TMPA-2017: Evolutionary Algorithms in Test Generation for digital systems

TMPA-2017: Tools and Methods of Program Analysis
3-4 March, 2017, Hotel Holiday Inn Moscow Vinogradovo, Moscow

Evolutionary Algorithms in Test Generation for digital systems
Yuriy Skobtsov, Vadim Skobtsov, St.Petersburg Polytechnic University

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1. Evolutionary Algorithms in Test Generation for digital systems Y.A.Skobtsov,V.Y.Skobtsov
2. 2 Outlines • Evolutionary computation • Classical GA • Test generation for combinational circuits • Test generation for sequential circuits • Fitness functions • Genetic programming based test generation of microprocessors systems • Conclusion
3. 3 Evolutionary computation Basic paradigms: ❖ Genetic algorithms ❖ Genetic programming ❖ Evolutionary strategies All basic paradigms can be applied in ATPG problem solving
4. 4 A flowchart of classical GA Initial population generation Selecting parents for crossover operator – reproduction operator Creating offsprings of selected pairs of parents – crossover operator Mutation of created individuals – mutation operator Insertion of new individuals to the current population Reducing the number of individuals to given number in new population by application of reproduction operator Search of the best individual in final population Final criterion checking – Yes/No? Yes No
5. 5 Evolutionary test generation For solving any problem with genetic algorithm we must define: 1) individual and population; 2) genetic operators; 3) fitness function. Genetic Algorithm Population, individual Genetic operators Fitness function & EWDTW'06 , Sochi, Russia, September 15-19, 2006
6. 6 Test generation for combinational circuits Definition of fault detection: Pattern of input variables values X is test for given fault if and only if , where f(X) is Boolean function of good circuit, (X) is Boolean function of faulty circuit. In the case of multiple outputs definition of fault detection can be generalized in following manner 1 ) ( ) ( ) (    X X f X D  1 ) ( ) ( ( )) ( ) ( ( )) ( ) ( ( ) ( 2 2 1 1        X X f X X f X X f X D m m   
7. Combinational circuits 7 x4 x5 x1 x2 x3 x6 Table 13.1. X1 X2 X3 X4 X5 X6 0 0 0 1 1 1 0 0 1 1 1 1 0 1 0 1 1 1 0 1 1 1 0 0 1 0 0 1 1 1 1 0 1 1 0 0 1 1 0 0 1 0 1 1 1 0 1 0 Table 13.2 Input vector (x1 , x2 , x3 ,) Detected faults Fitness function value h= Fn *r 000 x4 = 0, x5 =0, x6 =0 310 011 x2 =0, x3 =0, x5 =1, x6 =1 410 101 x2 =1, x4 =0, x5 =0, x6 =0 410 111 x6 =1 110
8. 8 Test Generation Genetic Algorithm Test generation(circuit) { Circuit initialization; Initial vectors population generation; While(stopping criteria met) { fault simulation; fitness function evaluation; insertion the best vector in test; genetic operators execution; reproduction; crossover; mutation; new population generation; } test sequence output; }
9. 9 Individuals encoding & crossover schemas for: a) combinational circuits test generation: b) sequential circuits test generation: b) population Mutation schemas: a) deleting one randomly located input pattern b) adding one input pattern to random position c) random bit inversion in test sequence
10. 10 Fitness functions : - the weighted number of the lines with different values in fault and fault-free circuits; ) , ( 1 f v f - the weighted number of flip-flops with different values in fault and fault-free circuits; ) , ( 2 f v f - fitness function for test input vector v ) , ( 2 1 ) , ( 1 ) , ( f v f c f v f f v h    flops flip of number lines of number c   _ _ _ _ 1 - normalization constant ) , ( 1 ) , ( f i v h length i i L f s H     - fitness function for test input sequence
11. 11 Test Generation Genetic Algorithm test_generation(circuit) { fault_list_generation(); while(given_fault_coverage_not_reached) { object=activate_the_object_fault(); // Stage 1 if(object == NO_FAULT ) goto end; sequence=GA:_test_sequence_generation(object)// Stage 2 if(sequence!= NO_SEQUENCE ) fault_simulation( sequence ); // Stage 3 else // can’t find the test sequence mark_object_fault_as_not_detectible(); } // end while – given fault coverage reached end: } // end of the algorithm EWDTW'06 , Sochi, Russia, September 15-19, 2006
12. 12 Genetic programming based test generation of microprocessors systems MP-system is checked with the test-program consisting of Assembler instructions: •Individual – Assembler test-program •Population – test-program set Individual is represented by the directed acyclic graph (DAG) :
13. 13 Genetic programming based test generation of microprocessors systems ❖ Evolutionary ( + ) strategy, where  - population power,  - offspring number. ❖ Tournament selection is applied. ❖ Genetic operators: mutation – a new node is inserted into the DAG in a random position; – an existing internal node is removed from DAG; – modifying parameters of an existing internal node; crossover – 2 different test-programs produce the 2 offspring by classical 1- point crossover. At first, parents are analyzed to detect potential cutting points, i.e., DAG nodes which create disjoint sub-graphs when are removed.
14. 14 There are also the following special nodes in the program: ➢Start node ➢Stop node ➢Subprogram calling nodes ➢Library subprogram calling nodes There are also parameters tuning, for example, the minimum and the maximum time for a program to run. The instruction library describes the assembly syntax, listing each possible instructions with the syntactically correct operands. Test programs are implemented by modifying directed graph topology and by crossover and mutation parameters inside directed graph nodes. A population of  individuals is cultivated, each individual representing a test program. In each step , an offspring of  new individuals are generated. Parents are selected using tournament selection with tournament size  (i.e.,  individuals are randomly selected and the best is picked ). Each new individual is generated by applying three mutation and crossover operators are implemented and applied with own probabilities respectively.
15. 15 Mutation: Mutation 1 (Add node): a new node is inserted into the directed graph in a random position. Mutation 2 (Remove node): an existing internal node (except start or end ) is removed from the directed graph . If the removed node was the target of one or more branch, parents’ edges are updated. Mutation 3 (Modify node): all parameters of an existing internal node are randomly changed. Crossover: Two different programs are mated to generated a new one. First, parents are analyzed to detect potential cutting points, i.e., vertices in the directed graph that if removed create disjoint subgraphs Then a SAG 1-point crossover is exploited to generate the offspring Evolutionary operations
16. 16 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 Crossover Point Selected Sub-graph Parent 1 Parent 2 1 1 1 1 1 1 Crossover Point 2 2 2 2 2 1 Offspring 2 Crossover Point 2 2 2 1 1 2 2 2 2 2 Offspring 1 Crossover Point Selected Sub-graph
17. 17 Fitness-function of the second level is build on the basis of coverage measure of VHDL operators. Thus fitness-function exploits the data obtained by means of Active VHDL (code coverage). During construction of tests for microprocessor system is used the following fitness-function: , where: Nao –the number of linear statements VHDL have been activated by test- program Nab –the number if statements have been activated by test program Naс – the number of case statements have been activated by test program No , Nb , Nc the common number of linear, if ,case statements accordingly; co , cb , cc -normalizing constants (co + cb + cc =1). c ac b b ab b o ao o N N c N N c N N c F   
18. 18 generation of test-program initial population; While (not attained maximum number of generation) { // loop according to generations Generation of various paths for each test program; While (not attained stop condition ) //loop according to paths { Test-program generation according to correspondent path Compilation of test-program to binary code Entry to Active VHDL environment Loading of binary code to ROM of microprocessor system VHDL model Estimation of test program coverage using Active VHDL Exit from Active VHDL environment; Calculation of fitness-function according to correspondent path; } // end of loop according to paths Calculation of fitness function for test-program (graph); //creation of the next generation; Selection of parents according to fitness-function value; Crossover; Mutation; Reduction of population; } //end of loop according to generations The algorithm of test program generation
19. 19 Fitness function MP-systems test generation F = Na + Nf * Nm + (Nf )2 *Nd , where Nf – total fault number, Na – the number of excited faults, Nm – the number of faults that modify memory, Nd – the number of detected faults.
20. 20 Conclusion Evolutionary calculations could be effectively applied to test generation at all DS representation levels: 1. Structural (gate) level: ❖ combinational logical circuits; ❖ sequential logical circuits; 2. FSM and structural levels: ❖ highly sequential circuits ; 3. Microprocessor systems.
21. 21 Thanks for your attention! You can write to the authors by e-mails: •Yuriy A. Skobtsov: ya_skobtsov@list.ru •Vadim Yu. Skobtsov: vasko_vasko@mail.ru