Keynote data analyticsforsw_productinnovation_pdf

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

Transcript of Keynote data analyticsforsw_productinnovation_pdf

Page 1: Keynote data analyticsforsw_productinnovation_pdf

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Data Analytics for Software Product Innovation

Guenther Ruhe

University of Calgary

©Guenther Ruhe

AGENDAAGENDA

How it began: The Esprit Project PROFES How it began: The Esprit Project PROFES

Product Innovation Product Innovation

Analytical Open InnovationAnalytical Open Innovation

AOI for Innovative ProductsAOI for Innovative Products

The Road AheadThe Road Ahead

PROFES 2014, Helsinki, Finland 2

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©Guenther Ruhe

Participating Companies

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©Guenther Ruhe

Project Team

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Frank van Latum

Pasi Kuvaya Janne JarvinenenMarkku Oivo

Dietmar Pfahl Rini van Solingen Guenther Ruhe

Andreas Birk

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©Guenther Ruhe

Elements of PROFES

• Combining and enhancing the strengths of goal‐oriented measurement, process assessment, product and process modelling and experience factory

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ISO15504

GQMGQMISO

15504

PROFES

QIP/EFISO9126

©Guenther Ruhe

Focus on PPDs

• Focus on investigating the relationship between product and process quality

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PPDPRODUCT PROCESS

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©Guenther Ruhe

Different Facets of PPDs

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Technologies Used

Design Inspections

ProductQuality

SoftwareProcess

Reliability Software Design

Low or AverageOverall Time Pressure

Context Characteristics

©Guenther Ruhe

AGENDAAGENDA

How it began: The Esprit Project PROFES How it began: The Esprit Project PROFES

Product Innovation Product Innovation

Analytical Open InnovationAnalytical Open Innovation

Analytics Case StudiesAnalytics Case Studies

The Road AheadThe Road Ahead

PROFES 2014, Helsinki, Finland 8

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©Guenther Ruhe

Innovation – What is it … after all?

• Innovativeness is the measure of “newness”

• New to the:

World

Market

Industry

Adopting unit

Consumer

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©Guenther RuhePROFES 2014, Helsinki, Finland 10

Crossing the Chasm 

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Being New … Being First

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• 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

©Guenther Ruhe

A Powerful Force for Everyday Fitness

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©Guenther Ruhe

AGENDAAGENDA

How it began: The Esprit Project PROFES How it began: The Esprit Project PROFES

Product Innovation Product Innovation

Analytical Open InnovationAnalytical Open Innovation

Analytics Case StudiesAnalytics Case Studies

The Road AheadThe Road Ahead

PROFES 2014, Helsinki, Finland 13

©Guenther Ruhe

Responding to change for gaining competitive advantage in the era of smart decisions will be based not on "gut instinct," but on predictive analytics.

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Ginni Rometty, Chairman, President and CEO, IBM, 2013

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©Guenther RuhePROFES 2014, Helsinki, Finland 15

Innovative product development: New ideas from leveraging external knowledge and resources, applying 

innovative processes and  technologies

©Guenther Ruhe

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. 

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Chesbrough, H., “Open Innovation: The New 

Imperative for Creating and Profiting 

from Technology”, Harvard Business 

Press, 2003. 

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Open Innovation for New Products

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©Guenther Ruhe

Analytic Open Innovation

• Open innovation utilizing the power of analytics (processes, tools, knowledge, techniques, decisions)

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©Guenther Ruhe

AGENDAAGENDA

How it began: The Esprit Project PROFES How it began: The Esprit Project PROFES

Product Innovation Product Innovation

Analytical Open InnovationAnalytical Open Innovation

Analytics Case StudiesAnalytics Case Studies

The Road AheadThe Road Ahead

PROFES 2014, Helsinki, Finland 19

©Guenther Ruhe

New Products – Data & Information Needs

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Information needs

Type of release planning problem

Features

Feature dep

enden

cies

Feature value

Stakeh

older

Stakeh

older opinion and 

priorities

Release readiness

Market tren

ds

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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Text mining

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Patternrecognition

Rough setanalysis

Cluster analysis

Morphologicalanalysis

Simulation

Optimization

CrowdsouringAnalytical Kano model

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Value Synergies

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In consideration of synergies

Without synergy considerations

Considering constraints is causing structural differences in plans and increase value (stakeholders feature points)

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Re‐planning of not implemented features before starting Q2 with updated data from different customer groups 

Re‐planning of not implemented features before starting Q3 with updated data from different customer groups 

Re‐planning of not implemented features before starting Q4 with updated data from different customer groups 

Time‐dependent Value

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©Guenther Ruhe

Text mining

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Patternrecognition

Rough setanalysis

Cluster analysis

Morphological analysis

Simulation

Optimization

CrowdsouringAnalytical Kano model

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©Guenther RuhePROFES 2014, Helsinki, Finland 27

1

2

ClustersClusters

Customization towards groups of customers

Having two cluster 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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Text mining

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Patternrecognition

Rough setanalysis

Cluster analysis

Morphological analysis

Simulation

Optimization

CrowdsouringAnalytical Kano model

©Guenther RuhePROFES 2014, Helsinki, Finland 30

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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

©Guenther Ruhe

Text mining

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Patternrecognition

Rough setanalysis

Cluster analysis

Morphological analysis

Simulation

Optimization

CrowdsouringAnalytical Kano model

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Customer satisfied

Customer dissatisfied

Requirementfulfilled 

Requirement not fulfilled

One‐Dimensional requirement

Attractive requirements

Must‐be requirements

(Berger et al., 1993)

Articulated specified measurable technicalNot expressed 

Customer tailored Cause delight

ImpliedSelf‐evidentNot expressedObvious

©Guenther Ruhe

OTT Services ‐ Kano Questionnaire 

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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?

______ 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 formof the question

Dysfunctional formof the question

https://qtrial2014.az1.qualtrics.com/SE/?SID=SV_eeMrc9WjpFX6ZKd

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Kano Evaluation Table

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Customer Requirements

Dysfunctional questions

Like Must‐be NeutralLive 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) 

©Guenther Ruhe

Text mining

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Patternrecognition

Rough setanalysis

Cluster analysis

Morphological analysis

Simulation

Optimization

CrowdsouringAnalytical Kano model

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©Guenther RuhePROFES 2014, Helsinki, Finland 37

©Guenther Ruhe

Text mining

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Patternrecognition

Rough setanalysis

Cluster analysis

Morphological analysis

Simulation

Optimization

CrowdsouringAnalytical Kano model

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New Product (Super App) Design

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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

©Guenther Ruhe

Release Readiness Optimization

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0.55

0.6

0.65

0.7

0.75

0.8

142 149 156 163 170

Calculated readiness

Projected readiness onrelease dateR

eadi

ness

Development time (days)

0.00 0.20 0.40 0.60 0.80 1.00

Number of feature implemented

Percentage of successful builds/integrationCode Churn per contributor per day

Defect find rate for last two weeks

Percentage of defect fixedDefects/KLOC

Test coverage: Covered LOC/ LOC

Number of code smells per class

Percentage of duplicated codeAverage method complexity

Percentage of issues fixed ()

Level of attribute satisfaction

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Release Readiness Optimization (2/2)

12/12/201441

©Guenther Ruhe

Text mining

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Patternrecognition

Rough setanalysis

Cluster analysis

Morphological analysis

Simulation

Optimization

CrowdsouringAnalytical Kano model

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©Guenther RuhePROFES 2014, Helsinki, Finland 43

(X*Y)*X*

(YXm)n

XnYmYm(YX)nXl

(XYm)n

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738

©Guenther Ruhe

AGENDAAGENDA

How it began: The Esprit Project PROFES How it began: The Esprit Project PROFES

Product Innovation Product Innovation

Analytical Open InnovationAnalytical Open Innovation

Analytics Case StudiesAnalytics Case Studies

The Road AheadThe Road Ahead

PROFES 2014, Helsinki, Finland 44

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©Guenther Ruhe

Text mining

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Patternrecognition

Rough setanalysis

Cluster analysis

Morphological analysis

Simulation

Optimization

CrowdsouringAnalytical Kano model

©Guenther RuhePROFES 2014, Helsinki, Finland 46

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©Guenther RuhePROFES 2014, Helsinki, Finland 47

PPDINNOVATIVE PRODUCTS PROCESSES

Innovative products through innovative processes

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Innovative Products through AOI

INNOVATIVE PRODUCTS

PROCESSES

• Acquiring innovationfrom external sources

• Analyzing data

• Integrate innovation

• Commercializing innovations

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©Guenther RuhePROFES 2014, Helsinki, Finland 49

We strive to  dothe 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 anti‐patterns!Data science should 

be about causation, not correlation (watch for bias and confounding 

factors!)

Data, analyses, methods and results have to be publicly 

shared

Don't show me what is; show me what to do

Your project has a history. Learn from it. 

Decide from it. Embrace it!

We will be able to gain insights from 

the past  to improve the 

future

Good data science does not get in theway of developing 

software but supports it (makes it more efficient)

Underlying theory needs to inform the 

data analysis

SE data sciencesshould be actionable, 

reproducible. 

SE data sciencesshould be actionable, 

reproducible. 

What Counts is Insight not … Numbers 

©Guenther Ruhe

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 Analysis.," Springer 2011.

Profes 2014, Helsinki ‐© Guenther Ruhe

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