Device and method for automatically repairing memory deallocation errors

    公开(公告)号:US10922209B2

    公开(公告)日:2021-02-16

    申请号:US16421097

    申请日:2019-05-23

    Abstract: A device for automatically repairing memory deallocation errors is disclosed. The device includes: a static analysis unit configured to generate status information for each one of the objects included in the source code of a program by way of a static analysis of the source code, where the status information includes position information, pointer information, and patch information, the position information associated with allocation sites of the objects, the pointer information associated with pointers pointing to the objects, the patch information associated with deallocation statements capable of deallocating the objects; a decision unit configured to choose patch candidates from the patch information and decide on a combination of the patch candidates capable of deallocating each of the objects only once; and a repair unit configured to repair the source code according to the combination of patch candidates.

    METHOD FOR AUTOMATICALLY GENERATING SEARCH HEURISTICS AND PERFORMING METHOD OF CONCOLIC TESTING USING AUTOMATICALLY GENERATED SEARCH HEURISTICS

    公开(公告)号:US20190258566A1

    公开(公告)日:2019-08-22

    申请号:US15985899

    申请日:2018-05-22

    Abstract: A method of generating a search heuristic for concolic testing includes: (a) generating feature vectors corresponding to branches included in the subject program and initializing sample spaces; (b) randomly generating first parameter vectors that contain components selected from the sample spaces; (c) selecting second parameter vectors based on a first branch coverage of each first parameter vector for the subject program; (d) reducing the sample spaces based on an average branch coverage of each second parameter vector for the subject program; (e) repeating steps (b) to (d) such that the average branch coverage of some of the second parameter vectors converges to a maximum; and (f) when the average branch coverage converges to a maximum, outputting an optimal parameter vector corresponding to the maximum average branch coverage, where the components of the first parameter vectors are selected from the sample spaces reduces in step (d) when step (b) is repeated.

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