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| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | Yi, Jooyong | - |
| dc.contributor.author | Phong, Nguyễn Gia | - |
| dc.date.accessioned | 2024-04-11T15:20:39Z | - |
| dc.date.available | 2024-04-11T15:20:39Z | - |
| dc.date.issued | 2024-02 | - |
| dc.description.abstract | In recent decades, automatic program repair has been advancing consistently according to benchmarks. However, its use in practice is still limited due to the difficulty in choosing a desired patch among the generated pool. This work introduces a method to logically differentiate between patches through symbolic execution. The technique generates a tree of decisions for developers to reason between patches based on the program's inputs and semi-automatically captured outputs. Its implementation Psychic based on KLEE is evaluated on patches automatically generated for toy programs in the IntroClass benchmark, showing promising preliminaries. |
- |
| dc.description.degree | Master | - |
| dc.description | Department of Computer Science and Engineering | - |
| dc.identifier.uri | https://scholarworks.unist.ac.kr/handle/201301/82205 | - |
| dc.identifier.uri | http://unist.dcollection.net/common/orgView/200000743734 | - |
| dc.language | ENG | - |
| dc.publisher | Ulsan National Institute of Science and Technology | - |
| dc.rights.embargoReleaseDate | 9999-12-31 | - |
| dc.rights.embargoReleaseTerms | 9999-12-31 | - |
| dc.title | Symbolic execution groundwork for distinguishing automatically generated patches | - |
| dc.type | Thesis | - |
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