A system comprising: one or more processors; and a memory comprising instructions which, when executed by the one or more processors, cause the one or more processors to perform operations comprising: accessing an input file and code for testing, the input file comprising a plurality of bytes; performing a plurality of runs of a fuzzing algorithm using the input file and the code for testing, each run including: performing a mutation of one or more bytes of the input file and determining which parts of the code for testing were executed when the code was run with the mutated input file; storing, for each run of the plurality of runs of the fuzzing algorithm, an indication of whether the mutation caused execution of a portion of the code for testing which was not executed prior to the mutation; tailoring the fuzzing algorithm based on the stored indications; executing the tailored fuzzing algorithm to identify one or more versions of the input file for which the code for testing crashes; providing, as a digital transmission, indicia of the one or more versions of the input file for which the code for testing crashes; and generating a heatmap of the input file based on the stored indications, the heatmap mapping each of the plurality of bytes in the input file to a value indicating whether the mutation of the byte caused execution of the portion of the code for testing which was not executed prior to the mutation, wherein tailoring the fuzzing algorithm based on the stored indications comprises tailoring the fuzzing algorithm based on the heatmap; wherein generating a heatmap of the input file based on the stored indications comprises: learning a machine-learned model based on the stored indications, the machine-learned model being for predicting the value indicating whether the mutation of each byte causes execution of the portion of the code for testing which was not executed prior to the mutation, said machine-learned model trained in a supervised setting; and generating the heatmap of the input file using the machine-learned model. one or more processors; and a memory comprising instructions which, when executed by the one or more processors, cause the one or more processors to perform operations comprising: accessing an input file and code for testing, the input file comprising a plurality of bytes; performing a plurality of runs of a fuzzing algorithm using the input file and the code for testing, each run including: performing a mutation of one or more bytes of the input file and determining which parts of the code for testing were executed when the code was run with the mutated input file; storing, for each run of the plurality of runs of the fuzzing algorithm, an indication of whether the mutation caused execution of a portion of the code for testing which was not executed prior to the mutation; tailoring the fuzzing algorithm based on the stored indications; executing the tailored fuzzing algorithm to identify one or more versions of the input file for which the code for testing crashes; providing, as a digital transmission, indicia of the one or more versions of the input file for which the code for testing crashes; and accessing an input file and code for testing, the input file comprising a plurality of bytes; performing a plurality of runs of a fuzzing algorithm using the input file and the code for testing, each run including: performing a mutation of one or more bytes of the input file and determining which parts of the code for testing were executed when the code was run with the mutated input file; storing, for each run of the plurality of runs of the fuzzing algorithm, an indication of whether the mutation caused execution of a portion of the code for testing which was not executed prior to the mutation; tailoring the fuzzing algorithm based on the stored indications; executing the tailored fuzzing algorithm to identify one or more versions of the input file for which the code for testing crashes; providing, as a digital transmission, indicia of the one or more versions of the input file for which the code for testing crashes; and generating a heatmap of the input file based on the stored indications, the heatmap mapping each of the plurality of bytes in the input file to a value indicating whether the mutation of the byte caused execution of the portion of the code for testing which was not executed prior to the mutation, wherein tailoring the fuzzing algorithm based on the stored indications comprises tailoring the fuzzing algorithm based on the heatmap; wherein generating a heatmap of the input file based on the stored indications comprises: learning a machine-learned model based on the stored indications, the machine-learned model being for predicting the value indicating whether the mutation of each byte causes execution of the portion of the code for testing which was not executed prior to the mutation, said machine-learned model trained in a supervised setting; and generating the heatmap of the input file using the machine-learned model. learning a machine-learned model based on the stored indications, the machine-learned model being for predicting the value indicating whether the mutation of each byte causes execution of the portion of the code for testing which was not executed prior to the mutation, said machine-learned model trained in a supervised setting; and generating the heatmap of the input file using the machine-learned model.