Context:Users and developers use bug tracking systems to report errors that occur during the development and testing of software. The manual identification of duplicates is a tedious task especially with software that have large bug repositories. In this context, their automatic detection becomes a necessary task that can help prevent frequently fixing the same bug.Objective:In this article, we proposeBERT-MLP, a novel pretrained language model using bidirectional encoder representations from ransformers (BERT) for duplicate bug report detection (DBRD) with the aim of improving the detection rate compared to existing works.Method:Our approach considers only unstructured data. These are fed into the BERT model in order to learn the contextual relationships between words. The output is fed into a multilayer perceptron (MLP) classifier, representing our base DBRD.Results:Our approach was evaluated on three projects: Mozilla Firefox, Eclipse Platform, and Thunderbird. It achieved an accuracy of 92.11, 94.08, and 89.03%, respectively, for Mozilla, Eclipse, and Thunderbird. A comparison with a dual-channel convolutional neural network (DC-CNN) model and other pretrained models, including RoBERTa and Sentence-Bert has been conducted. Results showed thatBERT-MLPoutperformed, the second best performing models (DC-CNN and Sentence-BERT) by 12% in accuracy for Eclipse and 9% for both Mozilla and Thunderbird, respectively.

Within the last years, probabilistic causality has become a very active research topic in artificial intelligence and statistics communities. Due to its high impact in various applications involving reasoning tasks, machine learning researchers have proposed a number of techniques to learn Causal Bayesian Networks. Within the existing works in this direction, few studies have explicitly considered the role that decisional guidance might play to alternate between observational and experimental data processing. In this paper, we spread our previous works which foster greater collaboration between causal discovery and ontology evolution so as to evaluate them on real case study.