1. Speaker: Liu Shuchang Osaka University
Extraction of Evolution History from Software Source Code Using Linear Counting
Speaker: Liu Shuchang
Osaka University 1

2. Background daily software development copy existing code
product variant
copy
edit
software evolution 2

3. Evolution History Example
only source code ❌ 3

4. Introduction Evolution History Recovery product variants
using only source code
Evolution Tree
vertex: variant
edge: derived relation (most similar pair)
key: product similarity
Previous Study
diff based (file-to-file similarity)
time needed (worst case: 2 days)
Linear Counting Algorithm
estimating instead of calculating 4

5. Linear Counting Algorithm
Cardinality: 11
Zero: 2
Bitmap Size: 8
-8 × ln(2/8) =
An example of the Linear Counting Algorithm 5

6. Estimate Product Similarity
Multiset A
Bit Map A
Bit Map
A∩B
hash function
bitwise operator
Initialization
Multiset B
Bit Map B
Bit Map
A∪B
hash function
Similarity: Jaccard Index
|A∩B|
———
|A∪B|
LC(A∩B)
continued
division
LC(A∪B) 6

7. Process Flow Variant A (Source Code) Initial Multiset A Initialization
1. n-gram modeling
Jaccard Index
2. each line of the code
|A∩B|
———
|A∪B|
Linear Counting Algorithm
Variant B
(Source
Code)
Initial
Multiset B
Initialization
(A, B), (A, C), (A, D), …
Evolution Tree
the most similar pair
Prim’s Algorithm 7

8. Research Data
A description of datasets we dealt with 8

9. Final Result of dataset5
The Evolution Tree we extracted
(the Best Configuration)
Existing actual evolution history 9

10. Analysis on Bitmap Size
Part of the experiment results of dataset5 10

11. Best Configuration Main Factors N-gram Modeling no (each line of code)
Bitmap Size
128,000,000 bits
Hashing Function
MurmurHash3
Results
Proper Edges
86.5% (on average)
Time
10s to 5mins 11

12. Contributions and Future Work
extract an ideal Evolution Tree efficiently
influence of various factors
best configuration
faster and showed better accuracy
Future Work
larger datasets
other programming language
solve the remaining problems 12