changes – Version history – Diary on the effort Case selection: – Can be shared – Can be repeated by others – Target different domains, have different purpose Resulting modeling solutions tested with reference examples
- 11. 11 Case1: DSM solution for IoT device
- 12. 12 Sample app2: Boat monitor
- 13. 13 Effort to create DSM for IoT* Solution: – 74 elements in metamodel – 39 constraints – 54 notational symbols – 9 icons – 1 model checker (21 LOC) – 1 code generator (556 LOC) Effort: – 3.8 man-days *Implementation available: www.metacase.com/download, ’IoT’ example project 0,0 1,0 2,0 Tooling Generators Notation Constraints Plain metamodel Days
- 14. 14 Case2: Modeling support for Archimate
- 15. 15 Effort to implement Archimate support* * Implementation of the ArchiMate is available from https://github.com/mccjpt/ArchiMate Solution: – 125 elements in metamodel, incl. 24 abstract ones – 10 000+ constraints, 10760 alone for legal relationships – 89 notational symbols – 77 icons – 3 model checkers (42 LOC) Effort: – 3.5 man-days 0 0,5 1 1,5 Tooling Generators Notation Constraints Plain metamodel Days
- 16. 16 Cost of DSL creation: industry cases Very few companies (and academic papers) report figures on the creation effort 0 5 10 15 Blood separators Military radio system High level synthesis, ESL Heating remote control Voice control Car infotainment Sport watches Touch screen controller Days
- 17. 17 Results Effort to create DSM solution is modest (in all cases) – 1-2 language engineers – 3-15 man-days, average 10 man-days in industry cases Data indicates that – Generator development takes 40-60% – The size of the resulting language had a surprisingly small effect on the effort required • We have detailed data from three industry cases (under NDA): effort increases less than linearly with size
- 18. 18 What influences to the effort? Size of the domain? Experience of language engineer(s)? Tooling? Tools seem to have the biggest influence to effort – Different languages, same tool: 1-5x differences • Among the 10 cases presented – Same language, different tools: 10-50x differences • El Kouhen et al. implementing BPMN in 5 different tools
- 19. 19 Conclusions We are not aware of many systematic studies comparing the development effort across several DSM solutions Effort to create Domain-Specific Modeling support is modest with MetaEdit+ – 10 man-days in average in industry cases Tools seem to have a big influence to the effort needed Future research – Repeat the study with other languages and tools – Address also maintenance, and not only DSM solution but also models made
- 20. 20 Thank you! Questions? Experiences? Counter arguments? Future research topics? jpt@metacase.com Juha-Pekka Tolvanen