Reverse Engineering Super-Repositories
WCRE '07 Proceedings of the 14th Working Conference on Reverse Engineering
Macro-level software evolution: a case study of a large software compilation
Empirical Software Engineering
MSR '09 Proceedings of the 2009 6th IEEE International Working Conference on Mining Software Repositories
Benchmarking Lightweight Techniques to Link E-Mails and Source Code
WCRE '09 Proceedings of the 2009 16th Working Conference on Reverse Engineering
How developers use the dynamic features of programming languages: the case of smalltalk
Proceedings of the 8th Working Conference on Mining Software Repositories
A study of ripple effects in software ecosystems (NIER track)
Proceedings of the 33rd International Conference on Software Engineering
How do developers react to API deprecation?: the case of a smalltalk ecosystem
Proceedings of the ACM SIGSOFT 20th International Symposium on the Foundations of Software Engineering
Towards modeling and analyzing variability in evolving software ecosystems
Proceedings of the Seventh International Workshop on Variability Modelling of Software-intensive Systems
Software ecosystems - A systematic literature review
Journal of Systems and Software
How (and why) developers use the dynamic features of programming languages: the case of smalltalk
Empirical Software Engineering
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In large software systems, knowing the dependencies between modules or components is critical to assess the impact of changes. To recover the dependencies, fact extractors analyze the system as a whole and build the dependency graph, parsing the system down to the statement level. At the level of software ecosystems, which are collections of software projects, the dependencies that need to be recovered reside not only within the individual systems, but also between the libraries, frameworks, and entire software systems that make up the complete ecosystem; scaling issues arise. In this paper we present and evaluate several variants of a lightweight and scalable approach to recover dependencies between the software projects of an ecosystem. We evaluate our recovery algorithms on the Squeak 3.10 Universe, an ecosystem containing more than 200 software projects.