Agile Mumbai 2022 Combining Human and Artificial Intelligence for Business Agility Rohit Handa Director, Digital Products & Platforms, HCL Technologies Ltd

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CombininghumanandArtificialIntelligenceforBusinessAgility

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Wewillcover…
1. NeedtoalignAIbasedtechniquestoPMPractices
2. AI:Potentialgamechanger for AgileProject Management
1. AIenabledBusinessAgility:WHAT,WHYandHOW?
2. Next Steps
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Hello!
Quick Introduction
D i r e c t o r ,
D i g i t a l P r o d u c t s & P l a t f o r m s
H C L T e c h n o l o g i e s L t d
• Who Am I ? Digital products consultant for global clients with
focus on artificial intelligence and machine learning, computer
vision & conversational commerce.
• What I do? 21 years of work experience defining strategic
direction, taking new products to market, creating innovative
solutions, and growing new businesses using enterprise digital
technologies.
• Loves to Learn, Un-Learn, Re-Learn: 9x Certified Azure Cloud
practitioner & Microsoft Trainer, holding global industry level
project management certifications like PMP, TOGAF and ITIL V3.
• Passionate about teaching and providing awareness on AI to
business and wider community.

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Now, if we think over for a bit…
Today, AI is often misunderstood as any automation!!

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Rate of technology adoption
and change is
accelerating
Technology has become an
existential need of an
organization
Tech Agility beyond coding is
essential for business &
operations to sustain and thrive
TheRealityof ProjectManagementPracticetoday
Dealing with
volatility,
uncertainty ,
complexity and
variability due to human aspect and
…. yet need to grow with velocity
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…whichisbackedupbymarketstatistics (www.agilemumbai.com)

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AreTechnicalAgility&Enablersenough… (www.agilemumbai.com)

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ArtificialIntelligence:
PotentialGameChangerforAgileProjectManagement
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1. Refine items in Product Backlog
2. Size Estimations
3. Sprint Planning
4. Sprint Backlog
5. Task Breakout
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 Identifying backlog items
 Refining backlog items
 Sprint Planning
 Pro-actively monitoring sprint progress
and managing risks

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**Source: AnalyticsIndiaMag.com
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REPRESENTATION
LEARNING
ENGINE Analyze sources in natural text:
• Product visions, sprint goals, description of backlog items, and communication among team members (e.g., comments
on backlog items) ,
• Codebases contain documentations such as release notes and comments,
• Account the capability and dynamics of agile teams through their involvement in project artifacts
1. NLP component which performs automatic analysis on project textual artifacts and then generates vector
representations of those artifacts.
• Deep learning-based NLP techniques such as word2vec, paragraph2vec, Long Short-Term Memory (used in Google
Translate), or Convolutional Neural Networks (used in Facebook’s DeepText engine) can generate dense vector
representations that produce superior results on various NLP tasks
2. Code Modelingcomponent is responsible for learning vector representations which reflect the semantic and
syntactic structure of source code.
• Long Short-Term Memory (LSTM) to automatically learn vector representations for both backlog items and source code
.LSTM enables us to learn the semantics and syntactic structures, particularly the long-term dependencies, existing in
both natural text and source code.
3. Feature Extraction and Aggregation extracts all the vector representations of the artifacts related
to a developer and learn to aggregate them to form a vector representation of the developer.
• This representation will be enriched with features representing work and social dependencies between team
members, extracted from communication logs (e.g. comments or discussions on work items).
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ANALYTICS
ENGINE (www.agilemumbai.com)

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PLANNING
&
OPTIMIZATION
ENGINE (www.agilemumbai.com)

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Demo
Deep learning-based models for software effort estimation using story points in agile environments
DATASET CONSIDERED:
Comprehensive dataset for story points-based estimation that contains 23,313 issues from 16 open-
source projects
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APPROACH TAKEN:
Our overall research goal is to build a prediction system that takes as input the title and description of an issue
and produces a story-point estimate for the issue. Title and description are required information for any issue
tracking system
• ABSEE estimate deals with the process of identifying one or more historical projects similar
to the target project, and from them infer the estimate. In other words, but using the same
line of reasoning
• A requirement in this context can be a case of use, a user’s story, or any software
requirement, provided that the data is in text format, and aligned with the target effort.
• We combine the title and description of an issue report into a single text document where
the title is followed by the description.
• Our approach computes vector representations for these documents. These
representations are then used as features to predict the story points of each issue.
• It is important to note that these features are automatically learned from raw text, hence
removing us from manually engineering the features.
Long-Deep Recurrent Neural Network
(LD-RNN) that has been designed for the story point prediction system.
It is composed of four components arranged sequentially:
(i) word embedding,
(ii) document representation using Long Short-Term Memory (LSTM) ,
(iii) deep representation using BERT ( Bidirectional Encoded Representations from
transformers) model
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Evaluation Results
The best results are highlighted in bold.
MAE - the lower the better, SA - the higher the better.

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Quick Glimpse of Predictions Vs Actual StoryPoint
estimations
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 Collect more textual requirement data to balance the dataset against existing labels, and thereby
increase the number of contexts; or apply data augmentation techniques to improve results.
 Update BERT_base vocabulary, including specific vocabulary, and then fine-tune.
 Perform fine tuning, such as with model BERT_large, and compare the results.
 Study and apply effective data augmentation techniques in order to balance the number of samples
in each existing effort class, and thus obtain possible improvements in the results.
 Study and apply different combinations of the layers in the BERT model, in order to evaluate the
performance of the model regarding fine-tuning
Conclusion & several possibilities to fine tune
research further
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Evaluate it using our existing
dataset of few open-source
projects.
IntroduceAI based tools in your
project management portfolio
Collaboratewith our existing industry
partners to perform an evaluation on
commercial software agile projects
Believethat AI will assist, not substitute,
human teams. Individuals, interactions,
and collaboration are still the key
elements of project success
KeyTakeaways
T O O L S I N M A R K E T
• Project Insight
• Trello
• Teamwork
• BaseCamp
• Wrike
• Asana
• ClickUp
• Proofhub
1
2
3
4
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THANK YOU
Rohit Handa
Director, HCL Technologies Ltd.
+91-9810182936
https://www.linkedin.com/in/handarohit2002/