Agile and Automation Conclave 2018
RAGHAVENDRA MEHARWADE
• Oxfam Trailwalker• Lead Functional Architect – Accenture
myWizard Agile• Scrum Master/Agile SME/Coach
ANUBHAV F GUPTA
• Agilist• myWizard Agile FA• CSM, SAFe Agilist, Trained Kanban
Practitioner
Agile and Automation Conclave 2018
AGENDA
Race for AI
What is AI?
What is ML?
Why Agile with AI?
Case Study
Next Steps, Possible Challenges and Solutions
Q&A
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Agile and Automation Conclave 2018
- by CBINSIGHTS
RACE FOR AI
– BY CBINSIGHTS https://www.cbinsights.com/research/top-acquirers-ai-startups-ma-timeline/
Agile and Automation Conclave 2018
WHAT IS AI?
AI ALLOWS SMART MACHINES TO EXTEND HUMAN CAPABILITIES
SENSE
COMPREHEND
ACT
LEARN
Computing Vision
Audio Processing
Natural Language ProcessingKnowledge Representation
Machine Learning
Expert Systems
Virtual Agent
Identity Analytics
Cognitive Robotics
Speech Analytics
Recommendation Systems
Data Visualization
Perceive the world
Analyze and understand
Make informed decisions
Improve performance
Agile and Automation Conclave 2018
FOUNDATION
ROBOTIC
VIRTUAL AGENTS & ANALYTICS
MACHINE LEARNING/DEEP LEARNING
Project level, ad-hoc automation
Automate repetitive and predictable tasks to eliminate manual
effort
Use of analytical tools to predict
and recommend
Self-learningsystems and
self-evolving tools
Agile Examples:• Automation of reporting
like Sprint Closure Report, Weekly Report etc.• Automation of Metrics
Calculation.
Agile Examples:• Prediction of velocity,
defects, throughput etc.• Root cause analysis using
descriptive analytics• Mitigation of risks using
predictive analytics.
Agile Examples:• Predict story points against
stories.• Predict efforts against tasks.• Insight into different aspects
using BOTs.
“INTELLIGENCE”
JUDGEMENT-DRIVEN
TRANSACTIONAL
RPA
AI
Agile Examples:• RPA based auto test
cases execution.• DevOps based CI, CD etc.
AI AUTOMATION LEVELS
Agile and Automation Conclave 2018Copyright © 2018 Accenture All rights reserved. 8
WHAT IS MACHINE LEARNING?
Agile and Automation Conclave 2018Copyright © 2018 Accenture All rights reserved. 9
“Machine learning is the science of getting
computers to act without being explicitly
programmed.” – Stanford
“Machine learning algorithms can figure out
how to perform important tasks by generalizing from examples.” – University of
Washington
MACHINE LEARNING
Agile and Automation Conclave 2018
TYPES OF MACHINE LEARNINGMachine Learning
• The training set is labeled. • Classification and Regression
• The training set is unlabeled. • Clustering
• No labeled or unlabeled data set.
• Algorithm learns to act in an env. to maximize reward.
• Customer Segmentation• Weather Forecast
• Spam Mail Detection• Speech Recognition
• Self-driving Car• Chess
Supervised Unsupervised Reinforcement
Agile and Automation Conclave 2018
COMMON MACHINE LEARNING ALGORITHMS
Reinforcement
Unsupervised
Supervised
Classification
Regression
Clustering
Support Vector
Machines
Discriminant
AnalysisNaïve Bayes
Nearest
Neighbor
Linear
Regression,
GLM
SVR, GPREnsemble
MethodsDecision Trees
Neural
Networks
K-Means, K-
Medoids Fuzzy
C-Means
HierarchicalGaussian
Mixture
Neural
Networks
Hidden Markov
Model
Temporal
Difference
Learning
Q-learning SARSA
Learning
Classifier
System
Dynamic
Treatment
Regime
ML
Agile and Automation Conclave 2018
FEATURE ENGINEERINGFeature Engineering is also called variable engineering or attribute engineering.
It is the selection of attributes in your data (such as columns in tabular data) that are most relevant to the predictive modeling problem you are working on.
Agile and Automation Conclave 2018
BINARY CLASSIFICATION
Agile Example:
Story is meeting the INVEST criteria or not. Requirement is ambiguous or not.Defect is duplicate or not.
Agile and Automation Conclave 2018
MULTI-CLASS CLASSIFICATION
Agile Example:
Classification of defects in backlog by severity (critical, high, medium, low)
Agile and Automation Conclave 2018
LINEAR REGRESSION
Agile Example:
Complexity of the story impacting the size (in story points) of the story.
Agile and Automation Conclave 2018
MULTIPLE REGRESSION
Agile Example:
Multiple variables Impacting the size of the story.• Complexity• Team member• CRUD operation• Team composition
Agile and Automation Conclave 2018
CLUSTERING
Agile Example:
Clustering of stories of similar type and then prediction of tasks for stories.
Agile and Automation Conclave 2018
TEXT ANALYTICS/NLP
Text Analytics
Types – ways to frame your data
Sentiment AnalysisAnalyze opinions for tones
Topic ModelingIdentify dominant themes
Term Frequency – Inverse Document FrequencyUncover frequency of a word
Named Entity RecognitionRecognize people, organizations, places and dates
Event ExtractionDiscover relationships between people, organizations, places and dates
Agile Example:Analyzing acceptance criteria of user stories to check quality of the story or to check testability of the story.
Agile and Automation Conclave 2018
STATE OF AGILE
Challenges Experienced Adopting & Scaling Agile Top 5 Tips for Success with Scaling
1
INTERNAL AGILE COACHES
EXECUTIVE SPONSERSHIP
CONSISTENT PROCESS AND PRACTISES
IMPLEMENTATION OF A COMMON TOOL ACROSS TEAMS
AGILE CONSULTANTS OR TRAINERS
5
2
3
4
Reference : https://www.versionone.com/about/press-releases/versionone-releases-11th-annual-state-of-agile-report/
Agile and Automation Conclave 2018
EXTENDING THE LIST OF SUCCESS FACTORS/TIPSSuccess Factors of Agile
Collaboration
Inspect & Adapt
Fail Fast
Continuous Improvement
Team Dynamics
Quality / Working S/W
Reduce Waste
AI Technologies
NLP (Virtual Agents, Chat BOT …)
Descriptive Analytics
Predictive Analytics
Prescriptive Analytics
Machine Learning / Deep Learning
RPA
Agile and Automation Conclave 2018
ENGAGEMENT CONTEXT
• Alpha Omega Inc. recently entered into an partnership with Japanese firm to provide backend
software support for newly furnished transport dashboard displays.
• The software is part of a bigger program that involves multiple related software systems, building
the displays and installing the displays on the railway stations.
• Alpha Omega has deployed four distributed scrum teams (Tokyo and Madrid) to work
collaboratively to deliver its scope.
• Each team is consisting of nine members (4-5 in Tokyo, the rest in Madrid).
• Scrum Master and Product Owner are based out of Tokyo.
• Since last month, a few teams are going through certain challenges which have risked Alpha
Omega’s delivery commitment and the overall program.
• You have been chosen as an Agile cum automation SME to help scrum teams solving their
challenges. We have connected with the teams and figured out the challenges teams are facing.
Agile and Automation Conclave 2018
ENGAGEMENT CONTEXT - CONTINUED
Product Owner Group Challenges:
• Most of the Product Owners are doing the job for the first time—not having enough experience
writing good stories / perform multiple rounds of review iterations with Scrum Master to
complete a story.
• Modules are tightly coupled leading dependent stories across several teams.
• Few Product Owners are not comfortable using English as communication language, converses
only in Japanese.
• Out of four, two Product Owners preferred ‘XYZ’ and the remaining two selected ‘ABC’ Agile ALM
tool to store their backlogs.
• The business user group has logged around 2400+ requirements in IBM Rational Req Pro which
forms the basis for prioritization.
• A good amount of these requirements have found to be duplicate over period of time.
Agile and Automation Conclave 2018
ENGAGEMENT CONTEXT - CONTINUED
Scrum Master Group Challenges:
• Out of four, two Scrum Masters are playing the role of admin for various tools, platforms and
environments as well as manage access control.
• The Release Management team is dependent on the Scrum Masters to collect measures and
metrics, publication of various reports (sprint planning outcome report, daily progress report,
sprint closure report, weekly status report).
• Scrum Masters are finding challenges to coach the team on ‘best-effort’ estimate.
Agile and Automation Conclave 2018
ENGAGEMENT CONTEXT - CONTINUED
Team Challenges:
• One member from each location has to stretch to attend each other's daily stand up for transition.
• Technology keeps changing in every sprint resulting into longer sprint planning sessions to arrive at sub tasks and estimate
hours.
• Production support staff seeks team support on intermittent basis for resolving tickets.
• Not everyone in the team has prior Agile experience.
• Team spends almost one full day to update Requirement Traceability Matrix (mandatory deliverable for every sprint).
• Good amount of time is dedicated for discussing stories/defects which found to be duplicate later.
• Team A is able to produce quality deliverables but not able to deliver the complete committed scope in a sprint.
• Team B is able to deliver what they commit to deliver in a sprint, but getting high severity defects during the sprint due to of
extra efforts to achieve the sprint goal.
• Team C spends lots of time in doing root cause analysis and reporting activities.
• Team D is facing an issue of changes in acceptance criteria during sprint execution.
Agile and Automation Conclave 2018
EXPECTATIONS FROM AGILE SME
As an Agile SME & Automation Expert, you are expected to:
• Go through challenges
• Identify automation opportunities
• Refer to data sheets provided and identify right features
• Update automation tracker with your findings
Agile and Automation Conclave 2018
ACTIVITY SCHEDULE
Stages Time (in Minutes) Description Set To Refer
1 10 • Explore case study and identify challenges Set 1
2 10• Identify automation opportunity for
selected challenges• Identify Level of Automation
Set 2 & Set 4
3 15 • Identify data sets and associated features/columns Set 3
4 10 • Selected participant will demo Automation Tracker in Set 5
Agile and Automation Conclave 2018
AUTOMATION TRACKER
Sr. No. Group Challenge Use Case for Automation / ML / AI Level of Automation Data Set & Features
10 Mins 10 Mins 15 Mins
1
2
Group
Team
Scrum Master
Product Owner
Challenge Use Case for Automation / ML / AI Data Set & Feature
< Specify the challenge which you are trying to solve > < Specify the use case which you are proposing as a solution of the challenge >
< Specify table name and respective column names
required to build AI model >
Level of AutomationFoundationRoboticVirtual Agents & AnalyticsMachine Learning / Deep Learning
Agile and Automation Conclave 2018
NEXT STEPS FOR AGILE SMES
Understand the exact problem or improvement area where AI capabilities can help.
Act as a functional expert and visualize the use case for AI
Analyze the historical data and visualize how identified use case be developed using this data like identification of features etc.
Highlight the benefits of using AI capabilities to the management to get required support and budget.
Work closely with the AI Dev Team to get it implemented correctly
Contribute towards building Agile data library!
Agile and Automation Conclave 2018
POSSIBLE CHALLENGES & SOLUTIONS• Lack of management support
• Insufficient budget
• Convincing buyer of value
– Highlighting the benefits of using AI capabilities
– Engagement and Involvement of management /client
– Standardization of practices, processes and metrics
• Unavailability of enough historical data with good volume and good quality
• Client confidentiality / data access
– Managing data repository at organization level for projects of different domains,
technologies etc.
• Short duration projects
• Implementation cost / effort
– Re-using AI assets developed
Agile and Automation Conclave 2018
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Thank You!
Raghavendra [email protected]
Anubhav F [email protected]
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