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XOMO: understanding Development Options for Autonomy

XOMO: understanding Development Options for Autonomy. Tim@TimMenzies.net PDX, USA Julian Richardson julianr@email.arc.nasa.gov RIACS,USRA. Autonomy: key to future of space exploration Many risks Many unknowns: large “trade space” Use COCOMO-like models to sample that space

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XOMO: understanding Development Options for Autonomy

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  1. XOMO: understanding Development Options for Autonomy Tim@TimMenzies.net PDX, USA Julian Richardsonjulianr@email.arc.nasa.gov RIACS,USRA

  2. Autonomy: key to future of space exploration Many risks Many unknowns: large “trade space” Use COCOMO-like models to sample that space Monte carlo simulation of: COCOMO-II effort estimation model COQUALMO defect model Madachy schedule risk model Use data miners to find key decisions in that space Seek fewest decisions with most impact. Simulation results: Development effort……………………….. almost halved Mean risk of schedule over-run…………. halved Defect densities…………………………… -85% Variance on results………………………. greatly reduced Download: http://unbox.src/bash/xomo(doco: http://promise.unbox.or/DataXomo) XOMO = COCOMO model(s) + data mining Coming soon: GUI (JAVA) version Integrated with data miners

  3. AboutAutonomy

  4. Deep Impact intercepted comet Tempel 1, July 4th 2005. On-board autonomy made three last-minute trajectory corrections. T-minus 90 minutes. T-minus 35 minutes T-minus 12.5 minutes Note: ground control could not have made the last correction Asteroid was 7½ light minutes from earth; i.e., 15 minutes round trip; “You can’t joystick this thing.” -- Deep Impact mission controller Autonomy: example Good news: Autonomy reduces flight risks! • Risks mitigated: • failure of ground-commanded trajectory calculations (based on old data, may be slow) • failure due to communications outage Challenge: How to build intelligent systems in a cost-effective manner?

  5. Automatic: no humans Autonomous: no ground control The hard case: autonomous + automatic e.g. Deep Impact But some autonomous system aren’t automatic And some automatic systems aren’t autonomous What is autonomy?

  6. Extends capabilities: Automatic rendezvous and docking Good for in-orbit assembly Faster reaction to science event Data collection > downlink capacity Extended mission life Less reliance on ground control Saves time (few controllers) Avoids human errors (e.g XXXX) Historical data: 41% of software anomalies triggered by communications uplink/downlink [Lutz 2003] Why autonomy?

  7. # thousands of lines of codes _ANY(ksloc, 2, 10000) # scale factors: exponential effect on effort ANYi(prec, 1, 6) ANYi(flex, 1, 6) ... # effort multipliers: linear effect on effort ANYi(rely, 1, 5) ... # defect removal methods _ANYi(automated_analysis, 1, 6) _ANYi(peer_reviews, 1, 6) _ANYi(execution_testing_and_tools, 1, 6) … # calibration parameters _ANY(a, 2.25,3.25) _ANY(b, 0.9, 1.1) function Prec() return scaleFactor("prec", prec()) ... function Effort() {return A() * Ksloc() ˆ E() *Rely()* Data()* Cplx()*Ruse()* Docu()* Time()* Stor()* Pvol()* Acap()*Pcap()* Pcon()* Aexp()* Plex()* Ltex()* Tool()*Site()* Sced() } function E() { return B() + 0.01*(Prec() + Flex() + Resl() + Team() + Pmat())} XOMO= support code for COCOMO Monte Carlos

  8. Talented PhD-level programmers No prior autonomy experience High reliability Complex software Hope that product will be reusable Ksloc = 75 .. 125 Rely = 5 Prec = 1 Acap = 6 Aexp = 1 Cplx = 6 Ltex = 1 Reus = 6 Pmat, time, resl, … = ? Scenario

  9. COCOMOmodels

  10. COCOMO-II effort model

  11. Madachy Risk Model: how many dumb things are you doing today?

  12. COQUALMO: defect introduction

  13. COQUALMO: defect removal

  14. Casestudy

  15. Sample call Sample output Effort does not predict for defect density Highest schedule risk, one of the lowest defects

  16. Binary classification of N-utilities Effort Defects Schedule risk BORE: best or rest selection

  17. Badgreat The TAR3 “treatment learner” Housing, baseline (% of housing types) • Classes have utilities (best > rest) • “treatment”= policy • what to do • what to watch for • seek attribute ranges that are • often seen in “good” • rarely seen in “bad”. • Treatment= • constraint that changes baseline frequencies 6.7 <= rm < 9.8 and 12.6 <= ptratio < 15.9 0.6 <= NOX < 1.9 and 17.16 <= lstat < 39 A few variables are (often) enough

  18. Cycle: repeat till happy (or no more improvement) Monte carlo simulations COCOMO-II sample possible inputs scenario COQUALMO (learned restraints) MADACHY BORE: TAR3: classification learning

  19. 3257 => 1780 77 => 36 0.97 => 0.15 variances reduced Only 17/28 restrained

  20. To do…

  21. COQUALMO: add model-based methods for defect removal

  22. Conclusion

  23. SE has much to offer AI Autonomy: New software But can be analyzed, at least partially, by existing methods AI has much to offer SE Use data miners to explore COCOMO-model(s) Large scale what-if scenarios Easy to explore And so…

  24. Questions? Comments?

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