Representation and learning in information retrieval
Representation and learning in information retrieval
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
An evaluation of reverse engineering tool capabilities
Journal of Software Maintenance: Research and Practice
Text-Learning and Related Intelligent Agents: A Survey
IEEE Intelligent Systems
A Little Knowledge Can Go a Long Way Towards Program Understanding
WPC '97 Proceedings of the 5th International Workshop on Program Comprehension (WPC '97)
Supporting Software Maintenance by Mining Software Update Records
ICSM '01 Proceedings of the IEEE International Conference on Software Maintenance (ICSM'01)
The Journal of Machine Learning Research
Studying Software Engineers: Data Collection Techniques for Software Field Studies
Empirical Software Engineering
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In a system maintained over a long time period, as is the case for legacy software, there will be many unknown and non-trivial relationships among components. Finding such hidden relationships may help software engineers in their maintenance activities. In this paper we present an approach whereby we mine software update records to find relationships between files that are changed together. The generalized models we present as results are obtained by using features extracted from different sources of knowledge such as source code and problem reports. The predictive quality of some of the generated models suggest that they can be deployed to be used in a real world setting. The paper also includes the results of analyzing the structure of some of the best models obtained.