File Download

There are no files associated with this item.

  • Find it @ UNIST can give you direct access to the published full text of this article. (UNISTARs only)

Views & Downloads

Detailed Information

Cited time in webofscience Cited time in scopus
Metadata Downloads

Full metadata record

DC Field Value Language
dc.contributor.advisor Kim, Mijung -
dc.contributor.author Jang, Jooyoung -
dc.date.accessioned 2026-03-26T22:13:47Z -
dc.date.available 2026-03-26T22:13:47Z -
dc.date.issued 2026-02 -
dc.description.abstract Automated test generation can reach large portions of code, yet it often fails to expose deep faults because it struggles to construct semantically valid, constraint-satisfying object states that actually trigger failures. We present BOOSTER, a developer-test amplification technique that increases fault exposure by harvesting and globally recomposing valid input ingredients from developer-written tests. BOOSTER extracts object-defining statements and primitive/string values into type-indexed pools, augments missing receiver and parameter types via recursive dependency synthesis, and instantiates masked test skeletons with combinatorial selection to efficiently explore input combinations under a fixed budget. By leveraging the architectural knowledge embedded in developer tests, BOOSTER overcomes the limitations of from-scratch generation and addresses the structural constraints of prior amplification techniques that lack global ingredient recomposition. We evaluate BOOSTER on 790 real-world bugs from Defects4J (v2.0.1) across 17 open-source Java projects. In the regression scenario, BOOSTER outperforms auto- mated generation tools, detecting 298 bugs compared to 270 by EVOSUITE and 177 by RANDOOP, and it significantly surpasses state-of-the-art amplification techniques, detecting 235 bugs compared to 113 by EVO-AMP and 25 by DSPOT. In the non-regression scenario, BOOSTER demonstrates dominant performance, detecting 160 bugs compared to 87 by EVOSUITE and 77 by RANDOOP. -
dc.description.degree Master -
dc.description Department of Computer Science and Engineering -
dc.identifier.uri https://scholarworks.unist.ac.kr/handle/201301/90943 -
dc.identifier.uri http://unist.dcollection.net/common/orgView/200000964828 -
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.subject Early mental health intervention, Context-aware personalization, Mixed method, Relational empathy, Procrastination and Perfectionism -
dc.title Generating Test Cases by Recomposing Existing Developer Tests -
dc.type Thesis -

qrcode

Items in Repository are protected by copyright, with all rights reserved, unless otherwise indicated.