This document summarizes a talk given about the most influential paper award from ICSE2023 on program repair and auto-coding. It discusses: 1. The 2013 SemFix paper which introduced an automated repair method using symbolic execution, constraint solving, and program synthesis to generate patches without formal specifications. 2. How subsequent work incorporated learning and inference techniques to glean specifications from tests to guide repair when specifications were not available. 3. The impact of machine learning approaches on automated program repair, including learning from large code change datasets to predict edits, and opportunities for continued improvement in localization and accuracy.

1. Abhik Roychoudhury, National University of Singapore
Satish Chandra, Google
PROGRAM REPAIR
& AUTO-CODING
Talk for ICSE 2023 10-year
Most Influential Paper Award

2. ICSE2023 MIP Award Talk
Hoang Duong Thien Nguyen, Dawei Qi, Abhik Roychoudhury
School of Computing, National University of Singapore, Singapore
Satish Chandra
IBM Research, USA
01
SemFix: ICSE 2013
In this paper, we present an automated repair method based on symbolic
execution, constraint solving and program synthesis.

3. Education Correctness Security
Program
Repair
Buggy Program
Tests
Patched
Program
02
ICSE2023 MIP Award Talk
PROGRAM REPAIR
Baseline
Codebase
Initial
Req
Modified
Codebase
CS1010S, ...
First-year programming
Intelligent Tutoring
System
Augmented
Req

4. THE DEBUGGING PROBLEM
05
ICSE2023 MIP Award Talk
RETROSPECTIVE: DEBUGGING
Source of specifications:
Previous version
Another implementation,
Tests … ?

5. Dagstuhl Seminar 13061
Fault Prediction, Localization, and Repair, Feb 2013
Repair exploiting symbolic execution
• Scalability with respect to search space size.
Repair via constraint solving
• Synthesize rather than lifting fixes from elsewhere.
Repair without formal specifications
• Do not depend on any being available.
From https://www.dagstuhl.de/13061
07
ICSE2023 MIP Award Talk
SPEC from TESTS

6. Test ID a b c oracle Pass
1 -1 -1 -1 INVALID
2 1 1 1 EQUILATERAL
3 2 2 3 ISOSCELES
4 2 3 2 ISOSCELES
5 3 2 2 ISOSCELES
6 2 3 4 SCALANE
1 int triangle(int a, int b, int c){
2 if (a <= 0 || b <= 0 || c <= 0)
3. return INVALID;
4. if (a == b && b == c)
5. return EQUILATERAL;
6. if (a == b || b != c) // bug !
7. return ISOSCELES;
8. return SCALENE;
9. }
08
Correct fix
(a == b || b== c || a == c)
ICSE2023 MIP Award Talk
EXAMPLE

7. Accumulated constraints
f (2,2,3) == true ^
f (2,3,2) == true ^
…
Find a f satisfying this constraint
By fixing the set of operators appearing in f
Program synthesis with fixed set of operators
1 int triangle (int a, int b, int c) {
if (a <= 0 || b <= 0 ||
c<=0) return INVALID;
if (a == b && b == c)
return EQUILATERAL;
if (f (a, b,c))
return ISOSCELES;
return SCALENE;
2
3
4
5
6
7
8
9 }
a==2
7
b==2 c==3
f (2,2,3) = true
Symbolic Execution
ICSE2023 MIP Award Talk
SPEC. from TESTS
Automatically generate the constraint
f (2,2,3) ^ f (2,3,2) ^ f (3,2,2) ^ ¬ f (2,3,4)
Solution
f(a,b,c) = (a == b || b == c || a == c)

8. “Program testing and program proving can
be considered as extreme alternatives.….
This paper describes a practical approach
between these two extremes…
Each symbolic execution result may be equivalent
to a large number of normal tests”
TESTING/
VERIFICATION
...
8
ICSE2023 MIP Award Talk
SYMBOLIC EXECUTION (1976)

9. Specification Inference
In the absence of formal specifications,
analyze the buggy program and its
artifacts such as execution traces via
various heuristics to glean a
specification about how it can pass tests
and what could have gone wrong!
1 int triangle (int a, int b, int c) {
if (a <= 0 || b <= 0 ||
c<=0) return INVALID;
if (a == b && b == c)
return EQUILATERAL;
if (f (a, b,c)) // X
return ISOSCELES;
return SCALENE;
2
3
4
5
6
7
8
9 }
a==2 b==2 c==3
X = true
Symbolic Execution
9
ICSE2023 MIP Award Talk
SE for REPAIR

10. a <=0 || b <= 0 || c <= 0
Yes
Yes
Yes
No
No
No
a == b && b == c
1, 1, 1
1,1,2
a == b || b != c
2,3,4
10
ICSE2023 MIP Award Talk
SE for TESTING
1 int triangle (int a, int b, int c) {
if (a <= 0 || b <= 0 ||
c<=0) return INVALID;
if (a == b && b == c)
return EQUILATERAL;
if (a == b || b != c)
return ISOSCELES;
return SCALINE;
2
3
4
5
6
7
8
9 }
-1, -1, -1

11. 11
ICSE2023 MIP Award Talk
REPAIR / TESTING a <=0 || b <= 0 || c <= 0
Yes
Yes
Yes
No
No
No
a == b && b == c
1, 1, 1
1,1,2
a == b || b != c
2,3,4
-1, -1, -1
1 int triangle (int a, int b, int c) {
if (a <= 0 || b <= 0 ||
c<=0) return INVALID;
if (a == b && b == c)
return EQUILATERAL;
if (f (a, b,c)) // X
return ISOSCELES;
return SCALINE;
2
3
4
5
6
7
8
9 }
a==2 b==2 c==3
X= true / X = f(2,2,3)
Symbolic Execution

12. SemFix paper
comes here
Passing &
failing tests
Extract
constraints
Learning/
Inference
Generate patch
candidates
Fault localization
Semantic
Repair
Learning-based
Repair
Search-based
Repair
Synthesize code via
constraint solving Predict patch
Validate patch
candidates
Model of patches
03
Patch
Code
transformations
Buggy Program Code corpus
SUMMARY
for t in Tests {
generate repair constraint
}
Synthesize e from
t
t t
Semantics-based Schematic

13. A BRIEF HISTORY OF 2013-2023
ML makes significant inroads into software tools
• code completion
• code search and recommendation
• troubleshooting
• test selection
• …
• and of course, automated program repair!
From research to mainstream in less than 10 years
A new era of
software tools
ICSE2023 MIP Award Talk
Large code repositories
aka “big code”
Huge progress in ML
esp. in deep learning

14. ML COMES TO
AUTOMATED PROGRAM REPAIR
Immense amount of code change data available on past fixes
• Sometimes even aligned with bug symptom
ML problem
• Given a potentially buggy code fragment, predict an edit
Software tool problem
• Localize the error [as before]
• Predict an edit [ML problem]
• Validate that the edited code works [as before]
ICSE2023 MIP Award Talk
Passing &
failing tests
Learning/
Inference
Fault localization
Learning-based
Repair
Predict patch
Model of repair
Patch
Buggy Program Code corpus
Code
transformations

15. GETAFIX (META, 2019)
ICSE2023 MIP Award Talk
+10
+2 +35
-10 -1
-7
+1
-1
42294d 5cdd7c 1ee3fc 181d81 1d89b2 f54c2d
public int getWidth() {
@Nullable View v = this.getView();
- return v.getWidth();
+ return v != null ? v.getWidth() : 0;
}
Bader et al, Learning to fix bugs automatically, OOPSLA 2019
x == null
? x.foo()
: default
x.foo() y == null
? y.bar()
: default
y.bar()
α == null
? α.β()
: default
α.β()
Pattern discovery by anti-unification
Pattern application by probabilistic calculation

16. GETAFIX (META, 2019)
ICSE2023 MIP Award Talk
Developers are picky about their code – semantic equivalence is
not enough
Emphasis on ranking and picking the most likely pattern – no
budget to compile multiple fixes
Convenient UI integration is important

17. Where ML has helped?
Generalization in fix patterns
Productive in static analysis errors,
build errors etc.
(somewhat narrow domain, spec is
easier)
CHALLENGES IN APR
ICSE2023 MIP Award Talk
Continued challenges
Patch accuracy: tests may not
capture the full spec
Localization continues to be a
challenge

18. Private
Code
GitHub Copilot
Service
OpenAI GPT-4 Model GitHu
b
Public code and
text on the
internet
Provide Editor context
Improve Suggestions
04
Provide Suggestions
ICSE2023 MIP Award Talk
PROSPECTIVE: 2022-23
Modern LLMs trained on large code corpora have shown surprising capabilities (beyond code completion) out-of-the-
box, and many more accessible with few-shot prompting. The impact of these capabilities is significant on research
and on the profession.

19. PROGRAM REPAIR IN THE ERA OF ML-
GENERATED CODE
1. ML-generated code does not mean bugs will not appear. In production,
new unforeseen/untested conditions might occur. The need for fixing
failures is going to be there.
2. Models will improve to be more predictable as well as to avoid the more
routine kind of bugs.
3. Prompts used in code generation might themselves become the entity of
record, in which case the notion of "repair" might be applicable to prompts
too.
4. The question will remain on when ML-generated code can be
“trusted” enough to be integrated as part of your SW project!

20. 20
Steering Search Specification Inference
GRADUAL CORRECTNESS
ICSE2023 MIP Award Talk
"EVIDENCE" from REPAIR
Automated Repair of Programs from Large Language Models, ICSE23.

21. Trustworthy Software
21
TRUSTED AUTOMATED PROGRAMMING
Repair techniques on code from LLMs
Evidence generation via repair
ICSE2023 MIP Award Talk