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Dataset for AIFix4SecCode (Work in progress) The dataset can be used for teaching machine-learning based fixing of detectable issues in AIFix4SecCode developed in the AssureMOSS project. It contains examples for vulnerable and fixed versions for: SonarQube: S1444 S2384 Spotbugs: EI_EXPOSE_REP EI_EXPOSE_REP2 FI_PUBLIC_SHOULD_BE_PROTECTED MS_EXPOSE_REP MS_MUTABLE_ARRAY MS_MUTABLE_COLLECTION MS_MUTABLE_COLLECTION_PKGPROTECT MS_SHOULD_BE_FINAL NP_NULL_ON_SOME_PATH NP_NULL_ON_SOME_PATH_EXCEPTION NP_NULL_PARAM_DEREF NP_NULL_PARAM_DEREF_ALL_TARGETS_DANGEROUS NP_NULL_PARAM_DEREF_NONVIRTUAL SQL_NONCONSTANT_STRING_PASSED_TO_EXECUTE XSS_REQUEST_PARAMETER_TO_SERVLET_WRITER
The Full paper using the dataset is available at https://doi.org/10.1109/SCAM55253.2022.00034
spotbugs, sonarqube, patch, bug, fix, manual
spotbugs, sonarqube, patch, bug, fix, manual
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
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| downloads | 1 |

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