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Gemini: Maintenance Support Environment Based on Code Clone Analysis

Gemini: Maintenance Support Environment Based on Code Clone Analysis. Yasushi Ueda*, Toshihiro Kamiya**, Shinji Kusumoto*** and Katsuro Inoue***. *Graduate School of Engineering Science, Osaka Univ. **PRESTO, Japan Science and Technology Corp.

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Gemini: Maintenance Support Environment Based on Code Clone Analysis

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  1. Gemini:Maintenance Support EnvironmentBased on Code Clone Analysis Yasushi Ueda*, Toshihiro Kamiya**,Shinji Kusumoto*** and Katsuro Inoue*** *Graduate School of Engineering Science, Osaka Univ.**PRESTO, Japan Science and Technology Corp. ***Graduate School of Information Science and Technology, Osaka Univ. y-ueda@ics.es.osaka-u.ac.jp {kamiya, kusumoto, inoue}@ist.osaka-u.ac.jp

  2. Contents • Background • Maintenance support environment, Gemini • Overview • System structure • Scatter Plot • Case Study • Conclusions

  3. clone pair clone class clone pair clone pair Background (1/2) • A code clone is a pair/set of code portions in source files that are identical or similar to each other.

  4. We have developed a code clone detection tool, CCFinder[1]. • Token-based clone detector • Its input is a set of source files and output is the locations of clone pairs. [1] T. Kamiya, S. Kusumoto, and K. Inoue, “CCFinder: A multi-linguistic token-based code clone detection system for large scale source code”, IEEE Transactions on Software Engineering, (to appear). Background (2/2) • Code clone is one of the factors that make software maintenance more difficult. • If some faults are found in a code fragment, it is necessary to correct the faults in its all clone pairs.

  5. CCFinder (1/4) • Clone detection process consists of four steps. Source files CCfinder • Target program • C / C++ • Java • FORTRAN • COBOL • LISP Lexical analysis Step 1 Token sequence Transformation Step 2 Transformed token sequence Step 3 Match detection Clones on transformed sequence Step 4 Formatting Clone pairs

  6. Source files Lexical analysis Token sequence Transformation Transformed token sequence Lexical analysis Match detection Token sequence Clones on transformed sequence Transformation Formatting Transformed token sequence Clone pairs Match detection Clones on transformed sequence Formatting CCFinder (2/4) • Example of clone detection process 1. static void foo() throws RESyntaxException { 2. String a[] = new String [] { "123,400", "abc", "orange 100" }; 3. org.apache.regexp.RE pat = new org.apache.regexp.RE("[0-9,]+"); 4. int sum = 0; 5. for (int i = 0; i < a.length; ++i) 6. if (pat.match(a[i])) 7. sum += Sample.parseNumber(pat.getParen(0)); 8. System.out.println("sum = " + sum); 9. } 10. static void goo(String [] a) throws RESyntaxException { 11. RE exp = new RE("[0-9,]+"); 12. int sum = 0; 13. for (int i = 0; i < a.length; ++i) 14. if (exp.match(a[i])) 15. sum += parseNumber(exp.getParen(0)); 16. System.out.println("sum = " + sum); 17. }

  7. Example of transformation rules in Java • All identifiers defined by user are transformed to same tokens. • Unique identifier is inserted at each end of the top-level definitions and declarations. • Prevents detecting clones that begin at the middle of class definition and end at the middle of another one. • ”java. lang. Math. PI” is transformed to ”Math. PI”. • By using import sentence, a class is referred to with either full package name or a shorter name • ” new int[] {1, 2, 3} ” is transformed to ” new int[] {$} ” • Eliminates table initialization code.

  8. Source files 1. static void foo() throws RESyntaxException { 2.String a[] = new String [] { "123,400", "abc", "orange 100" }; 3.org.apache.regexp.RE pat = new org.apache.regexp.RE("[0-9,]+"); 4. int sum = 0; 5. for (int i = 0; i < a.length; ++i) 6. if (pat.match(a[i])) 7. sum += Sample.parseNumber(pat.getParen(0)); 8. System.out.println("sum = " + sum); 9. } 10. static void goo(String [] a) throws RESyntaxException { 11. RE exp = new RE("[0-9,]+"); 12. int sum = 0; 13. for (int i = 0; i < a.length; ++i) 14. if (exp.match(a[i])) 15. sum += parseNumber(exp.getParen(0)); 16. System.out.println("sum = " + sum); 17. } Lexical analysis Token sequence Transformation Transformed token sequence Lexical analysis Lexical analysis Match detection Token sequence Token sequence Clones on transformed sequence Transformation Transformation Formatting Transformed token sequence Transformed token sequence Clone pairs Match detection Match detection Clones on transformed sequence Clones on transformed sequence Formatting Lexical analysis Formatting Token sequence Transformation Transformed token sequence Match detection Clones on transformed sequence Formatting CCFinder (2/4) • Example of clone detection process 1. static void foo() throws RESyntaxException { 2. String a[] = new String [] { "123,400", "abc", "orange 100" }; 3. org.apache.regexp.RE pat = new org.apache.regexp.RE("[0-9,]+"); 4. int sum = 0; 5. for (int i = 0; i < a.length; ++i) 6. if (pat.match(a[i])) 7. sum += Sample.parseNumber(pat.getParen(0)); 8. System.out.println("sum = " + sum); 9. } 10. static void goo(String [] a) throws RESyntaxException { 11. RE exp = new RE("[0-9,]+"); 12. int sum = 0; 13. for (int i = 0; i < a.length; ++i) 14. if (exp.match(a[i])) 15. sum += parseNumber(exp.getParen(0)); 16. System.out.println("sum = " + sum); 17. }

  9. CCFinder (3/4) • Application of CCFinder • Free software • JDK libraries (Java, 570 KLOC) • Linux, FreeBSD (C, 1.6 + 1.3 MLOC) • FreeBSD, OpenBSD，NetBSD(C) • Qt(C++，240KLOC) • Commercial software • NTT data Corp., Hitachi Ltd., NEC soft Ltd., ASTEC Inc., SRA Inc. • NASDA (Control program for rocket)

  10. Object file ID( file 0 in Group 0 ) Location of a clone pair ( Lines 53 - 63 in file 0.1 and Lines 542 - 553 in file 1.10 are identical or similar to each other) CCFinder (4/4) #version: ccfinder 3.1 #langspec: JAVA #option: -b 30,1 #option: -k + #option: -r abcdfikmnprsv #option: -c wfg #begin{file description} 0.0 52 C:\Gemini.java 0.1 94 C:\GeneralManager.java : : #end{file description} #begin{clone} 0.1 53,9 63,13 1.10 542,9 553,13 35 0.1 53,9 63,13 1.10 624,9 633,13 35 0.2 124,9 152,31 0.2 154,9 216,51 42 : : #end{clone} • Output of CCFinder • It is difficult to analyze source code by only this text-based information of the location of clone pairs.

  11. Goals of this study • Proposal of an interactive code clone analysis environment • Gemini • Case study to evaluate the proposed environment • Apply Gemini to programming exercise in our university and analyze the results.

  12. Gemini overview • A GUI-based code clone analysis environment • Uses CCFinder as a code clone detector. • Has several views to interactive analysis. • Scatter plot view • Select by mouse dragging • Sorting function • Zoom in/out • Metric graph view • Select by metric values • Source code view • Implemented in Java • About 10,000 lines of code

  13. Gemini User Interfaces Clone pair manager (CPM) Scatter plot view Clone selection information Clone pair list view CCFinder User Source code manager (SCM) Code clone detector (CCD) Source code view Source files Code clone database(CDB) Clone selection information Clone class manager (CCM) Metrics graph Clone class list view System structure of Gemini

  14. Scatter plot • Both the vertical and horizontal axes represent a token sequence of source code. • A dot means that corresponding two tokens on the two axes are same. • The main diagonal line is always drawn, since each dot on it refers to an identical position of the two axes. • A clone pair is shown as a diagonal line segment. • The distribution is symmetrical with the main diagonal line. a b c a b c a d e c a b c a b c a d e c a, b, c, ... : tokens : matched position

  15. f1 f2 f3 f4 f1 f2 f3 f4 f5 f6 f5 f6 f5 f4 f2 f1 f6 f3 f1 f6 f3 f4 f2 f5 Sorting function • When multiple files are compared in scatter plot, boundaries of their files are shown on the axes. • Depending on the file orders, the distribution of dots is spread widely. • We put similar files as near as possible.

  16. Snapshots of scatter plot

  17. DFL (C ): Estimation of how many tokens would be removed from source files when all code fragments of clone class C are replaced with caller statements of a new identical routine new sub routine caller statements Clone class metrics • LEN (C ): Length of token sequence of each element in clone class C • POP (C ): Number of elements in clone class C • RAD (C ): Distribution in the file system of elements in clone class C

  18. Aims of clone class metrics • We are interested in • Clone classes whose elements are spread widely. • High value of POP means that there are many similar code fragments. • High value of RAD means that the clones are spread over many subsystems. They are difficult to find all together in maintenance. • Clone classes which are appropriate for refactoring. • High value of DFL (high value POP and high value of LEN) means that the clone class is worth evaluating whether the elements can be merged into one routine.

  19. Snapshots of clone class metric graph LEN POP RAD DFL Filtering mode : ON

  20. Case study overview • Application target • Programs developed in a programming exercise of Osaka Univ. • Compiler in C language • Programs of 69 students • Total size is 360,000 lines of code • Issue of Analysis • Similarity among all programs • In the programming exercise, plagiarisms sometimes happen.

  21. Analysis (1/2) • Compiler of 69 students are arranged on the two axes. • The distribution is spread widely. • Rearrangement of scatter plot using sorting function • The grid represents boundary lines between individuals.

  22. B A Analysis (2/2) • The corresponding code • A (2 students) • Similar code fragments were from source code of sample compiler described in textbook. • B (4 students) • Many code fragments were similar even with respect to name of variables or comments.

  23. Conclusions • We presented a maintenance support environment based on code clone analysis, Gemini. • We also applied it to programming exercise to evaluate its usefulness. We are going to evaluate the applicability of Gemini to large scale software in actual software maintenance as future research work.

  24. Suffix-tree • Suffix tree is a tree that satisfies the following conditions. • A leaf node represents the starting position of sub-string. • A path from root node to a leaf node represents a sub-string. • First characters of labels of all the edges from one node are different from each other. → A common path means a clone

  25. new sub routine caller statements Definition of DFL and RAD • DFL(C ) • DFL(C) = LEN(C) ×POP(C) - 5×POP(C) + LEN(C) • LEN(C) ×POP(C) : the target code size for restructuring • 5×POP(C) : the code size of new caller statements • LEN(C) : the code size of new identical routine • RAD (C ) • Distribution in the file system of elements in clone class C • RAD(C) = 0 : C is enclosed within a single file. • RAD(C) = 1 : C is enclosed within a single directory. • RAD(C) = n : C is enclosed within a directory tree of n layers.

  26. f1 f2 f3 f4 f1 f2 f3 f4 f5 f6 f5 Step2:From among the remaining files, select the most similar file to F and put it next toF by the value of RST f6 RST(i,j) : Ratio of covered code range in file i by clones between a file i and a file j f1 f5 f4 f4 f2 f1 f1 f1 f1 f6 f6 f6 f6 f3 f3 f3 f1 f1 f1 f1 f1 f6 f6 f6 f6 f3 f3 f3 f4 f4 f2 f5 Sorting function Step1:Select a head file by the value of RSA(Make F the head file) RSA(i) : Ratio of covered code range in file i by clones between one file iof other files Step3:Repeat step2 recursively while any file remains, treating the most similar file in previous step2 as new F

  27. Analysis - reuse of programs (1/3) • RST(Parser,Checker) and RST(Checker,SPC) of each student were used as ratio of reused code.

  28. Parser Checker SPC Parser Checker SPC C D Analysis - reuse of programs (2/3) • The average of RST of S1 isthe lowest. • C : between Parser and Checker • D : between Checker and Parser • Minimum length of clone to be detected was changed to 15 tokens.

  29. S3 S2 Parser Checker SPC Parser Checker SPC Parser Checker SPC Parser Checker SPC Analysis - reuse of programs (3/3) • The highest average value of RST • S2 : 0.549, S3 : 0.701 • Different appearances in scatter plot

  30. S10 S9 Parser Checker SPC Parser Checker SPC C Parser Checker SPC Parser Checker SPC D E Analysis - Usefulness of metric graph • Verified the value of DFL from metrics graph • DFL(C) = (LEN(C) ×POP(C))– (LEN (C) + 5×POP(C)) • S9 ：The value of DFL(Parser) was very high • S10 ：The value of DFL(SPC) was very high The highest values of DFL in each program

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