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Predictors of Customer Perceived Quality
Predictors of Customer Perceived Quality
Predictors of Customer Perceived Quality
Predictors of Customer Perceived Quality
Predictors of Customer Perceived Quality
Predictors of Customer Perceived Quality
Predictors of Customer Perceived Quality
Predictors of Customer Perceived Quality
Predictors of Customer Perceived Quality
Predictors of Customer Perceived Quality
Predictors of Customer Perceived Quality
Predictors of Customer Perceived Quality
Predictors of Customer Perceived Quality
Predictors of Customer Perceived Quality
Predictors of Customer Perceived Quality
Predictors of Customer Perceived Quality
Predictors of Customer Perceived Quality
Predictors of Customer Perceived Quality
Predictors of Customer Perceived Quality
Predictors of Customer Perceived Quality
Predictors of Customer Perceived Quality
Predictors of Customer Perceived Quality
Predictors of Customer Perceived Quality
Predictors of Customer Perceived Quality
Predictors of Customer Perceived Quality
Predictors of Customer Perceived Quality
Predictors of Customer Perceived Quality
Predictors of Customer Perceived Quality
Predictors of Customer Perceived Quality
Predictors of Customer Perceived Quality
Predictors of Customer Perceived Quality
Predictors of Customer Perceived Quality
Predictors of Customer Perceived Quality
Predictors of Customer Perceived Quality
Predictors of Customer Perceived Quality
Predictors of Customer Perceived Quality
Predictors of Customer Perceived Quality
Predictors of Customer Perceived Quality
Predictors of Customer Perceived Quality
Predictors of Customer Perceived Quality
Predictors of Customer Perceived Quality
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Predictors of Customer Perceived Quality

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Presentation given in MSR course.

Presentation given in MSR course.

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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
  • 36. 28 / 28
  • 37. 28 / 28
  • 38. 28 / 28
  • 39. 28 / 28
  • 40. 28 / 28
  • 41. DISCUSSION 28 / 28

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