Given a portfolio of algorithms, the goal of Algorithm Selection (AS) is to select the best algorithm(s) for a new, unseen problem instance. Dynamic Symbolic Execution (DSE) brings together concrete and symbolic execution to maximise the program coverage. DSE uses a constraint solver to solve the path conditions and generate new inputs to explore. In this paper we join these lines of research by introducing a model that combines DSE and AS approaches. The proposed AS/DSE model is a generic and flexible framework enabling the DSE engine to solve the path conditions it collects with a portfolio of different solvers, by exploiting and extending the well-known AS techniques that have been developed over the last decade. In this way, one can increase the coverage and sometimes even outperform the aggregate coverage achievable by running simultaneously all the solvers of the portfolio.
Algorithm Selection for Dynamic Symbolic Execution: A Preliminary Study / Amadini R.; Gange G.; Schachte P.; Sondergaard H.; Stuckey P.J.. - ELETTRONICO. - 12561:(2021), pp. 192-209. (Intervento presentato al convegno 30th International Conference on Logic-Based Program Synthesis and Transformation, LOPSTR 2020 tenutosi a ita nel 2020) [10.1007/978-3-030-68446-4_10].
Algorithm Selection for Dynamic Symbolic Execution: A Preliminary Study
Amadini R.
Primo
;
2021
Abstract
Given a portfolio of algorithms, the goal of Algorithm Selection (AS) is to select the best algorithm(s) for a new, unseen problem instance. Dynamic Symbolic Execution (DSE) brings together concrete and symbolic execution to maximise the program coverage. DSE uses a constraint solver to solve the path conditions and generate new inputs to explore. In this paper we join these lines of research by introducing a model that combines DSE and AS approaches. The proposed AS/DSE model is a generic and flexible framework enabling the DSE engine to solve the path conditions it collects with a portfolio of different solvers, by exploiting and extending the well-known AS techniques that have been developed over the last decade. In this way, one can increase the coverage and sometimes even outperform the aggregate coverage achievable by running simultaneously all the solvers of the portfolio.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
