Predictors of Customer Perceived Quality
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  • 1. Predictors of Customer Perceived SOFTWARE QUALITY presented by Nicolas Bettenburg
  • 2. Software Quality matters! 01 / 28
  • 3. Imagine the Product Does NOT Satisfy a Customers Needs ... 02 / 28
  • 4. The company suffers •Maintenance costs •Additional expenses •Missed business opportunities 03 / 28
  • 5. Predict customerʼs experiences within the first 3 months! 04 / 28
  • 6. What are the factors? 05 / 28
  • 7. software platform hardware install location software updates system size missing information deployment issues usage patterns service contract 06 / 28
  • 8. Operating System System Size Predictors Software Ports Upgrades Deployment Time
  • 9. Use Predictors to form Models 13 / 28
  • 10. Software Failure Rare, high-impact problems resulting in a software change use logistic regression. Customer Interactions Frequent, low-impact problems, resulting in a customer call use linear regression. 14 / 28
  • 11. 15 / 28
  • 12. Logistic Regression xi β e P (Yi = 1|xi ) = 1+exi β 16 / 28
  • 13. Logistic Regression xi β e P (Yi = 1|xi ) = Binary 1+exi β Response Variable 16 / 28
  • 14. Logistic Regression xi β e P (Yi = 1|xi ) = Binary 1+exi β Response Predictor Variable Variable 16 / 28
  • 15. Logistic Regression xi β e P (Yi = 1|xi ) = Binary 1+exi β Response Predictor Logistic Model Variable Variable for one predictor Variable 16 / 28
  • 16. Logistic Regression Failure Report System Size 17 / 28
  • 17. Logistic Regression Failure Report System Size 18 / 28
  • 18. Logistic Regression 19 / 28
  • 19. Logistic Regression Beta Coefficient 19 / 28
  • 20. Logistic Regression Beta Coefficient Significancy Measures 19 / 28
  • 21. Software Failure Model 5.1.1 Modeling software failures sys Estimate Std. Err. z-value Pr(>|z|) sys (Intercept) −5.26 0.64 −8.18 3 ∗ 10−16 tan log(rtime) −0.30 0.03 −8.85 < 2 ∗ 10−16 as Upgr 1.38 0.15 9.01 < 2 ∗ 10−16 OX −1.18 0.17 −6.75 2 ∗ 10−11 ah WIN 1.01 0.34 2.98 0.003 ife log(nP ort) 0.36 0.08 4.37 10−5 bo nP ortN A 2.03 0.58 3.49 5 ∗ 10−4 LARGE 0.52 0.20 2.67 0.01 cau Svc 0.57 0.18 3.11 .002 fer US 0.52 0.27 1.92 0.05 the a s Table 1: Software failure regression results. tiv 20 / 28 ag
  • 22. Software Failure Model 5.1.1 Modeling software failures sys Estimate Std. Err. z-value Pr(>|z|) sys (Intercept) −5.26 0.64 −8.18 3 ∗ 10−16 tan log(rtime) −0.30 0.03 −8.85 < 2 ∗ 10−16 as Upgr 1.38 0.15 9.01 < 2 ∗ 10−16 OX −1.18 0.17 −6.75 2 ∗ 10−11 ah WIN 1.01 0.34 2.98 0.003 ife log(nP ort) 0.36 0.08 4.37 10−5 bo nP ortN A 2.03 0.58 3.49 5 ∗ 10−4 LARGE 0.52 0.20 2.67 0.01 cau Svc 0.57 0.18 3.11 .002 fer US 0.52 0.27 1.92 0.05 the a s Table 1: Software failure regression results. tiv 20 / 28 ag
  • 23. Software Failure Model 5.1.1 Modeling software failures sys Estimate Std. Err. z-value Pr(>|z|) sys (Intercept) −5.26 0.64 −8.18 3 ∗ 10−16 tan log(rtime) −0.30 0.03 −8.85 < 2 ∗ 10−16 as Upgr 1.38 0.15 9.01 < 2 ∗ 10−16 OX −1.18 0.17 −6.75 2 ∗ 10−11 ah WIN 1.01 0.34 2.98 0.003 ife log(nP ort) 0.36 0.08 4.37 10−5 bo nP ortN A 2.03 0.58 3.49 5 ∗ 10−4 LARGE 0.52 0.20 2.67 0.01 cau Svc 0.57 0.18 3.11 .002 fer US 0.52 0.27 1.92 0.05 the a s Table 1: Software failure regression results. tiv 20 / 28 ag
  • 24. Software Failure Model 5.1.1 Modeling software failures sys Estimate Std. Err. z-value e! Pr(>|z|) as sys (Intercept) −5.26 0.64 −8.18 3 ∗e −16 el 10 tan rr log(rtime) −0.30 0.03 −8.85ajo 2 ∗ 10−16 < as Upgr 1.38 a m < 2 ∗ 10−16 0.15 to 9.01 OX −1.18 0.17e −6.75 ra d 2 ∗ 10−11 ah WIN 1.01 u pg 0.34 2.98 0.003 ife log(nP ort) s t to 0.36 0.08 4.37 10−5 bo fir nP ortN Athe 2.03 0.58 3.49 5 ∗ 10−4 ʼt LARGEbe 0.52 0.20 2.67 0.01 cau d on Svc 0.57 0.18 3.11 .002 fer US 0.52 0.27 1.92 0.05 the a s Table 1: Software failure regression results. tiv 20 / 28 ag
  • 25. Linear Regression E(log(Yi )) = xi β 21 / 28
  • 26. Linear Regression E(log(Yi )) = xi β Number of Customer Calls 21 / 28
  • 27. Linear Regression E(log(Yi )) = xi β Number of Predictor Customer Calls Variable 21 / 28
  • 28. Linear Regression # Customer Calls System Size 22 / 28
  • 29. Linear Regression # Customer Calls System Size 23 / 28
  • 30. nician dispatches, and alarms within the first three months of in- stallation using linear regression. For example, in the case of calls, Customer Interactions the response variable Y calls is the number of calls within the first 2000 three months of installation transformed using the log function to make errors more normally distributed. The predictor variables, xi ˜ 1500 Model are described in detail in section 4. The model is: Calls 1000 E(log(Yicalls )) = xT β ˜i 500 5.2.1 Modeling customer calls 0 2003.6 Estimate Std. Err. t value Pr(>|t|) (Intercept) 0.35 0.04 7.90 3 ∗ 10−15 log(rtime) −0.08 0.00 −27.72 < 2 ∗ 10−16 Figu Upgr 0.73 0.02 46.78 < 2 ∗ 10−16 OX 0.13 0.01 9.62 < 2 ∗ 10−16 The two tren WIN 0.75 0.03 25.73 < 2 ∗ 10−16 flow of calls ca log(nP ort) 0.10 0.01 16.82 < 2 ∗ 10−16 itations we do nPortNA 0.39 0.04 10.80 < 2 ∗ 10−16 calls for new a LARGE 0.30 0.01 20.78 < 2 ∗ 10−16 Svc 0.28 0.01 23.06 < 2 ∗ 10−16 6. VALID US 0.41 0.01 28.99 < 2 ∗ 10−16 It is importa that results refl Table 3: Number of calls regression. R2 = .36. of the data coll We inspecte process and int 24 / 28 Most predictors are statistically significance due to large sample curacy. Throu
  • 31. nician dispatches, and alarms within the first three months of in- stallation using linear regression. For example, in the case of calls, Customer Interactions the response variable Y calls is the number of calls within the first 2000 three months of installation transformed using the log function to make errors more normally distributed. The predictor variables, xi ˜ 1500 Model are described in detail in section 4. The model is: Calls 1000 E(log(Yicalls )) = xT β ˜i 500 5.2.1 Modeling customer calls 0 2003.6 Estimate Std. Err. t value Pr(>|t|) (Intercept) 0.35 0.04 7.90 3 ∗ 10−15 log(rtime) −0.08 0.00 −27.72 < 2 ∗ 10−16 Figu Upgr 0.73 0.02 46.78 < 2 ∗ 10−16 OX 0.13 0.01 9.62 < 2 ∗ 10−16 The two tren WIN 0.75 0.03 25.73 < 2 ∗ 10−16 flow of calls ca log(nP ort) 0.10 0.01 16.82 < 2 ∗ 10−16 itations we do nPortNA 0.39 0.04 10.80 < 2 ∗ 10−16 calls for new a LARGE 0.30 0.01 20.78 < 2 ∗ 10−16 Svc 0.28 0.01 23.06 < 2 ∗ 10−16 6. VALID US 0.41 0.01 28.99 < 2 ∗ 10−16 It is importa that results refl Table 3: Number of calls regression. R2 = .36. of the data coll We inspecte process and int 24 / 28 Most predictors are statistically significance due to large sample curacy. Throu
  • 32. nician dispatches, and alarms within the first three months of in- stallation using linear regression. For example, in the case of calls, Customer Interactions the response variable Y calls is the number of calls within the first 2000 three months of installation transformed using the log function to make errors more normally distributed. The predictor variables, xi ˜ 1500 Modelare described in detail in section 4. The model is: Calls 1000 E(log(Yicalls )) = xT β ˜i 500 5.2.1 Modeling customer calls ly! 0 Estimate Std. Err. t value ra te 2003.6 uPr(>|t|) (Intercept) 0.35 0.04 7.90 acc3 ∗ 10−15 log(rtime) −0.08 ted 0.00 −27.72 < 2 ∗ 10−16 ic 46.78 < 2 ∗ 10−16 Figu Upgr OX 0.73 red 9.62 < 2 ∗ 10−16 0.02 0.13 e p0.01 The two tren WIN ca n b 0.03 25.73 < 2 ∗ 10−16 0.75 flow of calls ca log(nP ort)lls 0.10 0.01 16.82 < 2 ∗ 10−16 itations we do ca 10.80 < 2 ∗ 10−16 calls for new a er nPortNA 0.39 0.04 s tom Svc LARGE 0.30 0.01 20.78 < 2 ∗ 10−16 23.06 < 2 ∗ 10−16 6. VALID cu US 0.28 0.41 0.01 0.01 28.99 < 2 ∗ 10−16 It is importa that results refl Table 3: Number of calls regression. R2 = .36. of the data coll We inspecte process and int 24 / 28 Most predictors are statistically significance due to large sample curacy. Throu
  • 33. Points that I liked about the paper: • Clear and suitable models constructed • Emphasize on customerʼs perception of a software • Applicability to the real world 25 / 28
  • 34. Points that I disliked: • Evaluation of customer calls model lacks insights • Amount of effort needed to replicate the study • Terms are often misused and mixed 26 / 28
  • 35. Audris Mockus Empirical estimates of software availability of deployed systems. 2006 IEEE International Symposium on Empirical Software Engineering Audris Mockus, David Weiss Interval quality: relating customer perceived quality to process quality. 2008 International Conference on Software Engineering Nachiappan Nagappan, Brendan Murphy, Victor Basili The influence of organizational structure on software quality: an empirical case study. 2008 International Conference on Software Engineering 27 / 28
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  • 41. DISCUSSION 28 / 28