1 / 18

Lessons from Generative Techniques at Lucent Technologies

Lessons from Generative Techniques at Lucent Technologies. Lloyd H. Nakatani Avaya Labs Murray Hill, New Jersey lhn@avaya.com Mark Ardis Rose-Hulman Institute of Technology Terre Haute, Indiana Mark.A.Ardis@Rose-Hulman.edu. Three Lessons for DSLs. 1. Composability 2. Composability

charo
Download Presentation

Lessons from Generative Techniques at Lucent Technologies

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Lessons from Generative Techniques at Lucent Technologies Lloyd H. Nakatani Avaya Labs Murray Hill, New Jersey lhn@avaya.com Mark Ardis Rose-Hulman Institute of Technology Terre Haute, Indiana Mark.A.Ardis@Rose-Hulman.edu Generative Techniques Workshop

  2. Three Lessons for DSLs • 1. Composability • 2. Composability • 3. Composability Generative Techniques Workshop

  3. InfoWiz: Product Line for Jargons Fit programming language and API library WizTalk syntax Jargon J Semantics S Model M Product P InfoWiz interpreter Comes with InfoWiz (commonalities) Created by user (variabilities) Generative Techniques Workshop

  4. Example: Procedural Jargon • For modeling algorithms ;=height[;input[Enter your height: ]] ;if( ;.height > 72 ) ;output[Taller;cr] ;else( ;.height > 64 ) ;output[Medium;cr] ;else ;output[Short;cr] Generative Techniques Workshop

  5. Example: Data Jargon • For modeling hierarchical data ;person ;name ;first[Alan] ;last[Turing] ;address ;street[600 Mountain Ave.] ;town[Murray Hill] ;state[NJ] ;zip[07974] ;phone(work)[908/582-1234] Generative Techniques Workshop

  6. Example: Markup Jargon • For modeling text format ;p This is ;i[italic], this is ;b[bold], and this is ;s(16)[big]. Generative Techniques Workshop

  7. J1 J2 J3 Jargon Composition S1 S2 S3 InfoWiz interpreter P Composite model Generative Techniques Workshop

  8. Example: Composite Model • ;=person ;name ;first[Alan] ;last[Turing] ;if( [a-z] ~ ;.person(name|first) ) ;output Hello ;i[;.person(name|first)];cr ;else ;output[No name;cr] Generative Techniques Workshop

  9. J2 J1 M1 M2 Domain Engineering with Jargons Decompose Model Compose S1 S2 Subdomain D1 Domain model InfoWiz interpreter P Domain Subdomain D2 Generative Techniques Workshop

  10. Reconfiguration algorithms Example: Domain and Subdomains 5ESS Configuration Control Domain Hardware units and configuration Target data structures Generative Techniques Workshop

  11. Algorithm model S J Virtual Finite State Machine C code Reconfiguration Algorithms • ;realization(remove) • ;chk(cond,self,working) • ;chk(cond,spare_mate,ready) • ;invoke[elevate:spare_mate] • ;invoke[delevate:self] • ;invoke[remove:child] • ;chk(result,abortORfail) • ;set(ack)[fail,remove_child] • ;skipexps[endreal] • ;invoke[inhibit:self] • ;send[hardware:remove • ;chk(result,fail) • ;set(ack)[fail,remove] • ;invoke[allow:self] • ;skipexps[endreal] • ;set(cond)[remove] • ;set(ack)[success] • ;send[rmtce:remove] • ;skipend[endreal] • ;send[ack] • ;end 21 lines InfoWiz interpreter 2423 lines VFSM code generator 3505 lines Generative Techniques Workshop

  12. S C data structure code Configuration model Hardware Units and Configuration Visual modeling tool • . • . • . • . • . • . 642 lines InfoWiz interpreter 589 lines Generative Techniques Workshop

  13. RAD Visual Modeling Tool Generative Techniques Workshop

  14. Divide and Conquer Domains Generative Techniques Workshop

  15. Composition of Subdomain Models Generative Techniques Workshop

  16. Subdomain A1 Subdomain B1 Subdomain A2 Subdomain B2 Subdomain A3 Subdomain C1 Subdomain C2 Subdomain C3 Teamwork Today Domain A Domain B Domain C Generative Techniques Workshop

  17. Subdomain B1 Subdomain B2 Subdomain A1 Subdomain C1 Subdomain C2 Teamwork Tomorrow SuperdomainS Domain B Domain A Subdomain A2 Subdomain A3 Domain C Subdomain C3 Generative Techniques Workshop

  18. Conclusions for Generative DSLs • Multiple DSLs per domain, so they must be ... • easy to make and maintain • composable • to support divide-and-conquer • to scale up in complexity • We walked our own talk and made generative DSLs a product line Generative Techniques Workshop

More Related