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DIFFBASE: a differential factbase for effective software evolution management

Xiuheng Wu, Chenguang Zhu, Yi Li

2021Year
7Citations
3Top-tier citations

Abstract

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.

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