This document discusses quality in new digital and automated delivery paradigms. It uses an example of configuration management to illustrate how mature processes work with people and technology interacting through control, insight and forecasting. The document presents approaches to feature engineering including brainstorming features by deeply examining the problem and related data, devising features through automatic extraction, manual construction or mixtures, selecting features using importance scoring and selection methods, and evaluating models on unseen data using chosen features.

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

2. The Skewed Triangle
Technology is consuming the process and making it a part of itself
Process
People
Technology
Q
Process
People
Technology
Q+

3. NASA Supercomputer of 80’s Laptop of Today
Delhi Skyline Singapore Skyline

4. How Matured Process Works

5. A simple Example @ Config Mgmt
Work Area
Review
Area
Test Area
Baseline
Area
Change Control
Configuration 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

6. 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 Distribution
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 Distribution
Logical Cosmetic Others
2.6
3.2
2.4
2.6
3.3
2.3
2.7
3.5
2.5
2.9
3.7
2.9
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
0
10
20
30
40
50
60
70
80
90
Rework Effort
No of Defects Rework Effort (Hrs) Effort / Defect

7. 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

8. 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