1. T. E. Potok - University of Tennessee Software Engineering Dr. Thomas E. Potok Adjunct Professor UT Research Staff Member ORNL

2. 2 Software Engineering CS 594T. E. Potok - University of Tennessee Agenda  Homework Review  Petri Nets  Project Control  Example  Second Project

3. 3 Software Engineering CS 594T. E. Potok - University of Tennessee Petri Net Overview  Petri nets were invented by Carl Petri in 1966 to explore cause and effect relationships  Expanded to include deterministic time  Then stochastic time  Then logic

4. 4 Software Engineering CS 594T. E. Potok - University of Tennessee Definition  A Petri Nets (PN) comprises places, transitions, and arcs – Places are system states – Transitions describe events that may modify the system state – Arcs specify the relationship between places  Tokens reside in places, and are used to specify the state of a PN

5. 5 Software Engineering CS 594T. E. Potok - University of Tennessee Switch Example Place: ON Place: OFF Transition: SWITCH OFF Transition: SWITCH ON

6. 6 Software Engineering CS 594T. E. Potok - University of Tennessee Switch Example  Two places: Off and On  Two transitions: Switch Off and Switch On  Four arcs  The off condition is true  A transition can fire if an input token exists – One token is moved from the input place to the output place.

7. 7 Software Engineering CS 594T. E. Potok - University of Tennessee So what’s the big deal?  PERT networks, Activity Nets, Directed Graphs, can represent: – Nodes and arcs – Stochastic timings  But cannot represent states.

8. 8 Software Engineering CS 594T. E. Potok - University of Tennessee PN Properties  8-tuple mathematical model – M={P,T,I,O,H,PAR,PRED,MP} – P - the set of places – T - the set of transitions – I,O,H - Input, output, inhibition function – PAR - the set of parameters – PRED - Predicates restricting parameter range – PM - Parameter value  From this linear algebra can be used to analyze a network

9. 9 Software Engineering CS 594T. E. Potok - University of Tennessee Manufacturing Example K Enter In Enter Input Queue Busy Out Idle Cards Output Queue

10. 10 Software Engineering CS 594T. E. Potok - University of Tennessee Manufacturing Example K-1 Enter In Enter Input Queue Busy Out Idle Cards Output Queue

11. 11 Software Engineering CS 594T. E. Potok - University of Tennessee Manufacturing Example K-2 Enter In Enter Input Queue Busy Out Idle Cards Output Queue

12. 12 Software Engineering CS 594T. E. Potok - University of Tennessee Manufacturing Example K-2 Enter In Enter Input Queue Busy Out Idle Cards Output Queue

13. 13 Software Engineering CS 594T. E. Potok - University of Tennessee Manufacturing Example K-1 Enter In Enter Input Queue Busy Out Idle Cards Output Queue

14. 14 Software Engineering CS 594T. E. Potok - University of Tennessee Manufacturing Example K-1 Enter In Enter Input Queue Busy Out Idle Cards Output Queue

15. 15 Software Engineering CS 594T. E. Potok - University of Tennessee Manufacturing Example K Enter In Enter Input Queue Busy Out Idle Cards Output Queue

16. 16 Software Engineering CS 594T. E. Potok - University of Tennessee Petri Net Summary  Very rich modeling  Easily capable of modeling software project, requirements, architectures, and processes  Drawbacks – Complex rules – Analysis quite complex

17. 17 Software Engineering CS 594T. E. Potok - University of Tennessee Life-cycle and Project Tracking  A development life-cycle is controlled by the project schedule  Typically done in project meetings  A matter of style how strictly or loosely deadlines are enforced  Typically used as a means of reporting status of the project

18. 18 Software Engineering CS 594T. E. Potok - University of Tennessee Project Control Methods  Schedule – Ensure that the project is meeting the major and minor milestones – Ensure that the necessary inputs are on schedule, or contingency plans are in place – Calculate percent completion metrics

19. 19 Software Engineering CS 594T. E. Potok - University of Tennessee Project Control Methods  Cost – Track spending Vs. available funds – Relate to schedule completion – If you have spent 3/4 or the money, yet have only completed 1/3 or the project, you are in trouble  Information – Track the output coming from each phase of the project – Focus on demonstrations of the projects

20. 20 Software Engineering CS 594T. E. Potok - University of Tennessee Actual Example  Commercially available product  Second generation object-oriented port between platforms.  In this diagram, edges represent activities, and have durations associated with them, while nodes are milestones.  The final product has approximately 64 thousand lines of C++ code, the port required over 8 person- years of effort, and took 16 months to complete.  A Booch type object-oriented methodology was used.

21. 21 Software Engineering CS 594T. E. Potok - University of Tennessee PERT Diagram

22. 22 Software Engineering CS 594T. E. Potok - University of Tennessee Description of Nodes

23. 23 Software Engineering CS 594T. E. Potok - University of Tennessee Life-Cycle Model  There are five (unfolded) iteration cycles.  The first iteration ends with milestones 7 and 8,  The second with 13 and 14,  The third with 19 and 20,  The fourth one with 25 and 26, and  The final iteration with node 30.  The system testing activities run in parallel but are mainly aimed at the software emerging out of the final cycle.

24. 24 Software Engineering CS 594T. E. Potok - University of Tennessee Measure of Schedule Compliance

25. 25 Software Engineering CS 594T. E. Potok - University of Tennessee Completion Profile of First Project

26. 26 Software Engineering CS 594T. E. Potok - University of Tennessee Completion Profile of Second Project (Shown in PERT)

27. 27 Software Engineering CS 594T. E. Potok - University of Tennessee Completion Profile of Third Project

28. 28 Software Engineering CS 594T. E. Potok - University of Tennessee Observations  In all three projects the most frequent value for the task completion delay was zero. About 35%-60% of the tasks finished on the date originally planned.  It is uncommon to finish a task early. Only one project showed a task completing early.  In all three cases, a small group of intermediate or low priority tasks was significantly late, from 7 to 23 weeks after the original deadline.

29. 29 Software Engineering CS 594T. E. Potok - University of Tennessee Next Step  No obvious explanation as to why this result has occurred.  Actual project duration appears to be controlled by enforcement of the key milestones.  Reviewing these results in light of the business model described only plausible explanation for the contradiction observed.

30. 30 Software Engineering CS 594T. E. Potok - University of Tennessee Business Model  The business model provides strong discouragement to finishing key milestones late.  Yet does not provide strong incentives for early completion of intermediate milestone tasks.  Releases typically produce small amounts of code, while versions can be quite large.  The size of the programming team is relatively constant.

31. 31 Software Engineering CS 594T. E. Potok - University of Tennessee Theory  Business Model Drives Productivity – Key deadlines are strictly enforced, which leads to releases being comparatively overstaffed, with ample development time, and little incentive to complete early, – Versions are comparatively understaffed, with short development time, and strong incentive to finish on-time.

32. 32 Software Engineering CS 594T. E. Potok - University of Tennessee Productivity Drivers  Parkinson’s Law - Cyril Parkinson, 1957, the most remembered phase is that “work expands to fill the time ”  Gutierrez et al. have developed a stochastic model to represent the effects of Parkinson’s Law on a project – Unconstrained activity modeling (such as that seen in PERT models) may be inappropriate to represent real projects, – Completion time should be a function of the time scheduled for a project.  Scheduled time may be more a determinant of task completion time than estimated duration time!!

33. 33 Software Engineering CS 594T. E. Potok - University of Tennessee Productivity Drivers  Deadline Effect - Boehm defines the Deadline Effect as “the amount of energy and effort to an activity is strongly accelerated as one approaches the deadline for completing the activity”  Goal theory supports both the Parkinson’s Law, performance is lower if goals are easy, and the Deadline Effect, performance is higher if the deadline is challenging.  The Deadline Effect depends on enforcement of milestone (task/iteration) deadlines.

34. 34 Software Engineering CS 594T. E. Potok - University of Tennessee Model

35. 35 Software Engineering CS 594T. E. Potok - University of Tennessee Simulation Flow

36. 36 Software Engineering CS 594T. E. Potok - University of Tennessee Validation

37. 37 Software Engineering CS 594T. E. Potok - University of Tennessee Conclusion  Project schedules and how they are enforced appear to determine the duration of a task more than: – Task history – Task estimates  Derived distribution maps to reality, with understandable parameters.