Continuous Integration is a critical problem for software maintenance in global projects, compromising companies’ performance, which tends to accumulate a high-resolution time due to the approval process, conflict resolution, tests, and validations. The process of the validation involves the commit description interpretation and can be automated by NLP-mechanisms. This paper presents an intelligent NLP-based approach to evaluate whether the commits can be integrated into a certain software release based only on their descriptions. Our experiments showed an accuracy of 92.9%.

As programming skills are increasingly required world-wide and across disciplines, many students use online platforms that provide automatic feedback through a Programming Online Judge (POJ) mechanism. POJs are very popular e-learning tools, boasting large collections of programming problems. Despite their many benefits, students often struggle when solving problems not compatible with their prior knowledge. One important cause of this is that usually statements of problems are not classified according to programming topics (paradigms, data structures, etc.) and, hence, students waste time and effort in trying to solve exercises that are not tailored to their level and needs. Thus, to support students, we propose a new, "front-heavy" pipeline method to predict topics of POJ problems, using Bidirectional Encoder Representations from Transformers (BERT) for contextual text augmentation over the problem statements and further allowing for (lighter-weight) classical machine learning for classification. Our model outperformed all current state-of-the art, with an F1-score of 86% using stratified 10 fold cross-validation in a classically challenging multi-classification problem with seven categories. As a proof of concept, we conducted an experiment to show how our predictive model can be used as a human-AI hybrid complement for POJ, where learners would use AI-based recommendations to find the most appropriate problems.