On the Automatic Modularization of Software Systems Using the Bunch Tool
IEEE Transactions on Software Engineering
Revisiting the ΔIC approach to component recovery
Science of Computer Programming - Software analysis, evolution and re-engineering
Automated clustering to support the reflexion method
Information and Software Technology
Context-sensitive cut, copy, and paste
Proceedings of the 2008 C3S2E conference
Proceedings of the 2nd India software engineering conference
Software Engineering
Web service clustering using multidimensional angles as proximity measures
ACM Transactions on Internet Technology (TOIT)
Reverse-engineering of an industrial software using the unified process: an experiment
SEA '07 Proceedings of the 11th IASTED International Conference on Software Engineering and Applications
Automatic generation of abstract views for legacy software comprehension
Proceedings of the 3rd India software engineering conference
Deriving high-level abstractions from legacy software using example-driven clustering
Proceedings of the 2011 Conference of the Center for Advanced Studies on Collaborative Research
Improving the build architecture of legacy c/c++ software systems
FASE'05 Proceedings of the 8th international conference, held as part of the joint European Conference on Theory and Practice of Software conference on Fundamental Approaches to Software Engineering
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The majority of the algorithms in the software clusteringliterature utilize structural information in order to decomposelarge software systems. Other approaches, such as usingfile names or ownership information, have also demonstratedmerit. However, there is no intuitive way to combine informationobtained from these two different types of techniques.In this paper, we present an approach that combines structuraland non-structural information in an integrated fashion.LIMBO is a scalable hierarchical clustering algorithm basedon the minimization of information loss when clustering asoftware system.We apply LIMBO to two large software systems in a numberof experiments. The results indicate that this approachproduces valid and useful clusterings of large software systems.LIMBO can also be used to evaluate the usefulnessof various types of non-structural information to the softwareclustering process.