Write a Blog >>
Wed 12 Oct 2022 17:10 - 17:30 at Banquet B - Technical Session 17 - SE for AI Chair(s): Tim Menzies

Software requirement classification is a longstanding and important problem in requirement engineering. Previous studies have applied various machine learning techniques for this problem, including Support Vector Machine (SVM) and decision trees. With the recent popularity of NLP technique, the state-of-the-art approach NoRBERT utilizes the pre-trained language model BERT and achieves a satisfactory performance. However, the dataset PROMISE used by the existing approaches for this problem consists of only hundreds of requirements that are outdated according to today’s technology and market trends. Besides, the NLP technique applied in these approaches might be obsolete. In this paper, we propose an approach of prompt learning for requirement classification using BERT-based pretrained language models (PRCBERT), which applies flexible prompt templates to achieve accurate requirements classification. Experiments conducted on two existing small-size requirement datasets (PROMISE and NFR-Review) and our collected large-scale requirement dataset NFR-SO prove that PRCBERT exhibits significantly better classification performance than NoRBERT and MLM-BERT (BERT with the standard prompt template). On the de-labeled NFR-Review and NFR-SO datasets, Trans_PRCBERT (the version of PRCBERT which is fine-tuned on PROMISE) is able to have a satisfactory zero-shot performance with 50.30% and 72.87% F1-score when enabling a self-learning strategy.

Wed 12 Oct

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

16:00 - 18:00
Technical Session 17 - SE for AIResearch Papers / Late Breaking Results / NIER Track / Tool Demonstrations at Banquet B
Chair(s): Tim Menzies North Carolina State University
16:00
10m
Vision and Emerging Results
On the Naturalness of Bytecode Instructions
NIER Track
Yoon-ho Choi Handong Global University, Jaechang Nam Handong Global University
16:10
20m
Research paper
A Light Bug Triage Framework for Applying Large Pre-trained Language Model
Research Papers
Jaehyung Lee Pohang University of Science and Technology, Pohang , Hwanjo Yu Pohang University of Science and Technology, Pohang, HanKisun Samsung Research
16:30
10m
Vision and Emerging Results
Global Decision Making Over Deep Variability in Feedback-Driven Software Development
NIER Track
Jörg Kienzle McGill University, Canada, Benoit Combemale University of Rennes; Inria; IRISA, Gunter Mussbacher McGill University, Omar Alam Trent University, Francis Bordeleau École de Technologie Supérieure (ETS), Lola Burgueño University of Malaga, Gregor Engels Paderborn University, Jessie Galasso-Carbonnel Université de Montréal, Jean-Marc Jézéquel Univ Rennes - IRISA, Bettina Kemme McGill University, Canada, Sébastien Mosser McMaster University, Houari Sahraoui Université de Montréal, Maximilian Schiedermeier McGill University, Eugene Syriani Université de Montréal
16:40
20m
Research paper
CARGO: AI-Guided Dependency Analysis for Migrating Monolithic Applications to Microservices ArchitectureACM SIGSOFT Distinguished Paper Award
Research Papers
Vikram Nitin Columbia University, Shubhi Asthana IBM Research, Baishakhi Ray Columbia University, Rahul Krishna IBM Research
Pre-print
17:00
10m
Demonstration
Answering Software Deployment Questions via Neural Machine Reading at ScaleVirtual
Tool Demonstrations
Guan Jie Qiu School of Software, Shanghai Jiao Tong University, Diwei Chen School of Software, Shanghai Jiao Tong University, Shuai Zhang School of Software, Shanghai Jiao Tong University, Yitian Chai School of Software, Shanghai Jiao Tong University, Xiaodong Gu Shanghai Jiao Tong University, China, Beijun Shen School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University
17:10
20m
Research paper
PRCBERT: Prompt Learning for Requirement Classification using BERT-based Pretrained Language ModelsVirtual
Research Papers
Xianchang Luo University of Science and Technology of China, Yinxing Xue University of Science and Technology of China, Zhenchang Xing Australian National University, Jiamou Sun Australian National University
17:30
10m
Vision and Emerging Results
Test-Driven Multi-Task Learning with Functionally Equivalent Code Transformation for Neural Code GenerationVirtual
NIER Track
Xin Wang Wuhan University, Xiao Liu School of Information Technology, Deakin University, Pingyi Zhou Noah’s Ark Lab, Huawei Technologies, Qixia Liu China Mobile Communications Corporation, Jin Liu Wuhan University, Hao Wu Yunnan University, Xiaohui Cui Wuhan University
17:40
10m
Paper
Towards Using Data-Influence Methods to Detect Noisy Samples in Source Code CorporaVirtual
Late Breaking Results
Anh T. V. Dau FPT Software AI Center, Nghi D. Q. Bui Singapore Management University, Thang Nguyen-Duc FPT Software AI Center, Hoang Thanh-Tung Vietnam National University