Abstract: Collaborative development projects(CDPs) and engineering development projects(EDPs) represent two typical project models within the open-source software(OSS) ecosystem,reflecting a project's development status and engineering maturity.For developers,determining whether a project is a CDP is often challenging due to the lack of clear boundaries defining its collaborative nature.Conversely,identifying an EDP requires access to sufficient valuable development information.For researchers,neglecting CDP and EDP samples during selection contaminates the sample pool with numerous projects not development-oriented or unintentionally collaborative,thereby diminishing the validity of research findings.Current research lacks automated screening methods for these two project types.Addressing this gap,this study constructs a standardized dataset to validate the performance differences of 50 combinations of methods and features,along with 24 combinations of machine learning algorithms and feature sets,in project screening.This provides researchers with an efficient model for targeting relevant projects.The findings reveal:1)For scenarios demanding high Precision,baseline methods excel,achieving Precision scores of 0.900 and 0.880 when screening CDPs and EDPs,respectively;2)For scenarios prioritizing high F1-Score,machine learning methods perform best,yielding F1-Scores of 0.879 and 0.821 for CDP and EDP screening,respectively;3)Large language model methods achieve Precision scores of 0.691 and 0.569 for CDP and EDP screening,respectively;4)Integrating machine learning methods with existing screening approaches improve Precision by 4.56% to 42.8% for CDP screening and by 5.9% to 237.5% for EDP screening.

Key words: Data mining, Open-sources software, GitHub