1. 1 Feedback Control of The Software Development Process Department of Computer Sciences Purdue University CS 490 D Technology Review Aditya P. Mathur …in collaboration with João Cangussu Ray. A. DeCarlo Tuesday February 27, 2001

2. 2 Software Development Process: Definitions A Software Development Process (SDP) is a sequence of well defined activities used in the production of software. An SDP usually consists of several sub-processes that may or may not operate in a sequence. The Design Process, the Software Test Process, and the Configuration Management Process are examples of sub-processes of the SDP.

3. 3 Research Question Can we control the SDP in a manner similar to how physical systems and processes are controlled?

4. 4 Research Methodology 1.Understand how physical systems are controlled? 2. Understand how software systems relate to physical systems. Are there similarities? Are there differences? 3. Understand the theory and practice of the control of physical systems. 4.Can we borrow from this theory? If “yes,” then proceed further, else drink coffee or tea and think of another research direction. 5.Adapt control theory to the control of SDP and develop models and methods to control the SDP. 6.Study the behavior of the models and methods in real-life settings.

5. 5 Research Methodology 7.Improve the model and methods. 8.Repeat steps 6 and 7 until you are thoroughly bored or get rich.

6. 6 Current Focus Software Test Process (STP): System test phase Objective: Control the STP so that the quality of the tested software is as desired. Quantification of quality of software: Number of remaining errors Reliability

7. 7 Introduction Problem Scenario r - number of remaining errors t- time cp 1 cp 2 cp 3 cp 4 cp 5 cp 6 cp 7 cp 8 cp 9 where cp i = check point i r0r0 rfrf schedule set by the manager approximation observed deadline t0t0

8. 8 Introduction Our Approach Actual STP Controller r error (t)  ’  w’ f + + wf+wfwf+wf  +  wf+wfwf+wf STP State Model r observed (t) r expected (t) scsc r0r0 scsc r0r0 Initial Settings (w f,  ) r expected (T f )=r f Manager  wfwf

9. 9 Physical Systems: Laws of Motion Second Law: The acceleration of an object is directly proportional to the net force acting upon it and inversely proportional to its mass. F=ma Fundamental concepts: Force, Mass, Velocity Derived concepts: Acceleration, Inertia (mv)

10. 10 Physical Systems: Spring-Mass system Block Dashpot Spring Arrows show directions of forces on the block. Rigid surface Spring is assumed to be stretched to the left. External force

11. 11 Physical Systems: Spring-Mass System Block: Software under test. Mass: Software complexity Spring: Effective test effort; larger spring coefficient implies larger workforce. Dashpot: Opposing force; quality of the test process is inversely proportional to the coefficient of viscosity. Position: Number of remaining errors.

12. 12 Physical Systems: Control Controllability Is it possible to control X by adjusting Y? Observability Does the system have distinct states that can't be unambiguously identified by the controller ? Robustness Will control be regained satisfactorily after an unexpected disturbance?

13. 13 Assumption I The magnitude of the rate of decrease of the remaining errors is directly proportional to the net applied effort and inversely proportional to the complexity of the program under test.

14. 14 Assumption II The magnitude of the effective test effort is proportional to the product of the applied work force and number of remaining errors. for an appropriate .

15. 15 Assumption III The error reduction resistance is proportional to the error reduction velocity and inversely proportional to the overall quality of the test phase. for an appropriate .

16. 16 State Model

17. 17 Feedback

18. 18 Case Study II: Razorfish Project Description Project Goal: translate 4 million lines of Cobol code to SAP/R3 A tool has been developed to achieve the goal of this project. Goal of the test process: Test the generated code, not the tool.

19. 19 Case Study II: Razorfish Project Error Correspondence x x x x x x x x x Assumption 1 x x x x x x x x Assumption 2 A B A B Where: A represents errors in the transformer B represents errors in the generated code

20. 20 Validation: Razorfish Project Testing Process Transformer = modify S SAP R/3 run output 1 output 2 run S Cobol Select a Test Profile input continue testing yes no

21. 21 Case Study II: Razorfish Project Results Case study began at week 14. deadline at week 25. Error reduction of 85% desired by the deadline. Project completed in 37 weeks.

22. 22 Case Study II: Razorfish Project Alternatives from Feedback Eigenvalue needed To meet the deadline=-0.152. Cannot be obtained by changing process quality.

23. 23 Case Study II: Razorfish Project Alternatives from Feedback Eigenvalue needed To meet the deadline=-0.152

24. 24 Case Study II: Razorfish Project Alternatives from Feedback

25. 25 Case Study II: Razorfish Project Alternatives from Feedback

26. 26 Summary Analogy between physical and software systems presented. The notion of feedback control of software processes introduced. One case study described.

27. 27 Ongoing Research Sensitivity analysis of the model. Expansion of the model to include the entire SDP. Additional case studies.

28. 28 Physical Systems: Oven Controller T0T0 TeTe Heat loss to environment Oven temperature thermal resistance of insulation Temperature reading Electrical heater of heat capacity Ch C0C0 ChCh R0R0 R ho Controller ThTh W oven, heat capacity C0 thermal resistance To adjust power dissipated in the heating elements in the heater.