Requirements engineering is the first phase in software development life cycle and it also plays one of the most important and critical roles. Requirement document mainly contains both functional requirements and non-functional requirements. Non-functional requirements are significant to describe the properties and constraints of the system. Early identification of Non-functional requirement has direct impact on the system architecture and initial design decision. Practically, non-functional requirements are extracted manually from the document. This makes it tedious, time-consuming task and prone to various errors. In this paper, we propose an automatic approach to identify and classify non-functional requirements using semantic and syntactic analysis with machine learning approaches from unconstrained documents. We used A dataset of public requirements documents (PURE) that consists of 79 unconstrained requirements documents in different forms. In our approach, features were extracted from the requirement sentences using four different natural language processing methods including statistical and state-of-the-art semantic analysis presented by Google word2vec and bidirectional encoder representations from transformers models. The adopted approach can efficiently classify non-functional requirements with an accuracy between 84% and 87% using statistical vectorization method and 88% to 92% using word embedding semantic methods. Furthermore, by fusing different models trained on different features, the accuracy improves by 2.4% compared with the best individual classifier.

Recent studies show that it is possible to detect technical dept automatically from source code comments intentionally created by developers, a phenomenon known as self-admitted technical debt. This study proposes a system by which a comment or commit is classified as one of five dept types, namely, requirement, design, defect, test, and documentation. In addition to the traditional term frequency-inverse document frequency (TF-IDF), several word embeddings methods produced by different pre-trained language models were used for feature extraction, such as Word2Vec, GolVe, bidirectional encoder representations from transformers (BERT), and FastText. The generated features were used to train a set of classifiers including naive Bayes (NB), random forest (RF), support vector machines (SVM), and two configurations of convolutional neural network (CNN). Two datasets were used to train and test the proposed systems. Our collected dataset (A-dataset) includes a total of 1,513 comments and commits manually labeled. Additionally, a dataset, consisting of 4,071 labeled comments, used in previous studies (M-dataset) was also used in this study. The RF classifier achieved an accuracy of 0.822 with A-dataset and 0.820 with the M-dataset. CNN with A-dataset achieved an accuracy of 0.838 using BERT features. With M-dataset, the CNN achieves an accuracy of 0.809 and 0.812 with BERT and Word2Vec, respectively.

This paper introduces a novel approach for developing a learner model for school students by incorporating assessments from multiple sources, including students, parents, and teachers, in addition to exams. Traditional Intelligent Tutoring Systems (ITS) typically rely on a single source, such as exams, which often results in an incomplete picture of a student’s learning needs. To address this limitation, we developed a multi-source ITS that integrates feedback from various stakeholders to enhance the accuracy and personalization of the learner model. In a study involving 75 high school students, participants were divided into three groups: one receiving traditional classroom instruction, one using a conventional ITS, and one using our multi-source ITS. Results showed that the group using the multi-source system demonstrated the greatest improvement, with an average 38% increase in post-test scores, compared to 19% for the traditional instruction group and 26% for the conventional ITS group. Moreover, students using the multi-source ITS reported higher engagement and motivation, driven by their ability to interact with and adjust their learner model. These findings highlight the significant benefits of incorporating multi-source feedback into ITS to boost student performance, engagement, and motivation.