1. DECISION SUPPORT FOR RE-PLANNING OF SOFTWARE PRODUCT RELEASES S. M. Didar-Al-Alam Dept. of Computer Science University of Calgary, Calgary, AB, Canada Dietmar Pfahl Institute of Computer Science University of Tartu, Tartu, Estonia Guenther Ruhe Dept. of Computer Science University of Calgary, Calgary, AB, Canada

2. Outline Context Research Questions Solution Approach Release plan Generation Monitoring Release plan Implementation Re-planning Case Study : Dyna-H2W Innovations Threats to Validity Future Works 2

3. Context Release plan fails, if implementation process is uncontrolled Key solution approaches are - 1) Process Control [1] 2) Release Re-planning [2] Multi-factor process control should monitor release plan implementation Multiple factors should help Re-planning decisions 3

4. Research Questions RQ1: How to provide decision support on detecting the appropriate time to initiate re-planning process? RQ2: How to provide decision support on performing dynamic re-planning in consideration of multiple factors? RQ3: How to integrate predictive models into the decision support provided under RQ1 and RQ2? 4

5. SOLUTION APPROACH 5

6. Solution Approach Code Defect Data Repositories Change Big Data (Legacy Data) Analysis (Decision Centric Data Mining) Mining legacy data to find i) Monitoring factors & ii) Monitoring rules Analysis of Big Data 50 200 250 300 350 400 450 500 550 0 600 Factor Δ 2 100 150 50 200 250 300 350 400 450 500 550 0600 Factor Δ 3 100 150 50 200 250 300 350 400 450 500 550 0600 Factor Δ 1 100 150 Rational Unified Process Statistical Process Control INCEPTION ELABORATION CONSTRUCTION TRANSITION Initial Elab-1 Elab-2 Const-1 Const-2 Const-3 Tran-1Tran-2 PHASES ITERATIONS Statistical Process Control [3] Multiple Re-planning Criteria Δ 1 (t*) = g(t*) – h(t*) > 0 ( = β 1 ) Δ 2( t*) = abs(effort pred (t, r i ) - effort act (t, r i )) > β 2 Δ 3 (t*) = abs( defects pred (t) - defects act (t) ) > β 3 Optimized Operational Plan Continuously Updated Project Data Optimized Feature Selection

7. Release Plan Generation Strategic Planning Optimized feature selection to achieve maximum release value Conducted using EVOLVE II [2,4] Operational Planning Assign developers to implement selected features Scheduling tasks to achieve minimum release time Conducted using RASORP [5] 7

8. Monitoring Release plan Implementation Release plan implementation process is continuously monitored Inspired by Statistical Process Control (SPC) [3] Monitoring attributes are chosen based on process factors For each attribute maximum variation level is found Multiple Re-planning criteria are created 8

9. Monitoring Process: Hypothetical Example Software Quality (Δ 3 ) Effort Usage (Δ 2 ) Feature Change Request (Δ 1 ) 9 Process Factors Continuous Monitoring Re-planning Criteria Δ 1 (t*) = g(t*) – h(t*) > 0 ( = β 1 ) Δ 2( t*) = abs(effort pred (t,r i ) - effort act (t, r i )) > β 2 Δ 3 (t*) = abs( defects pred (t) - defects act (t) ) > β 3

10. Re-planning Re-planning criteria are monitored If fulfilled a warning is triggered Re-planning decision is human expert driven Project data is updated Strategic re-planning is conducted using EVOLVE II Operational planning is conducted using RASORP 10

11. Case Study : Dyna – H2W  Dyna-H2W: Instantiation of proposed approach  Multi-factor process control and multi-factor re-planning are evaluated  Comparative study is conducted among three strategies - Static re-planning with single factor monitoring - Dynamic re-planning with single factor monitoring [6] - Dyna-H2W: Dynamic re-planning with multiple factor monitoring 11

12. Case Study Setup Real world text editor software development with- Fifty (50) candidate features. Two future releases (planning horizon) Two planning criteria- Urgency & Value Ten developers Feature implementation is completed through five tasks- Requirement Elicitation Design Application Development Third party Development Quality Assurance 12

13. Case Study Results Number of defects detected and fixed Baseline (with no re-planning) Dynamic re-planning with single factor Static re-planning with single factor Dyna H2W 412407399422 Comparison of different strategies in terms of quality Comparison of different strategies in terms of release value (satisfaction value) 13

14. Innovations  Utilizing multiple process factors in software development process control  Supporting detection of out-of-control situation  Dynamic re-planning based on multiple process factors  Achieved higher release value and better quality compared to other strategies 14

15. Threats to Validity Only quality assurance (QA) effort is considered responsible for defect detection and debugging Effort, value and strategic plan data are collected from real world but QA data are synthetically generated Accuracy of Dyna-H2W depends on prediction and experts knowledge Re-planning criteria are considered to have constant tolerance level 15

16. Future Works Comprehensive evaluation of Dyna-H2W in industry settings Utilizing different combination of process factors Altering re-planning criteria with varying threshold values Automating re-planning criteria generation with machine learning techniques 16

17. References [1] T.A. Hughes,”Measurement and control basics”, ISA-The Instrumentation, Systems, and Automation Society, 2002. [2] G. Ruhe, “Product Release Planning: Methods, Tools and Applications,” CRC Press 2010. [3] W. Florac and A. Carleton, “Measuring the Software Process,” Addison Wesley, 1999 [4] https://www.expertdecisions.com/ [5] A. Ngo-The, G. Ruhe, “Optimized Resource Allocation for Software Release Planning,” IEEE Transactions on Software Engineering, Volume 35, 2009, pp. 109-123. [6] A. Al-Emran, D. Pfahl, and G. Ruhe, “DynaReP: A discrete event simulation model for re-planning of software releases,” Proc. ICSP 2007, LNCS 4470, pp. 246–258. 17

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