Newtons Law of Motion in Software Development Processes

Newton's Law of Motion in Software Development Processes? Aditya P. Mathur Department of Computer Sciences Purdue University, West Lafayette Visiting Profesor, BITS, Pilani Research collaborators: João Cangussu (CS, UT Dallas) Ray. A. De. Carlo (ECE, Purdue University) Presentation at: Indian Institute of Technology, Kanpur, India Monday November 10, 2003 Software Process Control 1

Research Question Can we control the Software Development Process in a manner similar to how physical systems and processes are controlled ? The fundamental control problem (Ref: Control System Design by G. C. Goodwin et al. , Prentice Hall, 2001) The central problem in control is to find a technically feasible way to act on a given process so that the process adheres, as closely as possible to some desired behavior. Furthermore, this approximate behavior should be achieved in the face of uncertainty of the process and in the presence of uncontrollable external disturbances acting on the process. Software Process Control 2

Research Methodology 1. Understand how physical systems are controlled? 2. Understand how software systems relate to physical systems. Are there similarities? Differences? 3. Understand theory and practice of the control of physical systems. Can we borrow from this theory? 4. Adapt control theory to the control of SDP and develop models and methods to control the SDP. 5. Study the behavior of the models and methods in real-life settings and, perhaps, improve the model and methods. 6. Repeat steps 6 and 7 until you are thoroughly bored or get rich. Software Process Control 3

Feedback Control Required Quality Specifications Effort + Program Additional effort Observed Quality - f(e) Software Process Control What is f ? 4

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. Software Process Control 5

Software Development Process: A Life Cycle Requirements Elicitation Requirements Analysis Design Code/Unit test Integrate/Test System test Not all feedback loops are shown. Software Process Control More test Deploy 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 Software Process Control 7

Problem Scenario r 0 r - number of remaining errors observed Approximation of how r is likely to change schedule set by the manager rf cp 1 cp 2 cp 3 cp 4 cp 5 cp 6 cp 7 cp 8 cp 9 t 0 t- time deadline Software Process Control cpi = check point i 8

Our Approach s c r 0 + Initial Settings (wf, ) wf+ wf + robserved(t) Actual STP ’ Controller Test Manager wf + wf: workforce : quality of the test process rerror(t) w’f STP State Model s c r 0 rexpected(t) Software Process Control 9

Physical and Software Systems: An Analogy External force To err is Human Dashpot Block Xcurrent Software X: Position Number of remaining errors Spring Xequilibrium Rigid surface Spring Force Effective Test Effort Mass of the block Software complexity Software Process Control Viscosity Quality of the test process 10

Physical Systems: Laws of Motion [1] First Law: Every object in a state of uniform motion tends to remain in that state of motion unless an external force is applied to it. Does not (seem to) apply to testing because the number of errors does not change when no external effort is applied to the application. Software Process Control 11

Physical Systems: Laws of Motion [2] Newton’s Second Law: The relationship between an object's mass m, its acceleration a, and the applied force F is F = ma. CDM First Postulate: The relationship between the complexity Sc of an application, its rate of reduction in the number of. . remaining errors, and the applied effort E is E=Sc r. Software Process Control 12

Physical Systems: Laws of Motion [3] Third Law: For every action force, there is an equal and opposite reaction force. When an effort is applied to test software, it leads to (mental) fatigue on the tester. Unable to quantify this relationship. Software Process Control 13

CDM First Postulate 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. This is analogous to Newton’s Second Law of motion. Software Process Control 14

CDM Second Postulate Analogy with the spring: The magnitude of the effective test effort is proportional to the product of the applied work force and the number of remaining errors. for an appropriate . Note: While keeping the effective test effort constant, a reduction in r requires an increase in workforce. Software Process Control 15

CDM Third Postulate Analogy with the dashpot: 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 . Note: For a given quality of the test phase, a larger error reduction velocity leads to larger resistance. Software Process Control 16

State Model Force (effort) balance equation: : Disturbance. x(t) = Ax(t) + B u(t) Software Process Control 17

Computing the feedback-Question Given: r(T): the number of remaining errors at time T r(T+ T): the desired number of remaining errors at time T+ T Question: What changes to the process parameters will achieve the desired r(T+ T) ? Software Process Control 18

Computing the feedback-Answer From basic matrix theory: The largest eigenvalue of a linear system dominates the rate of convergence. Hence we need to adjust the largest eigenvalue of the system so that the response converges to the desired value within the remaining weeks ( T). This can be achieved by maintaining: Obtain the desired eigenvalue. Software Process Control 19

Computing the feedback-Calculations ( max) Given the constraint: Compute the desired max We know that the eigenvalues of our model are the roots of its characteristic polynomial of the A matrix. Software Process Control 20

Computing the feedback-Calculations ( max) We use the above equation to calculate the space of changes to wf and such that the system maintains its desired eigenvalue. Software Process Control 21

Computing the feedback-Input to the Manager The space of changes in the workforce and the quality of the process is made available to the test manager in the form of suggestions for possible process changes. The test manager may decide to select a combination of these values for implementation or simply ignore them. So far, in each of the two commercial studies we carried out, the manager ignored the suggestions given using the model. Software Process Control 22

Case Study I: The Razorfish Project Goal: translate 4 million lines of Cobol code to SAP/R 3 A tool has been developed to achieve the goal of this project. Goal of the test process: (a) Test the generated code, not the tool. (b) Reduce the number of errors by about 85%. Software Process Control 23

Razorfish Project Test Process continue testing output 1 yes = output 2 no run SCobol modify Transformer run SSAP R/3 input Select a Test Profile Software Process Control 24

Razorfish Project: Results (intermediate) Expected behavior Project data Prediction using the model Prediction using feedback If the process parameters are not altered then the goal is reached in about 35 weeks. 85% reduction achieved. Software Process Control 25

Alternatives from Feedback: STP Quality Desired eigenvalue=-0. 152 Improving quality alone will not help in achieving the goal. Software Process Control 26

Alternatives from Feedback: Workforce Desired eigenvalue=-0. 152 Changing the workforce alone can produce the desired results. Software Process Control 27

Alternatives from Feedback: STP quality and workforce Set of valid choices for changing the quality and the workforce Software Process Control 28

Razorfish Project Results (final) The project was completed in 32 weeks. The model predicted 85% error reduction in 35 weeks. Software Process Control 29

Case Study 2: Company X P 1: Week 9 • Start of the study • 1 week = 5 working days • Estimated R 0 = 557 • 70% reduction – 10 weeks • 90% reduction 16 weeks Software Process Control 30

Company X P 1: Week 12 • No recalibration • Estimated R 0 = 557 • 70% reduction – 10 weeks (confirming previous prediction) • 90% reduction 14 weeks Note: for 90% error reduction, the change in 14 wks vs. 16 wks from the previous slide is due to an increase in the number of testers from 5 to 7 Software Process Control 31

Company X P 1: Week 14 • Recalibration • Estimated R 0 = 758 (agressive) • 70% reduction – 13. 6 weeks • 90% reduction 21. 6 weeks Software Process Control 32

Company X P 1: End of Phase 1 Software Process Control 33

Company X Observ. Estimated ed Week R 0 Defect # P 1: Summary 70% Defect Reduction 90% Defect Reduction Actual Estimated 9 281 557 -- 10 weeks -- 16 weeks 12 458 557 10 weeks -- 14 weeks 14 535 758 -- 16 590 758 18 635 764 13. 4 weeks 13. 6 weeks Software Process Control -21 21 21. 6 weeks 21. 4 weeks 21. 2 weeks 34

Summary Analogy between physical and software systems presented. The notion of feedback control of software processes introduced. Two case studies described. Parameter estimation techniques used for model calibration. Made use of system identification techniques. Software Process Control 35

Ongoing Research Sensitivity analysis (completed, IEEE TSE May 2003) r is more sensitive to changes in the model parameters during the early stages of the test process than during the later stages. An improvement in the quality of the STP is more effective than an increase in the workforce. Brook’s Law was also observed during the analysis. Expansion of the model to include the entire SDP: ongoing project in collaboration with Guidant Corporation (detailed model). Software Process Control 36

Physical Systems: Controllability Is it possible to control X (r) by adjusting Y (workforce and process quality)? Observability Does the system have distinct states that cannot be unambiguously identified by the controller ? Robustness Will control be regained satisfactorily after an unexpected disturbance? Software Process Control 37