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Mirela Riveni

Publications and source records attributed to Mirela Riveni.

2 recordsLinked to original sources

AdProv: A Method for Provenance of Process Adaptations

Provenance in scientific workflows is essential for understand- ing and reproducing processes, while in business processes, it can ensure compliance and correctness and facilitates process mining. However, the provenance of process adaptations, especially modifications during execu- tion, remains insufficiently addressed. A review of the literature reveals a lack of systematic approaches for capturing provenance information about adaptive workflows/processes. To fill this gap, we propose the AdProv method for collecting, storing, retrieving, and visualizing prove- nance of runtime workflow adaptations. In addition to the definition of the AdProv method in terms of steps and concepts like change events, we also present an architecture for a Provenance Holder service that is essential for implementing the method. To ensure semantic consistency and interoperability we define a mapping to the ontology PROV Ontol- ogy (PROV-O). Additionally, we extend the XES standard with elements for adaptation logging. Our main contributions are the AdProv method and a comprehensive framework and its tool support for managing adap- tive workflow provenance, facilitating advanced provenance tracking and analysis for different application domains.

cs.CR

Ecosystem-wide influences on pull request decisions: insights from NPM

The pull-based development model facilitates global collaboration within open-source software projects. However, whereas it is increasingly common for software to depend on other projects in their ecosystem, most research on the pull request decision-making process explored factors within projects, not the broader software ecosystem they comprise. We uncover ecosystem-wide factors that influence pull request acceptance decisions. We collected a dataset of approximately 1.8 million pull requests and 2.1 million issues from 20,052 GitHub projects within the NPM ecosystem. Of these, 98% depend on another project in the dataset, enabling studying collaboration across dependent projects. We employed social network analysis to create a collaboration network in the ecosystem, and mixed effects logistic regression and random forest techniques to measure the impact and predictive strength of the tested features. We find that gaining experience within the software ecosystem through active participation in issue-tracking systems, submitting pull requests, and collaborating with pull request integrators and experienced developers benefits all open-source contributors, especially project newcomers. These results are complemented with an exploratory qualitative analysis of 538 pull requests. We find that developers with ecosystem experience make different contributions than users without. Zooming in on a subset of 111 pull requests with clear ecosystem involvement, we find 3 overarching and 10 specific reasons why developers involve ecosystem projects in their pull requests. The results show that combining ecosystem-wide factors with features studied in previous work to predict the outcome of pull requests reached an overall F1 score of 0.92. However, the outcomes of pull requests submitted by newcomers are harder to predict.

cs.SE