1. Precise Condition Synthesis for Program Repair
Y. Xiong at el. International Conference on Software Engineering Presentor : Jee-weon Jung

2. Index
Overview
Introduction
Motivating example
Approach
Evaluation

3. Overview Object : produce PRECISE patches
Concentrate on condition synthesis
Study three approaches and finally propose ACS
Know what variables should exist in a ‘if’ condition  use sorting method based on dependency relations between variables
Propose document analysis technique on API documents  filter variables
Mine a set of frequently used predicates
Successfully repaired 18 defects on 4 projects of Defects4J
Best performance reported, precision was 78.3% (others usually 40%)

4. Introduction (1)
Automatic program repair techniques generate patch that match specification
Test suite is mostly used as specification
Patch is considered ‘plausible’ if it passes all tests
Concept of precision is adtopted in plausible patches
Precision is VERY important
Low precision patches decrease efficiency
Precision of well known programs such as GenProg is about 4%
Reason : precise patch is sparse in the space of repair systems

5. Introduction (2) Rank patches to achieve high precision
Studied widely (Prophet, HistoricalFix, DirectFix, …)
This paper proposes more accurate ranking criteria for condition synthesis
Condition synthesis : insert / modify ‘if’ condition
One of most strongest techniques
Proposed method decompose condition synthesis
Variable selection : decide which variable to use
Predicate selection : decide what to do

6. Introduction (3)
Example : ‘if (a > 10)’
First select variable ‘a’  select predicate ‘>10’
Use proposed 3 techniques for ranking variables and predicates
Dependency-based ordering : more recent dependency showing variable is more likely to be used in condition
Document analysis : analyze Javadoc comments in the src code
Predicate mining : extract from existing projects  sort by frequency

7. Motivating example (1) Math99 defect in Defects4J dataset
2 test cases given
‘a=1,b=50’ / ‘a=Integer.MIN_VALUE,b=1’
Many plausible conditions exist  most are not precise
Proposed ACS addes lines 2~4
Synthesize the condition at line 2

8. Motivating example (2) Dependency-based ordering Document Analysis
In the previous example: ‘lcm’ depends on ‘a’ and ‘b’  ‘lcm’ is more likely to be used
Use dependency in ordering
Document Analysis
Mnay java methods come with Javadoc comments  assist condition synthesis using comments
Consider tag in the document
Analyze the subject of sentence
Consider only the mentioned variables when making guard condition

9. Motivating example (3) Predicate Mining
Use variable type, name as context
Ex : ‘hour’ frequently used with >24, <12, …
Ex : ‘factorial’ frequently used with <21 (20! Is the largest factorial representable with 64bit integer

10. Approach (1) - overview Use 2 types of templates
Directly return the oracle
Identify the last executed statement s in the failed test  insert one of if (c) return v / if (c) throw e before the statement
Modification of an existing condition
Locate potential faulty ‘if’ condition c  apply either widening (c  c || c) / narrowing (c  c && c)
c : synthesized condition
Use SBFL & predicate switching for locating potential faulty condition

11. Approach (2) – extracting the oracle
Three types
Constant : directly copy
Specified via XXXException.class annotation : throw an instance
Function mapping the test input to the output : complicated.. First perform backward slicing from the oracle expression perform backward slicing from the test input arguments

12. Approach (3) – variable ranking
3 steps : preparing candidate  filtering by document analysis  sort using dependency-based ordering
Preparing candidate variable
Consider 4 types : local variable / method parameter / ‘this’ pointer / expressions used in other ‘if’ conditions in the current method
Sorting by dependency
Create a dependency graph between variables
Node : variable / dege : dependency relation

13. Approach (4) – predicate ranking
Mining related conditions
Use variable type, name, and method name to decide context
Two word is considered similar when word decomposition by capital letter has common word
Variable name is considered meaningful when length >=2
Synthesize a condition c with variable x in the method m
A conditional expression c0 is considered to be in a similar context of c, if (1) it contains one variable x0, (2) x0 has the same type as x, (3) the name of x0 is similar to x when the name of x is meaningful, or the name of the method surrounding c0 is similar to m when the name of x is not meaningful.

14. Approach (5) – counting predicates
Using collected similar expressions, extracted used predicates
Choose among only predefined predicates only
Expanding search space often leads to incorrect patches
Syntactically differenct predicates may semantically be the same
Apply ‘pred’ function to extract multiset  sort by frequencies  heuristically select top 20

15. Evaluation (1) Implementation Dataset
Based on the source ocde of Nopol
Use fault localization library Gzoltar
Apache OpenNLP is exploited
Dataset
Top five most starred Java projects on GitHub (2016/7/15)
Four projects from Defects4J

16. Evaluation (2) Research questions
RQ1: how do the three ranking techniques perform on ranking variables and predicates?
RQ2: how does our approach perform on real world defects?
RQ3: how does our approach compare with existing approaches?
RQ4: to what extent does each component of our approach contribute to the overall performance?

17. Evaluation (3)
RQ1: performance of the three techniques

18. Evaluation (4)
RQ1: performance of the three techniques

19. Evaluation (5)
RQ1: performance of the three techniques

20. Evaluation (6)
RQ2: performance of ACS

21. Evaluation (7)
RQ3: comparison with existing approaches

22. Evaluation (8)
RQ4: detailed analysis of the components

23. Conclusion Study refined ranking techniques for condition synthesis
Achieve high precision and reasonable recall on Defects4J