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XOMO: understanding Development Options for Autonomy

This paper discusses the pivotal role of autonomy in future space exploration, highlighting risks and uncertainties involved. By leveraging COCOMO-like models and Monte Carlo simulations, we can significantly reduce development effort, schedule overruns, and defect densities. Notably, improvements in spacecraft autonomy, demonstrated by the Deep Impact mission, show how on-board systems can perform critical tasks without ground control. This work advocates for the integration of data mining techniques to inform decision-making and optimize efforts in software engineering for autonomous systems.

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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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