Architecting an Evolvable System by Iterative Object Process Modeling

1. Architecting an Evolvable System by Iterative Object Process Modeling Presented by Ashirul Mubin The University of Alabama Graduate School Contributors: Daniel Ray, Math & Computer Science, University of Virginia Rezwanur Rahman, Aerospace Engineering, The University of Alabama

2. Problem Scope • Difficult to foresee future emerging requirements at the time of large system development • Unable to properly meet the newly emerged requirements • makes the system to gradually lose its value • The users become less satisfied & customers do not get proper services • Eventually, the system becomes outdated • Very costly to replace the entire system THE UNIVERSITY OF ALABAMA, TUSCALOOSA, AL 35487

3. Objectives • Enable newly developed large system to sense current state of evolution • Identify and track the emerging new requirement changes in specific areas of the system workflow • Deduce the inception time of the next evolutionary phase from system feedback • Provide as much information/data as necessary to apply the changes to the system THE UNIVERSITY OF ALABAMA, TUSCALOOSA, AL 35487

4. Redesign of Systems: Comparison Evolving smaller system by replacements Evolution of Camry Over last 20 years Evolution of Sony TV Over last 20 years Evolution of large system by iterative changes in system requirements & design Evolve by iteration Evolve by replacement Evolve by replacement Evolve by replacement THE UNIVERSITY OF ALABAMA, TUSCALOOSA, AL 35487

5. User Satisfaction Index (USI) • To quantify the system value with level of satisfaction of the users (and affiliates). • It may also quantify the service-quality to its customers • Psychological factors are also involved • A system architect has the freedom to define a system specific USI • that will most closely quantify its system value • that will also give correct index of users’ overall experiences • USI indicates the value for an instant of time (time stamped) THE UNIVERSITY OF ALABAMA, TUSCALOOSA, AL 35487

6. Examples of Quantifying Factors: • Development activity log • Tracking of update/change history • Request Inter-arrival time • Service (wait) time • System usage activity log • Data flow & control flow patterns • Component or Feature ranking of • The existing features • The newly requested features • Collecting user experiences & suggestions • On-page feedback • Periodic surveys • Changes in system state • meta-data & meta-model THE UNIVERSITY OF ALABAMA, TUSCALOOSA, AL 35487

7. Inception of Evolutionary Factors No action is taken to revive the system value; users continue with lower USI New System Replacement at high cost, with higher USI System USI USIhigh2 USIhigh1 USIavg USIth Tbegin1 Te1 Td1 Te2 Td2 TeN TdN Tbegin2 Time Behavior of a Traditional System THE UNIVERSITY OF ALABAMA, TUSCALOOSA, AL 35487

8. Inception of Evolutionary Factors After the iteration, the system USI is restored System USI USIhigh USIavg USIth Time Tbegin Te1 Td1 Te2 Td2 TeN TdN Behavior of an Evolvable System THE UNIVERSITY OF ALABAMA, TUSCALOOSA, AL 35487

9. USI Metrics for Analyzing System Behavior … the general purpose is to guide the “Analyzer” throughout an evolvable cycle. THE UNIVERSITY OF ALABAMA, TUSCALOOSA, AL 35487

10. Evolutionary Factors … the purpose is to provide Decision Support Data (DSD) to the analyzer – but it will vary widely from system-to-system. THE UNIVERSITY OF ALABAMA, TUSCALOOSA, AL 35487

11. (1) Capture system behavior System Meta-Data feedback Probing Station probing points adjust Evolvable System Evolution Policy Analyzer system boundary (3) Apply new changes (2) Update system state System Meta-Model adjust Iterative Architecture The wrapper system coordinates three basic stages that iterate through an evolutionary cycle at the inception of evolving factors. THE UNIVERSITY OF ALABAMA, TUSCALOOSA, AL 35487

12. Stage 1: Capturing System Behavior • Identify system/environmental parameters • place probing points in system workflow to collect behavioral data into probing station: • Workflow activity pattern • Change request history • Request inter-arrival time • Feature ranking • Manual feedback/survey • Establish a system measurement profile (behavioral data) from the deduced system metrics to aid computing inception of evolutionary phase, as well as any needed changes in the system THE UNIVERSITY OF ALABAMA, TUSCALOOSA, AL 35487

13. Stage 2: Updating System State • Collected behavioral data is fed into “Evolution Policy Analyzer” that updates system states (system meta-data and system meta-model) • The purpose of the analyzer is to accurately predict the inception of evolutionary phases by applying appropriate statistical models: • Based on non-parametric Kernel Density Estimation to predict future probable inter-arrival times • Hypothesis testing for more detail & rigorous testing • Comparative Histograms to show close match of the prediction with that of the actual data • Running Models from continuously changing data at each iteration • At each iteration, look for any Transient Indicators •  whether steady state is reached after sometime THE UNIVERSITY OF ALABAMA, TUSCALOOSA, AL 35487

14. Stage 2 (cont.): Statistical Models for the Analyzer The histograms from given data set and estimated from “Kernel Density Estimation” technique are highly correlated. This may help to predict the efficiency of the system based on Monte Carlo Simulation. (a) Histogram from given data set (b) Histogram from estimated dataset October 14 THE UNIVERSITY OF ALABAMA, TUSCALOOSA, AL 35487 14

15. Stage 3: Applying New Changes • When the “Evolution Policy Analyzer” signals the inception of a new evolutionary phase, the wrapper system (probing station, meta-data & meta-model) provides necessary specifications for updating the target system (by service orientation) • By selecting appropriate tools & methodologies (such as SaaS , integrated architectural approach [4] or reflective arch [5]. • Short, gradual & incremental updates • Adjust probing points in the revised workflow • Keep probes live, attached to appropriate places in the workflow • Continue collecting data for the upcoming cycles • Progressively maintain an evolvable system for its extended life cycle THE UNIVERSITY OF ALABAMA, TUSCALOOSA, AL 35487

16. Modeling the system using OPCAT Developed by MIT research affiliates, OPCAT is a system modeling tool that uses Object Process Methodology THE UNIVERSITY OF ALABAMA, TUSCALOOSA, AL 35487

17. General Observation from Empirical Data • Calculation of USI by quantifying factors: • System development activity log • Update/change tracking history (inter-arrival times) • System state at inception time (previous USI) • Promptness of addressing the requested changes • Service time (wait time) • User experiences • by Surveys & On-page feedbacks • Key indicators: • Request inter-arrival times • Frequency of update requests • Collective USI THE UNIVERSITY OF ALABAMA, TUSCALOOSA, AL 35487

18. Applying Evolutionary Factors THE UNIVERSITY OF ALABAMA, TUSCALOOSA, AL 35487

19. Conclusion • Significant benefits can be obtained from a large evolvable system: • Clear insight of the system • Increased control over maintaining & upgrading the system • Much lower maintenance cost • Progressively maintain satisfactory system outcomes • In these days of technological advancements, the system architects and developers have much wider freedom for developing evolvable systems • Future Considerations: • Automated ways to update system objects, processes and codes needs to be investigated further (tools, like OPCAT) • An in-depth study of environmental and psychological impact on USI changes for each iteration will help refine related metrics & identify effective evolutionary factors • Applying further statistical analyses on the accumulated historical data will direct more accurate prediction of evolutionary cycles THE UNIVERSITY OF ALABAMA, TUSCALOOSA, AL 35487

20. Questions? THE UNIVERSITY OF ALABAMA, TUSCALOOSA, AL 35487