Etg open problem 2 improvement of etg by static analysis
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ETG open problem #2 Improvement of ETG by static analysis. H. Schlingloff, A. Baars, Y. Hassoun, M. Leucker. Static Analysis in ETG. EvoTest WP4: removing irrelevant variables from evolutionary search by variable dependence analysis

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ETG open problem #2 Improvement of ETG by static analysis

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Etg open problem 2 improvement of etg by static analysis

ETG open problem #2Improvement of ETG by static analysis

H. Schlingloff, A. Baars,Y. Hassoun, M. Leucker


Static analysis in etg

Static Analysis in ETG

  • EvoTest WP4: removing irrelevant variables from evolutionary search by variable dependence analysis

    • SUT is a C function, test case is tupel of input parameters, objective is some coverage (e.g. branch)

    • a variable is irrelevant for a particular goal if its value does not contribute to the goal

    • this property can be (partially) decided by static analysis

    • if a variable is irrelevant, it is not included in the genome. This reduces the search space

  • This technique is applicable whenever it can be statically determined whether a certain variable (gene) contributes to the fitness of the whole individual (genome) or not

    • does this really reduce execution time???


Static analysis 1

Static Analysis (1)

  • The static analysis could improve the parameters of a genetic algorithm (like cross-over operators, fitness function etc.) rather than just limiting the search space

    • example:f(x,y,s,t) = if (x+y>10 and s+t>20) then goal: …;

    • crossover that changes x or y when their sum satisfies the condition might be counterproductive for the search in the sense that prevents reaching the solution quickly.

      • This information could be used when deciding upon a rep, in order to reduce the probability that these crossovers can happen

    • abstract interpretation and equivalence partitioning can define the range of input variables and hence limit the search space

      • This can easily be incorporated into the search

    • symbolic execution can determine path conditions

      • How can this be incorporated into the search??


Static analysis 2

Static Analysis (2)

  • more generally

    • functional dependencies between variables can be statically analyzed

      • How can this be incorporated into the search??

      • ask

    • this seems to be a generalization of the previous result (how do parts of the genome influence the fitness of the individual) and should be investigated in more detail

  • Might be useful to determine the order of variables in the genome (in the genetic diversity computation)

    • Bayesian estimation?

    • Learning of dependencies?

  • Might be useful to optimize the order in which goals are targeted

    • dominating and dominated block analysis together with set cover


Human interaction in etg

Human Interaction in ETG

  • Human interaction (assertions) can help in static analysis; thus, obviously, it can help in ETG

  • Seeding can be done manually

  • More generally, humans can choose and dynamically alternate some parameters of the genetic algorithm (e.g. crossover operators, mutation rates, …)

  • This could be coupled to the symbolic execution (to be investigated)


Model checking and etg

Model Checking and ETG

  • ETG might help MC

  • it is not clear how MC might help ETG

    • prove certain goals to be unreachable?

    • prove abstraction transformations?


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