1

Information Extraction for Real-time Embedded Systems

Sebastian FischmeisterUniversity of Waterloo

esg.uwaterloo.ca

2

Aim of the Talk

• Provide an overview of the research done within or associated with the project ORF-RE 03-045: “Certification of Safety-critical Software-intensive Systems”

• Create an opportunity for integrating of research results and collaborating

3

Assumptions / Facts

• Software is where the innovation is happening!Features sell, apps everywhere

• Software size and complexity is thechallenge!

Illustrating one root cause:Bridge from Tokyo

to Vancouver

4

Assumptions / Facts

• Computing systems are now beyond deep human comprehension.

• Evidence:– Software size is growing exponentially– Processor complexity is growing exponentially– 80% of the developer’s time is debugging

– We have software with 100M lines of code!

5

100M LOC?

• Nuclear shutdown system: 40k lines of code

• F-22 Raptor (‘97): 1.7M lines of code• F-35 Joint Strike Fighter (‘06): 5.7M lines of code• Boeing 787 (‘09): 6.5M lines of code

• Current generation limousine: 100M LOC

Can we comprehend such software?

6

10 0

00 d

ots

100K?10M?

7

Trying to Understand the Certification Problem

• Toronto: 2,503,281 • Ontario: 13,210,667• USA: 300M

You need to ensure that each person is doingthe right thing at the right time.

8

PROJECT SAMPLER:REAL-TIME EMBEDDED SOFTWARE GROUP @ UNIVERSITY OF WATERLOO

9

Vision: Information Extraction

Time awareinstrumentation

Coverage criterion [RTAS’09, TII]

ISA extension [TR]

Time-triggeredruntime verification

Crit. CFG & sampling [FM’11]

Mem vs. sampl. tradeoff [RV’11]

Time-triggeredexecution monitoring

Markers[LCTES’10]

bitvec+[LCTES’11]

Observabilityin software

Super-loop[LCTES’11]

Preemptive[OPODIS’11]

Debugging, tracing &monitoring

framework forRT embeddedapplications

Tagging

Basics [TR] Security [TR]

10

Understanding Complex Programs• Problem: Can we efficiently trace information flow in a

software system? => Tagging

• Implemented in QNX at the kernel level• Applied to tracing, resource scheduling, and security• Applicable to testing, monitoring non-functional req.

Process

Network

Tag

Tag

Tag

Tag

X

11

Understanding Complex Programs• Problem: Can we instrument programs without

changing the timing (thus the behaviour)? => time-aware instrumentation

• Applied to three case studies (OLPC, FS, SNU)• Software solution, hardware solution, code dup• Useful for tracing, testing, information extraction

Instrumented

Freq

uenc

y

Execution time

Original

Deadline

X

X X

12

Monitoring Complex Programs• Problem: Can we engineer run-time monitoring

and checking of programs? => TTRVApplication

Program

Observer

Monitor

Steering

Report

Observe

Eval.properties

• Time-triggered monitoring & property evaluation

• Useful for system safety,security, steering, tuning, …

13

Trying to Understand Complex Programs• Problem: How do people try to understand

software systems? => debugging study

• Useful to guide future tools• Useful to understand developers’ minds

Successfuldebuggers

Failingdebuggers

14

Conclusions• Software systems are hard to understand• Software is growing in size and complexity

=> Developers need support to understand what is going on at run time!

• We research methods that help developers understand what the software is doing, especially tailored to (real-time) embedded systems.

• (We also work on benchmarking & real-time networking)• (We also host the CFI Real-time Embedded Software Lab)

15

Acknowledgements

• This research was supported in part by industrial partners and the Canadian tax payer!

• In collaboration with Akramul Azim, Pansy Arafa, Akramul Azim, Shay Berkovich, Borzoo Bonakdarpour, Sina Gholamian, Hany Kashif, Patrick Lam, Samaneh Navabpour, Hiren Patel, Yassir Rizwan, Ahmad Rehman, Johnson Thomas, Mahesh Tripunithara, Augusto Oliveira, Wallace Wu.

Thanks.

Questions?

(PS: Postdoc positions available, email me at sfischme@uwaterloo.ca)