This is a retrospective of my 30 year career in Software Engineering. Most of the tools I worked on have to do with Java, Eclipse, Python, visualization, profiling, and people.

1. Visualization of
Complex Systems
Chris Laffra
1989 - 2019

2. Who is Chris Laffra?
Chris works at Uber Amsterdam, since Jan 2018.
Chris has worked in Tech at IBM Research, Morgan Stanley,
IBM Rational, Bank of America, and Google. At Uber, Chris
works on tools that increase Uber’s ability to roll out software
faster and more frequently.
Chris has spent the last 30 years of his life on a mission to
make other engineers more productive.
See chrislaffra.com for more details.

3. Goals
This is an overview of my career. Over the last 30 years,
I have spent most of my technical career towards
understanding, profiling, and visualizing complex systems.

4. System
“Related entities forming a unified whole”
First, what is a system?

5. Complex
“No centrally defined interactions”
“Consisting of many parts”
“Complicated”
Second, what is complexity?

6. Cynefin framework

7. “Images, diagrams, or animations
communicating a message.”
“A picture is worth a thousand words”
Visualization
Finally, what is visualization?

8. PROCOL
How I got started, by visualizing the execution of applications
written in PROCOL, my PhD thesis language.
Book #1 Book #2

9. Explore the
heap of objects,
starting with the
“main” object.
Understand the
shape of an object
See live calls to
object methods
And argument
values
1989
The goal: Understand
what is happening and
see surprises.

10. HotWire
My first job after my PhD. Developed a system for custom
visualizations for C++ and Smalltalk.

11. the
subject
Showing
live objects,
methods, &
patterns

12. Original source code
Custom, declarative
visualization script.
The result
1994

13. JVM
As soon as Java came out, I wanted to learn more about it.
So, I ended up writing book the fourth book that was
published on Java while working at Morgan Stanley.
Book #3

14. At IBM Research, I worked on Jax, which used JikesBT, that I developed, to analyze whole
program call graphs and optimize Java programs for size. For example, we reduced javac to
half its size. Aside from being smaller, the secondary benefit was that applets loaded faster. 1999

15. 2001
I wrote the first AdBlock, 8 years before the actual AdBlock was launched. I instrumented the
entire IE Java runtime, using JikesBT to do bytecode instrumentation, and added specific
hooks for networking, general profiling, and ability to stop/start/kill any applet remotely.

16. Eclipse
Book #4
My IBM/OTI team in Amsterdam developed the first commercial IBM product
based on Eclipse, focusing on embedded Java. We constantly worried about size
and performance. The learnings from Jax were applied to make embedded Java
apps as small as possible. I also started looking into performance of larger Java
programs, such as Eclipse itself. To better understand how Eclipse works, I signed
up to co-author a book on Eclipse and wrote numerous profilers.

17. One experiment was a
heap-exploration tool, in
this case showing all the
objects in Eclipse
pointing to a given
editor.
2004

18. A custom CPU
and memory
analysis tool I
developed for
Eclipse was
XRay. The goal
was to show
patterns and for
engineers to
discover
surprising
patterns related
to performance.
2004

19. Other tools, such as Eclipse Universe, offered novel structural communication patterns
between Eclipse plugins, allowing engineers to dive deeper into specific scenarios. It showed
domain-specific issues, in addition to resource management issues and challenges. 2004

20. Finally, working with IBM Research, I devised novel way to package up Eclipse applications
using streaming delivery, combined with introducing memory-mapped shared classes using the
J9 JXE format. This allowed us to save about ⅓ in startup time and memory used. 2005
WebSphere VMs per Node
Normal JXEs
My impact.
2X scaling.

21. Python
Book #5
At Bank of America, I was the first hire on the Quartz project. My interest on this
project was to increase other people’s understanding in how the platform was
structured and implemented. I developed a live-coding training system and
contributed to coding workflows, training, and general developer advocacy.
Quartz
Academy
Chris Laffra
Bank of America

22. 2012
At BofA, I developed a
live coding system
similar to Jupyter
notebooks.
A novel invention I
added was the ability
to do time-travel
debugging of generic
Python code, but also
algorithms that were
implemented using
Quartz features.
I also wrote all the
training materials,
published internally.

23. Still at BofA, I
developed repository of
>50 Computer Science
algorithms with
visualization.
This essentially was a
reimplementation of
Hotwire for Python.
This was implemented
using Google
AppEngine and cool
Python execution.
2013

24. code test
2016
The goal of Auger was to introspect a running system, record everything, and then produce
specific output that uses all the data being recorded. In this case I generate unit tests. But,
another application could be to automatically generate performance metrics, or derive types.

25. Pava
Pava takes Java
bytecodes and
converts them to
Python source code.
Not sure how useful
Pava really is, but for
me, this was really an
effort to study the
differences in
bytecodes for Java
and Python.
2017

26. Ikke
HappyMac
Tempo
At Google, I worked a lot on Java and Android tools and apps. I also developed an
interest in making the life of engineers better by providing them with tools to
understand how they fit into their organisation and how they can profile their life.

27. 2016-2018
People also represent
a complex system and
I played a lot with
different visualizations
of my personal and
work graph to discover
human connections.
github: Ikke Graph.

28. While working at Uber, my MacBook was always struggling with the CPU usage of Intellij and
the Android Emulator. To understand better what was going on, I created HappyMac, which
allows its user to discover expensive processes and auto-suspend them using some rules. 2018

29. HappyWeb is like HappyMac, but focuses on slow network connections. 2018

30. Tempo constantly
watches your
MacBook and
records the
currently active
program and saves
an event in a local
SQL database.
The recorded
events can show
effectiveness of
workflows, how
much time is spent
in certain activities,
and how to avoid
context switches.
A human profiler?
2019

31. Summary
Complex systems are all around us, either as technical solutions or as connections between humans. In
order to improve these complex systems, we need to understand them better. To understand them better,
we need to measure and generate metrics. Custom tools will then provide insights.

33. The End
“Be humble, we have small brains.”