
CleanAnalysed_auditDF.csv:This CSV file contains anonymized data used in the audit of machine learning experiments on software defect prediction. The dataset includes variables and performance metrics extracted from studies published between 2019 and 2023. It supports the audit's evaluation of study reproducibility and issues related to experimental design and statistical analysis. This data can be used for replication and further analysis of the trends and reproducibility issues identified in the paper. ML_Audit_Aug2025.Rmd:This RMarkdown file contains the analysis script used for the statistical analysis and audit of the machine learning experiments reviewed in the study. It includes the procedures for data preprocessing, statistical evaluations, and reproducibility assessments. The script is integral for replicating the audit results presented in the paper and can be used by other researchers to perform similar audits or extend the analysis on different datasets.
| 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 |
