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Sentiment analysis has increasingly gained significance in commercial settings, driven by the rising impact of reviews on purchase decision-making in recent years. This research conducts a thorough examination of the suitability of machine learning and deep learning approaches for sentiment analysis, using Romanian reviews as a case study, with the aim of gaining insights into their practical utility. A comprehensive, multi-level analysis is performed, covering the document, sentence, and aspect levels. The main contributions of the paper refer to the in-depth exploration of multiple sentiment analysis models at three different textual levels and the subsequent improvements brought with respect to these standard models. Additionally, a balanced dataset of Romanian reviews from twelve product categories is introduced. The results indicate that, at the document level, supervised deep learning techniques yield the best outcomes (specifically, a convolutional neural network model that obtains an AUC value of 0.93 for binary classification and a weighted average F1-score of 0.77 in a multi-class setting with 5 target classes), albeit with increased resource consumption. Favorable results are achieved at the sentence level, as well, despite the heightened complexity of sentiment identification. In this case, the best-performing model is logistic regression, for which a weighted average F1-score of 0.77 is obtained in a multi-class polarity classification task with three classes. Finally, at the aspect level, promising outcomes are observed in both aspect term extraction and aspect category detection tasks, in the form of coherent and easily interpretable word clusters, encouraging further exploration in the context of aspect-based sentiment analysis for the Romanian language. [ABSTRACT FROM AUTHOR]

In the context of new research in the software defect prediction (SDP) task using pre-trained language models, the present study aims to analyze the relevance of semantic features extracted using BERT-based language models for the detection of defective source codes. RoBERTa and CodeBERT-MLM language models are used to generate source code embeddings that capture semantic and contextual features which help in solving language understanding tasks such as SDP. The learned representations are then fed to a neural network-based SDP classifier in order to decide which of the learned code embeddings are more informative in discriminating between faulty and non-faulty software entities. Extensive experiments are conducted in a cross-version SDP scenario for Apache Calcite, an open-source framework for data management. The evaluation results of the defect classifiers show a statistically significant improvement when the code representations are learned by the pre-trained models compared with the semantic representations provided by other natural language-based models, doc2vec and LSI. [ABSTRACT FROM AUTHOR]

Software authorship attribution, defined as the problem of software authentication and resolution of source code ownership, is of major relevance in the software engineering field. Authorship analysis of source code is more difficult than the classic task on literature, but it would be of great use in various software development activities such as software maintenance, software quality analysis or project management. This paper addresses the problem of code authorship attribution and introduces, as a proof of concept, a new supervised classification model AutoSoft for identifying the developer of a certain piece of code. The proposed model is composed of an ensemble of autoencoders that are trained to encode and recognize the programming style of software developers. An extension of the AutoSoft classifier, able to recognize an unknown developer (a developer that was not seen during the training), is also discussed and evaluated. Experiments conducted on software programs collected from the Google Code Jam data set highlight the performance of the proposed model in various test settings. A comparison to existing similar solutions for code authorship attribution indicates that AutoSoft outperforms most of them. Moreover, AutoSoft provides the advantage of adaptability, illustrated through a series of extensions such as the definition of class membership probabilities and the re-framing of the AutoSoft system to address one-class classification. [ABSTRACT FROM AUTHOR]

Software authorship identification has a major relevance in software development, as it can be of help in detecting potential problems with the source code, identifying false reporting of authorship within a project and it can contribute to the improvement of the code review and testing effort prioritization. This paper introduces SoftId, a one-class classification model. The model is trained to encode and recognize the programming style of a given set of software developers, thus gaining the ability to detect whether a certain source code is authored by a developer from the original set or it is an "unknown" software developer. The novelty of the SoftId classifier resides in solving the software authorship identification task in an open-set configuration using a deep autoencoder and based on textual representations of the source codes. The experimental results highlight a significant improvement of SoftId compared to One-class Support Vector Machine classifier used in the literature for anomaly and novelty detection. [ABSTRACT FROM AUTHOR]