Automatically classifying software changes via discriminative topic model

TL;DR: Zhang et al. as mentioned in this paper proposed an approach to automatically generate PR descriptions based on the commit messages and the added source code comments in the PRs using a sequence-to-sequence model.

TL;DR: The experimental results show that the proposed change-level SATD Determination model achieves a promising and better performance than four baselines in terms of AUC and cost-effectiveness and “Diffusion” is the most discriminative dimension among the three dimensions of features for determining TD-introducing changes.

TL;DR: A taxonomy based on recurring patterns in commit messages' expressions is developed, investigating whether “good” commit messages can be automatically identified and whether such automation could prompt developers to write better commit messages.

TL;DR: The results of the empirical investigation show that fixing code smells is not the main driver for developers to refactoring their code bases, and this classification challenges the original definition ofRefactoring, being exclusive to improving software design and fixing code smelling.

TL;DR: This work proposes a generative model for text and other collections of discrete data that generalizes or improves on several previous models including naive Bayes/unigram, mixture of unigrams, and Hofmann's aspect model.

TL;DR: This paper proposed a generative model for text and other collections of discrete data that generalizes or improves on several previous models including naive Bayes/unigram, mixture of unigrams, and Hof-mann's aspect model, also known as probabilistic latent semantic indexing (pLSI).

TL;DR: WordNet1 provides a more effective combination of traditional lexicographic information and modern computing, and is an online lexical database designed for use under program control.

TL;DR: This work considers the problem of automatically recognizing human faces from frontal views with varying expression and illumination, as well as occlusion and disguise, and proposes a general classification algorithm for (image-based) object recognition based on a sparse representation computed by C1-minimization.