Figure 3. Moderating Effects of Software Volatility Magnitude by Comparing Knowledge Approaches between Patterns 2 and 3 Technology-based Approach Skill-based Approach Approach Vertical axis represents software quality (error rates). Lower scores represent higher quality. Blue: Technology-based approach Red: Experience-based approach Skill-based approach Solid line: Volatility Pattern 2 Doubled Line: Volatility Pattern 3 Table 1. Mapping of Software Volatility Patterns Based on Software Volatility Factors Software Volatility Factors Pattern 1 Pattern 2 Pattern 3 Pattern 4 (P1) (P2) (P3) (P4) Modification frequency High X X Low X X Modification predictability High X X X Low X Modification magnitude Large X X Small X X 2. Summary of Variables Variable Description Mean St. Dev. Lag? Transformed Dependent variable Number of defects in nightly ERROR 2.26 1.10 No Log runs during time period t Independent variables An application’s software volatility Volatility pattern, P2, P3, or P4. Pattern 1 is NA NA NA NA pattern the base pattern. Proportion of the application that TECH was created or maintained with 0.26 0.27 Yes Normalized CASE tools Average number of days spent EXP together by team members during 128.80 144.55 Yes Normalized time period t Average of assigned SKILL 3.49 1.12 Yes Normalized maintainers for time period t Control variables AGE Application age 122.55 59.22 Yes No APPFP Function points for the application 2,290.60 1,501.83 Yes Normalized LOC Lines of code for the application 356,757.4 33,7874.2 Yes Normalized Complexity measure (ZN2 per appl. COMPL 470.88 226.85 Yes Normalized module) Number of online transactions in TXNUM 642,837.4 1,001,172 No Normalized time period t Table 3. Volatility Patterns Model Results Variable Full Model
Figure 3. Moderating Effects of Software Volatility Magnitude by Comparing Knowledge Approaches between Patterns 2 and 3 Technology-based Approach Skill-based Approach Approach Vertical axis represents software quality (error rates). Lower scores represent higher quality. Blue: Technology-based approach Red: Experience-based approach Skill-based approach Solid line: Volatility Pattern 2 Doubled Line: Volatility Pattern 3 Table 1. Mapping of Software Volatility Patterns Based on Software Volatility Factors Software Volatility Factors Pattern 1 (P1) Pattern 2 (P2) Pattern 3 (P3) Pattern 4 (P4) Modification frequency High X X Low X X Modification predictability High X X X Low X Modification magnitude Large X X Small X X 2. Summary of Variables Variable Description Mean St. Dev. Lag? Transformed Dependent variable ERROR Number of defects in nightly runs during time period t 2.26 1.10 No Log Independent variables Volatility pattern An application’s software volatility pattern, P2, P3, or P4. Pattern 1 is the base pattern. NA NA NA NA TECH Proportion of the application that was created or maintained with CASE tools 0.26 0.27 Yes Normalized EXP Average number of days spent together by team members during time period t 128.80 144.55 Yes Normalized SKILL Average of assigned maintainers for time period t 3.49 1.12 Yes Normalized Control variables AGE Application age 122.55 59.22 Yes No APPFP Function points for the application 2,290.60 1,501.83 Yes Normalized LOC Lines of code for the application 356,757.4 33,7874.2 Yes Normalized COMPL Complexity measure (ZN2 per appl. module) 470.88 226.85 Yes Normalized TXNUM Number of online transactions in time period t 642,837.4 1,001,172 No Normalized Table 3. Volatility Patterns Model Results Variable Full Model