1. Effective Debugging

2. Why Debugging Matters There will be bugs
Debugging (and testing) take 50% of software development time [1]
Not just for software: any complex system
[1] Beizer, B. (1990). Software Testing Techniques. International Thomson Computer Press.

3. Debugging Attitude Debugging is a learned skill
You can learn to debug effectively
The system is not magic
You can understand it
Debugging can be deeply rewarding
Driven by curiosity & creativity
Satisfaction of understanding a bug’s root cause

4. Today Systematic Approach to Debugging Interactive Debugging Demo
Infection Model
Wolf-Fence Algorithm
Detective Method
Interactive Debugging Demo
Team Debugging Activity

5. Infection Model       Software systems are causal
States/
Program variables
Software systems are causal
So are many other types of systems…
A faulty operation causes an infection
The infection spreads through the program state
Eventually an infection causes an observable failure
Example: crash, print wrong value, … 
Faulty code 
Infected state    
Observed Failure

6. Wolf-Fence Algorithm
How to find a wolf in Alaska? ?

7. Identifying the Root Cause
Variables
Identifying the Root Cause
Time
Potential set of root causes is huge!
Need to systematically eliminate possible causes
Goal: most information for least effort
Quickly identify root cause
Tools:
Wolf-fence
Causal control flow of program         
How to build a wolf-fence?
* Inspect values with debugger
* Print-lining   
Observed Failure

8. Detective (or Scientific) Method
Description
Examples
Inspect program state
Validate/Invalidate hypothesis
View stdout/stderr
Inspect variables with debugger
Observe
Hypothesize
Predict
Experiment
Develop potential cause for observed behaviour
This SEGFAULT is caused by exceeding bounds of array ‘street_segments’
Predict expected behaviour for hypothesized cause
Variable ‘i’ will exceed ‘street_segments.size()’ at some point
Observe/Predict/Experiment largely mechanical
Hard part is generating good hypothesise.
Hypothesis goals: Easily testable, quickly lead to root cause (may take several iterations)
Improves greatly with experience!
Perform a controlled experiment to verify prediction
Re-run identical scenario in debugger

9. Debugging Demo
Demo example from their project (e.g. M2 graphics crash)
Also illustrate differences between build-types: Release/Debug/DebugOpt

10. Team Debugging Activity

11. Argument Sorter $ ./argsort 6 1 3 Output: 1 3 6
Sorts its command-line arguments:
$ ./argsort 6 1 3
Output: 1 3 6
$ ./argsort
Output:

12. Argument Sorter Code int main(int argc, char* argv[]) {
std::vector<int> vec;
for (int i = 0; i < argc - 1; ++i) {
std::stringstream ss(argv[i]);
int v;
ss >> v; }
shell_sort(vec);
std::cout << "Output: ";
for (int i = 0; i < vec.size(); ++i) {
std::cout << i << " ";
std::cout << "\n";
void shell_sort(std::vector<int>& vec) {
int h = 1;
do {
h = h * 3 + 1;
} while (h <= vec.size());
h /= 3;
for (int i = h; i < vec.size(); ++i) {
char j, v = vec[i];
for (j = i;
j >= h && vec[j - h] > v; j -= h) {
vec[j] = vec[j - h]; }
if (i != j) {
vec[j] = v;
} while (h != 1);
Demo web gui

13. Argument Sorter $ ./argsort 6 1 3 Output: 1 3 6
Sorts its command-line arguments:
$ ./argsort 6 1 3
Output: 1 3 6
argc = 4
argv = {“./argsort”, “6”, “1”, “3”}
$ ./argsort
Output:
argc = 6
argv = {“./argsort”, “115”, “1”, “50”, “42”, “130”}

14. Team Debugging Activity
Go To:
argsorter code in web-based IDE
Try to fix all the bugs!
Detective/Scientific Method
Infection Model
Wolf-Fences ?
Brainstorm ideas w/ students:
* Good wolf-fences
* Hypothesises, how to test?
Observe
Hypothesize
Predict
Experiment 
Faulty code
Infected
Value 

15. More Resources
A. Zeller, “Why programs fail: a guide to systematic debugging”, Morgan Kaufmann, 2009
EJ Gauss, “The Wolf Fence Algorithm for Debugging”, Communications of the ACM, 1982