This document discusses models for predicting customer perceptions of software quality based on factors collected within the first three months of installation. Logistic regression is used to model rare, high-impact software failures based on variables like system size, software upgrades, operating system, etc. Linear regression is used to model frequent, low-impact customer interactions like calls based on similar predictor variables. The models found most predictors to be statistically significant due to the large sample size.
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.
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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
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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
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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
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