Automatically classifying software changes via discriminative topic model: Supporting multi-category and cross-project

• The discovered topics have a one-to-one correspondence with category labels.
• The method performs both single-category and multi-category change classification.
• The method overcomes the ambiguity coming from manually assigning weights.
• The method is applicable to cross-project analysis without the need of re-learning.

Accurate classification of software changes as corrective, adaptive and perfective can enhance software decision making activities. However, a major challenge which remains is how to automatically classify multi-category changes. This paper presents a discriminative Probability Latent Semantic Analysis (DPLSA) model with a novel initialization method which initializes the word distributions for different topics using labeled samples. This method creates a one-to-one correspondence between the discovered topics and the change categories. As a result, the discriminative semantic representation of the software change messages whose largest topic entry directly corresponds to the category label of the change message which is directly used to perform single-category and multi-category change classification. In the evaluation on five open source projects, the experimental results show that the proposed approach achieves a more accurate performance than the four baseline methods. Especially with the multi-category classification task which improves the recall rate. Moreover, the different projects share the same vocabulary and the estimated model so that DPLSA is well applicable to cross-project software change message analysis.