Keynote data analyticsforsw_productinnovation_pdf

12/12/2014
1
Data Analytics for Software
Product Innovation
Guenther Ruhe
University of Calgary
©Guenther Ruhe
AGENDA
How it began: The Esprit Project PROFES
Product Innovation
Analytical Open Innovation
AOI for Innovative Products
The Road Ahead
PROFES 2014, Helsinki, Finland 2

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2
Participating Companies
©Guenther Ruhe
Project Team
Markku Oivo Pasi Kuvaya Janne Jarvinenen
Dietmar Pfahl Rini van Solingen Frank van Latum
Guenther Ruhe
©Guenther Ruhe
Andreas Birk

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Elements of PROFES
• Combining and enhancing the strengths of goal‐oriented
measurement, process assessment, product
and process modelling and experience factory
©Guenther Ruhe
ISO
15504 GQM
ISO GQM
15504
PROFES
ISO9126 QIP/EF
Focus on PPDs
• Focus on investigating the relationship between
product and process quality
PRODUCT PPD PROCESS
©Guenther Ruhe

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Different Facets of PPDs
Context
Characteristics
©Guenther Ruhe
Technologies
Used
Design Inspections
Product
Quality
Software
Process
Reliability Software Design
Low or Average
Overall Time Pressure
©Guenther Ruhe
AGENDA
Product Innovation
Analytics Case Studies
The Road Ahead

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Innovation –What is it … after all?
• Innovativeness is the measure of “newness”
• New to the:
©Guenther Ruhe
World
 Market
 Industry
 Adopting unit
 Consumer
Crossing the Chasm
PROFES 2014, Helsinki, Finland ©Guenther Ruhe 10

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Being New … Being First
©Guenther Ruhe
• New technology
• New product line
• New product features
• New product design
• New process
• New service
• New customers
• New uses
• New quality
• New type of benefit
A Powerful Force for Everyday Fitness
©Guenther Ruhe

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©Guenther Ruhe
AGENDA
Product Innovation
The Road Ahead
Responding to change for gaining competitive
advantage in the era of smart decisions will be based
not on "gut instinct," but on predictive analytics.
Ginni Rometty, Chairman, President and CEO, IBM, 2013
©Guenther Ruhe

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Innovative product development:
New ideas from leveraging external
knowledge and resources, applying
innovative processes and technologies
Open Innovation
• An (open) approach for integration of internal and
external ideas and paths to market that merges
distributed knowledge and ideas into production
processes.
Chesbrough, H., “Open Innovation: The New
©Guenther Ruhe
Imperative for Creating and Profiting
from Technology”, Harvard Business
Press, 2003.

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Open Innovation for New Products
PROFES 2014, Helsinki, Finland ©Guenther Ruhe
17
Analytic Open Innovation
• Open innovation utilizing the power of analytics
(processes, tools, knowledge, techniques, decisions)
©Guenther Ruhe

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©Guenther Ruhe
AGENDA
Product Innovation
The Road Ahead
New Products – Data & Information Needs
Profes 2014, Helsinki ‐ © Guenther
Ruhe ©Guenther Ruhe
20
Information needs
Type of release planning problem
Features
Feature dependencies
Feature value
Stakeholder
Stakeholder opinion and
priorities
Release readiness
Market trends
Resource consumptions
and constraints
What to release × × × × × × ×
Theme based × × × × × × ×
When to release × × × × × × ×
Consideration of quality requirements × × × × × × ×
Operational release planning × × ×
Consideration of technical debt × × × ×
Multiple products × × × × × × ×

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Cluster
analysis
Crowdsouring
Rough set
analysis
©Guenther Ruhe
Simulation
Text mining
Pattern
recognition
Morphological
analysis
Optimization
Analytical
Kano model

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Value Synergies
©Guenther Ruhe
In consideration
of synergies
Without synergy
considerations
Considering
constraints is causing
structural differences
in plans and increase
value (stakeholders
feature points)
Time‐dependent Value
Re‐planning of not implemented features before starting Q2 with updated data from
different customer groups

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Cluster
analysis
Crowdsouring
Rough set
analysis
©Guenther Ruhe
Simulation
Analytical
Kano model
Text mining
Pattern
recognition
Morphological
analysis
Optimization

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1
2
Clusters
Having two cluster of
customers
Customization
towards groups
of customers
Having six clusters of
customers
©Guenther Ruhe
Comparison of planning
without clustering and by
considering 6 clusters
created from the crowd.

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Cluster
analysis
Crowdsouring
Rough set
analysis
©Guenther Ruhe
Simulation
Text mining
Pattern
recognition
Morphological
analysis
Optimization
Analytical
Kano model

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ServiceID Service
S1 Live channel coverage
s2 Multiscreen
S3 Switch display
S4 Aspect ratio change
S5 EPG
S6 Remote control
S7 Support without touch screen
S8 Video on demand
S9 Youtube integration
S10 Source signal selection
S11 Variety of product usage model support
S12 Advertisement
S13 Archive
S14 Search
S15 Intuitive navigation
S16 Detect location
S17 Bookmarking
S18 Categorization
S19 Triple play
S20 Social network accessibility
S21 Playlist
S22 History
S23 Multicast
S24 Different views supportability
S25 Replay
S26 Instant streaming
S27 DRM
S28 Memory management
S29 Player integration
S30 Variety of quality support
S31 Parental control
S32 Channel preview
S33 Picture‐in‐picture
S34 Peer‐to‐peer wireless screen casting support
S35 Video recommendation
S36 Share content
Cluster
analysis
Crowdsouring
Rough set
analysis
©Guenther Ruhe
Simulation
Text mining
Pattern
recognition
Morphological
analysis
Optimization
Analytical
Kano model

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Customer satisfied
Customer dissatisfied
Requirement
fulfilled
©Guenther Ruhe 33
Requirement
not fulfilled
One‐Dimensional
requirement
Attractive
requirements
Must‐be
requirements
(Berger et al., 1993)
Articulated specified
Not expressed measurable technical
Customer tailored
Cause delight
Implied
Self‐evident
Not expressed
Obvious
OTT Services ‐ Kano Questionnaire
How would you feel if “Support of
Video‐on‐Demand (VOD)” was provided
with this mobile app?
How would you feel if “Support of Video‐on‐
Demand (VOD)” was NOT provided with
this mobile app?
©Guenther Ruhe
______ I like it that way
______ It must be that way
______ I'm indifferent
______ I can live with it that way
______ I dislike it that way
______ I like it that way
______ It must be that way
______ I'm indifferent
______ I can live with it that way
______ I dislike it that way
Functional form
of the question
Dysfunctional form
of the question
https://qtrial2014.az1.qualtrics.com/SE/?SID=SV_eeMrc9WjpFX6ZKd

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Kano Evaluation Table
©Guenther Ruhe
Customer
Requirements
Dysfunctional questions
Like Must‐be Neutral Live
with Dislike
Functional
questions
Like Q A A A O
Must‐be R I I I M
Neutral R I I I M
Live with R I I I M
Dislike R R R R Q
Must‐be (M) One‐Dimensional (O) Attractive (A) Indifferent (I)
Reverse (R) Questionable (Q)
Cluster
analysis
Crowdsouring
Rough set
analysis
©Guenther Ruhe
Simulation
Text mining
Pattern
recognition
Morphological
analysis
Optimization
Analytical
Kano model

12/12/2014
19
Cluster
analysis
Crowdsouring
Rough set
analysis
©Guenther Ruhe
Simulation
Text mining
Pattern
recognition
Morphological
analysis
Optimization
Analytical
Kano model

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New Product (Super App) Design
©Guenther Ruhe
M O A R I
2 2 1 2 0
{S4,S10,S11,S14,S20,S26,S27}
Value Effort
1570 261
M O A R I
1 1 3 1 3
{S1,S2,S3,S4,S5,S6,S7,S14,S19,S21,
S22,S23,S25,S28,S32}
Value Effort
4506 261
Release Readiness Optimization
0.8
0.75
0.7
0.65
0.6
Percentage of issues fixed ()
Average method complexity
Percentage of duplicated code
Number of code smells per class
Test coverage: Covered LOC/ LOC
Defects/KLOC
Percentage of defect fixed
Defect find rate for last two weeks
Code Churn per contributor per day
Percentage of successful builds/integration
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©Guenther Ruhe
0.55
142 149 156 163 170
Calculated readiness
Projected readiness on
release date
Readiness
Development time (days)
0.00 0.20 0.40 0.60 0.80 1.00
Number of feature implemented
Level of attribute satisfaction

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Release Readiness Optimization (2/2)
12/12/2014 41
©Guenther Ruhe
Cluster
analysis
Crowdsouring
Rough set
analysis
©Guenther Ruhe
Simulation
Text mining
Pattern
recognition
Morphological
analysis
Optimization
Analytical
Kano model

12/12/2014
22
2779
(X*Y)*X*
358
379
(YXm)n
1809
738
XmYnXl
21
Ym(YX)nXl XnYm
77 154
(XYm)n
154
(XmY)n
38
21
56
(YX)n X(XY)n
(XYX)n
41
59
XYnXm
YnXm
132
814
971
839
YXn YnX
(YX)nXm
49
(XXY)nXm
(XY)nXm
4
14
25
284
3
15
(YnX)m
9
76
©Guenther Ruhe
AGENDA
Product Innovation
The Road Ahead

12/12/2014
23
Cluster
analysis
Crowdsouring
Rough set
analysis
©Guenther Ruhe
Simulation
Text mining
Pattern
recognition
Morphological
analysis
Optimization
Analytical
Kano model

12/12/2014
24
Innovative products through innovative processes
INNOVATIVE PPD
PRODUCTS PROCESSES
Innovative Products through AOI
INNOVATIVE
PRODUCTS
PROCESSES
• Acquiring innovation
from external sources
• Analyzing data
• Integrate innovation
• Commercializing
innovations

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25
What Counts is Insight not … Numbers
We strive to do
the best we can with
the evidence at hand,
but we accept that
evidence may be
incomplete, noisy,
and even wrong
If you have certain
patterns in mind, you
will look for supporting
evidence naturally.
So ask for
We will be able to
gain insights from
the past to
improve the
future
Data science should anti‐patterns!
be about causation, not
correlation (watch for
bias and confounding
factors!)
Data, analyses,
methods and results
have to be publicly
shared
Good data science
does not get in the
way of developing
software but supports it
(makes it more efficient)
Don't show me
what is; show
me what to do
Your project has a
history. Learn from it.
Decide from it.
Embrace it!
Underlying theory
needs to inform the
data analysis
SE data sciences
should be actionable,
reproducible.
References
[1] Nayebi, M and Ruhe, G (2015), “Analytical Product Release Planning”, accepted to be
published in the book “The Art and Science of Analyzing Software Data: Analysis
Patterns”, C. Bird, T. Menzies, and T. Zimmermann (eds.), Kaufman & Morgan 2015.
[2] Nayebi, M and Ruhe, G (2015), “Analytic Open Innovation for Trade‐off Service Portfolio
Planning – A Case Study on Mining the Android App Market”. Submitted to Special Issue
on Software Business, JSS
[3] Nayebi, M. (2014), “Mining Release Cycles in the Android App Store”, The 36th CREST Open
Workshop on App Store Analysis, London, England
[4] S. Alam, S. M. Shahnewaz, D. Pfahl, and G. Ruhe, “Analysis and Improvement of Release
Readiness ‐ A Genetic Optimization Approach,” Proceedings of Product Focused Software
Development and Process Improvement (PROFES), 2014
[5] Workshop on Data Analytics, Dagstuhl 2014
[6] Chesbrough, H., “Open Innovation: The New Imperative for Creating and Profiting from
Technology”, Harvard Business Press, 2003.
[7] Ritchey, T. , "Wicked Problems‐‐Social Messes: Decision Support Modelling with Morphological
©Guenther Ruhe
Analysis.," Springer 2011.
Profes 2014, Helsinki ‐ © Guenther
Ruhe 50

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