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ICSE 2022
Sun 8 - Fri 27 May 2022
Sun 8 May 2022 12:10 - 12:30 at NLBSE room - Paper Session 2 Chair(s): Andrea Di Sorbo, Sebastiano Panichella

Background: Systematic Literature Reviews are an important research method for gathering and evaluating the available evidence regarding a specific research topic. However, the process of conducting a Systematic Literature Review manually can be difficult and time-consuming. For this reason, researchers aim to semi-automate this process or some of its phases. Aim: We aimed at using a deep-learning based contextualized embeddings clustering technique involving transformer-based language models and a weighted scheme to accelerate the conduction phase of Systematic Literature Reviews for efficiently scanning the initial set of retrieved publications. Method: We performed an experiment using two manually conducted SLRs to evaluate the performance of two deep-learning-based clustering models. These models build on transformer-based deep language models (i.e., BERT and S-BERT) to extract contextualized embeddings on different text levels along with a weighted scheme to cluster similar publications. Results: Our primary results show that clustering based on embedding at paragraph-level using S-BERT-paragraph represents the best performing model setting in terms of optimizing the required parameters such as correctly identifying primary studies, number of additional documents identified as part of the relevant cluster and the execution time of the experiments. Conclusions: The findings indicate that using natural-language-based deep-learning architectures for semi-automating the selection of primary studies can accelerate the scanning and identification process. While our results represent first insights only, such a technique seems to enhance SLR process, promising to help researchers identify the most relevant publications more quickly and efficiently.

Sun 8 May

Displayed time zone: Eastern Time (US & Canada) change

11:20 - 12:45
Paper Session 2NLBSE at NLBSE room
Chair(s): Andrea Di Sorbo University of Sannio, Sebastiano Panichella Zurich University of Applied Sciences
On the Evaluation of NLP-based Models for Software Engineering
Maliheh Izadi Delft University of Technology, Matin Nili Ahmadabadi University of Tehran
Identification of Intra-Domain Ambiguity using Transformer-based Machine Learning
Ambarish Moharil Eindhoven University of Technology, Arpit Sharma
Can NMT Understand Me? Towards Perturbation-based Evaluation of NMT Models for Code Generation
Pietro Liguori University of Naples Federico II, Cristina Improta University of Naples Federico II, Simona De Vivo University of Naples Federico II, Roberto Natella Federico II University of Naples, Bojan Cukic University of North Carolina at Charlotte, Domenico Cotroneo University of Naples Federico II
Supporting Systematic Literature Reviews Using Deep-Learning-Based Language Models
Rand Alchokr Otto von Guericke University, Manoj Borkar , Sharanya Thotadarya otto von Guericke University, Thomas Leich Harz University of Applied Sciences, Germany, Gunter Saake Otto von Guericke University
Story Point Level Classification by Text Level Graph Neural Network
Hung Phan , Ali Jannesari Iowa State University

Information for Participants
Sun 8 May 2022 11:20 - 12:45 at NLBSE room - Paper Session 2 Chair(s): Andrea Di Sorbo, Sebastiano Panichella
Info for room NLBSE room:

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