A paper [1] in collaboration with my PhD student, Xiuheng Wu, and Chenguang Zhu from UT Austin, was accepted by FSE’21. A quick summary of the paper is given below.
Numerous tools and techniques have been developed to extract and analyze information from software development artifacts. Yet, there is a lack of effective method to process, store, and exchange information among different analyses. In this paper, we propose differential factbase, a uniform exchangeable representation supporting efficient querying and manipulation, based on the existing concept of program facts. We consider program changes as first-class objects, which establish links between intra-version facts of single program snapshots and provide insights on how certain artifacts evolve over time via inter-version facts. We implement a series of differential fact extractors supporting different programming languages and platforms, and demonstrate with usage scenarios the benefits of adopting differential facts in supporting software evolution management.
The software artifact associated with this paper has won a Best Artifact Award at the 29th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE 2021). More details and associated artifacts can be found on the companion website: https://d-fact.github.io/.
References
- Wu, X., Zhu, C., & Li, Y. (2021, August). DIFFBASE: A Differential Factbase for Effective Software Evolution Management. Proceedings of the 29th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (FSE).