Repair & Refactoring

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1 S C I E N C E P A S S I O N T E C H N O L O G Y Repair & Refactoring Birgit Hofer Institute for Software Technology 1 u

2 Outline Model-based Software Debugging o Traffic Light Example Repair o Genetic programming o Software (C, Java) o Spreadsheets Refactoring Exam 2

3 Outline Model-based Software Debugging o Traffic Light Example Repair o Genetic programming o Software (C, Java) o Spreadsheets Refactoring Exam 3

4 Traffic Light Example 1. public static void test () { 2. TrafficLight tl = new TrafficLight(0); // initializes tl.state=0; 3. int i = 0; 4. int finalstate; 5. while (i < 5) { 6. tl.printstate(); 7.

4 4 Traffic Light Example 1. public static void test () { 2. TrafficLight tl = new TrafficLight(0); // initializes tl.state=0; 3. int i = 0; 4. int finalstate; 5. while (i < 5) { 6. tl.printstate(); 7. if (tl.state == 0) { 8. tl.state = 1; 9. } else { 10. if (tl.state == 1) { 11. tl.state = 2; 12. } else { 13. if (tl.state == 2) { 14. tl.state = 3; 15. } else { 16. tl.state = 3; // should be tl.state = 0; 17. } 18. } 19. } 20. i++; 21. } 22. tl.printstate(); 23. finalstate = tl.state; 24. }

5 Traffic Light Example Hints for the solution #FACTS eq(not_intrace_0,0) # initialization eq(finalstate_0,1) # expected output value eq(state_0,0) # input Intrace variables WHILEs are control-dependent of the previous WHILE The IF in Line 7 is control-dependent on the previous WHILE Each other IF is control-dependent on the previous IF. Lines 8/11/12/16 are control-dependent on the previous IF Line 20 is control-dependent on the previous WHILE Line 23 is control-dependent on intrace_0. 5

6 Traffic Light Example How many single fault solutions do you get? Do you get a solution if you set the watchsumgeq/leg of the AB variables to 0? Would it help if you add more facts to the constraints system, e.g. state_2=2? 6

7 Outline Model-based Software Debugging o Traffic Light Example Repair o Genetic programming o Software (C, Java) o Spreadsheets Refactoring Exam 7

8 Visualization Design & Maintenance Support Static Analysis Modeling Spreadsheet Quality Assurance Techniques Debugging Fault localization Spectrumbased Model-based Testing Repair Genetic Source: Jannach et al. Avoiding, Finding and Fixing Spreadsheet Errors A Survey of Automated Approaches for Spreadsheet QA, in Journal of Systems and Software, Visualization: Patrick Koch,Diploma Seminar, TU Graz,

9 Genetic Programming Evolutionary methodology to find computer programs that perform a user-defined task 9

10 Principles of Genetic Programming (GP) Evolution Mutation Cross-over Survival of the fittest Fitness function, e.g., test cases 10

11 Program Representation in GP + - * 8 / y - x 2 8 x / 2 + y * (-3) 3 11

12 Mutation * 8 / y - x x / 2 + y * (-3) 3 12

13 Crossover * 8 / y - x x / 2 + y * x (-3) / 2 13

14 Crossover 2 Mutant 1 Mutant / x 3 x 2 14

15 Crossover 3 Crossing back Original program - Mutant - 8 / x 3 x 2 15

16 Genetic Programming - Workflow 16 Source: Patrick W. Koch: Framework for Automated Spreadsheet Debugging conducting Evolutionary Strategies, Master-Project, 2013.

17 Outline Model-based Software Debugging o Traffic Light Example Repair o Genetic programming o Software (C, Java) o Spreadsheets Refactoring Exam 17

(Fitness) Elimination Copy Copy 1 Copy 2 Mutant 1 n Copy 1 Mutant n 18

18 Genetic Programming For Automated Software Repair Faulty program AST (abstract syntax tree) Copy Copy Copy 1 Copy 2 1 n Test cases Evaluation (Fitness) Elimination Copy Copy 1 Copy 2 Mutant 1 n Copy 1 Mutant n 18 Source: Forrest et al.: A Genetic Programming Approach to Automated Software Repair, GECCO, 2009.

Elimination Copy Copy 1 Copy 2 Mutant 1 n Copy Mutant 1 n Minimized Solution 19

19 Genetic Programming For Automated Software Repair Faulty program AST (abstract syntax tree) Copy Copy Copy 1 Copy 2 1 n Test cases Evaluation (Fitness) Elimination Copy Copy 1 Copy 2 Mutant 1 n Copy Mutant 1 n Minimized Solution 19 Source: Forrest et al.: A Genetic Programming Approach to Automated Software Repair, GECCO, 2009.

20 Smart statement selection for mutation Weighted path Statement visited by Negative TC 1.0 Positive and negative TC 0.1 Positive TC 0.0 Finer-grained selection possible Do you have ideas for a finer selection? 20

21 Mutation operators Delete statement Insert statement Swap statements 21

22 GCD Example Test case gcc(0,55) 22 Source: Weimer et al.: automatically finding patches using genetic programming, ICSE, 2009.

23 GCD Example Program passes test case We need more test cases Test case gcc(0,55) gcc(1071,1029) 23 Source: Weimer et al.: automatically finding patches using genetic programming, ICSE, 2009.

24 Test Cases and Fitness Function Negative Test Cases Characterize the fault Positive Test Cases Encode the functionality requirements Fitness Function The more passing test cases - the better the score All test cases pass repair found 24

25 GCD Example Primary Repair Test case gcc(0,55) gcc(1071,1029) 25 Source: Weimer et al.: automatically finding patches using genetic programming, ICSE, 2009.

26 The Zune Bug Test cases 26 Source: Forrest et al.: A Genetic Programming Approach to Automated Software Repair, GECCO, 2009.

27 The Zune Bug Possible Mutations 27

28 The Zune Bug Solution 28

29 ASTOR Automatic Software Transformation for program Repair Tool for automatically repairing Java programs Under GPL v2 (GNU General Public License) Uses Spoon (library for Java code analysis and manipulation) 29 Source: Martinez and Monperrrus: ASTOR - Evolutionary Automatic Software Repair for Java, Technical Report hal , Inria, 2014

30 Outline Model-based Software Debugging o Traffic Light Example Repair o Genetic programming o Software (C, Java) o Spreadsheets Refactoring Exam 30

31 GP for Spreadsheets Mutations Constants Change Boolean values Permutate digits Increase / decrease number by 1 Use a random number Change sign References Increase/decrease the borders of areas Change a single reference Formulas Replace a binary operator with another binary operator A1+A2 A1-A2 Replace a formula with another formula which can process the same arguments SUM(A1:A4) AVG(A1:A4) Remove parts of a formula A3+A4*3 A3*3 Relocate parts of a formula if(a1>1;a2;a3) if(a1>1;a3;a2) 31

32 Genetic Programming for Spreadsheet Repair I 32

33 Genetic Programming for Spreadsheet Repair II 33

34 Genetic Programming for Spreadsheets 34

35 Outline Model-based Software Debugging o Traffic Light Example Repair o Genetic programming Refactoring Exam 35

36 Refactoring Process of changing the internal structure of a program without changing the functionality 36

37 How does refactoring contribute to the quality of spreadsheets? 37

38 BumbleBee f1 f2 SUM(R)/COUNT(R) AVERAGE(R) Parameterized references via variables A1+A2+A3 SUM(A1:A3) {i,j} + {i,j+1} SUM({i,j}:{j,j+1}) Areas {i,j}+ +{m,n} SUM({i,j}:{m,n}) Source: Hermans and Dig: BumbleBee: A Refactoring Environment for Spreadsheet Formulas, FSE

39 TableProg 39 Source: Harris and Gulwani: Spreadsheet Table Transformations from Examples, PLDI, 2011.

40 TableProg 40 Source: Harris and Gulwani: Spreadsheet Table Transformations from Examples, PLDI, 2011.

41 TableProg 41 Source: Harris and Gulwani: Spreadsheet Table Transformations from Examples, PLDI, 2011.

42 Outline Model-based Software Debugging o Traffic Light Example Repair o Genetic programming o Software (C, Java) o Spreadsheets Refactoring Exam 42

43 Exam General List Fault Types List Quality Assurance Techniques (+ subcategories) Explain R1C1 cell reference system Static Code Analysis techniques Explain the terms Code Smells and Refactoring List, explain and identity Software/Spreadsheet Code Smells List and explain Static Spreadsheet Analysis Techniques (UCheck, ) Spectrum-based fault localization Explain and apply SFL to software snippets and spreadsheets Explain the terms o Observation matrix o Error vector o Similarity coefficient o Best/worst/average case ranking o Cone 43

44 Exam Model-based fault localization List + explain all parts of diagnosis problem Explain the terms o Conflict / Conflict set o Diagnosis / Minimal diagnosis o Hitting Set / Minimal hitting set o SSA form Solve simple examples via conflicts and hitting sets (hardware, simple software) Write (in pseudo code) a dependency-/value-based model for a given spreadsheet Explain how to compute double fault diagnoses List, explain and compare (+/-) the different types of behavior models Explain how to derive a dependency-based model from a value-based model (+ apply by means of an example) Explain coincidental correctness + list examples Explain how MBSD for Software works Compare SFL with MBSD w.r.t. complexity, granularity, input, result 44

45 Exam Automatic repair Explain the terms o Genetic Programming o Mutation (+ list different types) o Crossover (+ list / explain different types) o Fitness function Explain how automatic repair with genetic programming works for software/spreadsheets 45

46 Haven t you had enough of spreadsheets yet? Master Project Diploma Thesis 46

47 Thank you for participating in this lecture. Are there any suggestions/wishes for changes/improvements? 47

Model-based Software Debugging. Part III

Model-based Software Debugging. Part III S C I E N C E P A S S I O N T E C H N O L O G Y Part III Birgit Hofer Institute for Software Technology 60 u www.tugraz.at Outline Model-based debugging for Spreadsheets o Dependency-based models Model-based