Software feature extraction using infrequent feature extraction

Evolution and maintenance processes are important but time consuming and expensive. It is very important to make the processes effective and efficient. A software developer can use resource like user opinion data to get information, such as user request, bug report, and user experience. It represents user needs and can be used to help allocate the necessary effort of software evolution and maintenance. The amount of user opinion data is very large and is difficult manually process them. A Recent study has tried to implement collocation finding method to extract software features from user opinion data. However, it is not able to extract non-frequently mentioned features. In this paper, we proposed an improvement for software feature extraction from user opinion data. Linguistic rules were used to complement collocation finding method. Feature pruning was also added to eliminate irrelevant features. The result shows that the proposed method is able to extract more features than collocation finding method.