quality in the new delivery paradigm

9
Quality in the new delivery paradigm of digital and automation [The Skewed Triangle] Raja Mohapatra @ Infosys Ltd

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Page 1: Quality in the new delivery paradigm

Quality in the new delivery paradigm of digital and

automation[The Skewed Triangle]

Raja Mohapatra @ Infosys Ltd

Page 2: Quality in the new delivery paradigm

The Skewed Triangle

Technology is consuming the process and making it a part of itself

Process

People

TechnologyQProcess

People

TechnologyQ+

Page 3: Quality in the new delivery paradigm

NASA Supercomputer of 80’s Laptop of Today

Delhi Skyline Singapore Skyline

Page 4: Quality in the new delivery paradigm

How Matured Process Works

Page 5: Quality in the new delivery paradigm

A simple Example @ Config Mgmt

Work Area Review Area Test Area Baseline

Area

Change ControlConfiguration Control

Process Artifact Developer Time Stamp Volume of Chg Nature of Chg

Development -3 Code # 1 Ms Y 20:30:15; 15/12/16 10% Logical

Review Code # 1 Mr X … 0% NA

Rework Code # 1 Ms Y … 20% 10% Cosmetic 210 Logical

Page 6: Quality in the new delivery paradigm

People and Technology Interaction [Control + Insight + Forecast]

40% 30% 25% 20%

10% 20% 30% 35%

50% 50% 45% 45%

0%

20%

40%

60%

80%

100%

120%

Dev.1 Dev.2 Dev.3 Dev.4

Defect Distributi on

Logical Cosmetic GUI

30%

60%50%

40%50%

0% 0% 0%

20%

40%50%

60%

0%

10%

20%

30%

40%

50%

60%

70%

CODE REVIEW UT IT ST

Stage wise Defect DistributionLogical Cosmetic Others

2.6

3.2

2.42.6

3.3

2.32.7

3.5

2.52.9

3.7

2.9

0.00.51.01.52.02.53.03.54.0

0102030405060708090

Rework Eff ort

No of Defects Rework Effort (Hrs) Effort / Defect

Page 7: Quality in the new delivery paradigm

Feature Engineering

Process Artifact Developer Time Stamp Volume of Chg Nature of Chg

Development -3 Code # 1 Ms Y 20:30:15; 15/12/16 10% Logical

Review Code # 1 Mr X … 0% NA

Rework Code # 1 Ms Y … 20% 10% Cosmetic 210 Logical

Records

Features

Page 8: Quality in the new delivery paradigm

Approach..• Brainstorm features: Really get into the problem, look at a lot of data,

study feature engineering on other problems and see what you can steal.• Devise features: Depends on your problem, but you may use automatic

feature extraction, manual feature construction and mixtures of the two• Select features: Use different feature importance scorings and feature

selection methods to prepare one or more “views” for your models to operate upon.• Evaluate models: Estimate model accuracy on unseen data using the

chosen features

Page 9: Quality in the new delivery paradigm