apiusagepatterns have been considered as significant materials in reusing software library apis for saving development time and improving software quality. Although efforts have been made on discovering and searchin...
详细信息
apiusagepatterns have been considered as significant materials in reusing software library apis for saving development time and improving software quality. Although efforts have been made on discovering and searching apiusagepatterns, the following two issues are still largely unexplored: how to provide a well-organised view of the discovered apiusagepatterns? and how to recommend follow-up apiusagepatterns once a usage pattern is adopted? This paper proposes two methods for categorizing and recommending apiusagepatterns: first, categories of the usagepatterns are automatically identified based on a proposed degree centrality-based clustering algorithm;and second, follow-up usagepatterns of an adopted pattern are recommended based on a proposed metric of measuring distances between patterns. In the experimental evaluations, the patternscategorization can achieve 85.4% precision rate with 83% recall rate. The patterns recommendation had approximately half a chance of correctly predicting the follow-up patterns that were actually used by the programmers.
Although efforts have been made on discovering and searching apiusagepatterns, how to categorize and recommend follow-up apiusagepatterns is still largely unexplored. This paper advances the state-of-the-art by pr...
详细信息
ISBN:
(纸本)9781509034383
Although efforts have been made on discovering and searching apiusagepatterns, how to categorize and recommend follow-up apiusagepatterns is still largely unexplored. This paper advances the state-of-the-art by proposing two methods for categorizing and recommending apiusagepatterns: first, categories of the usagepatterns are automatically identified based on a proposed degree centrality-based clustering algorithm;and second, follow-up usagepatterns of an adopted pattern are recommended based on a proposed metric of measuring distances between patterns. In the experimental evaluations, the patternscategorization can achieve 85.4% precision rate with 83% recall rate. The patterns recommendation had approximately half a chance of correctly predicting the follow-up patterns that were actually used by the programmers.
暂无评论