SANER 2017

2017 IEEE 24th International Conference on Software Analysis, Evolution, and Reengineering (SANER), February 20-24, 2017, Klagenfurt, Austria

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Features and Knowledge
Main Research
StiCProb: A Novel Feature Mining Approach using Conditional Probability
Yutian Tang and Hareton Leung
(Hong Kong Polytechnic University, China)
Supplementary Material
Abstract: Software Product Line Engineering is a key approach to construct applications with systematical reuse of architecture, documents and other relevant components. To migrate legacy software into a product line system, it is essential to identify the code segments that should be constructed as features from the source base. However, this could be an error-prone and complicated task, as it involves exploring a complex structure and extracting the relations between different components within a system. And normally, representing structural information of a program in a mathematical way should be a promising direction to investigate. We improve this situation by proposing a probability-based approach named StiCProb to capture source code fragments for feature concerned, which inherently provides a conditional probability to describe the closeness between two programming elements. In the case study, we conduct feature mining on several legacy systems, to compare our approach with other related approaches. As demonstrated in our experiment, our approach could support developers to locate features within legacy successfully with a better performance of 83% for precision and 41% for recall.


Time stamp: 2020-02-27T10:56:04+01:00