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Thu 13 Oct 2022 10:50 - 11:10 at Gold A - Technical Session 24 - Human Aspects Chair(s): Silvia Abrahão

Requirements Engineering in the industry is expertise-driven, heavily manual, and centered around various types of requirement specification documents being prepared and maintained. These specification documents are in diverse formats and vary depending on whether it is a business requirement document, functional specification, interface specification, client specification, and so on. These diverse specification documents embed crucial product knowledge such as functional decomposition of the domain into features, feature hierarchy, feature types and their specific feature characteristics, dependencies, business context, etc. Moreover, in a product development scenario, thousands of pages of requirement specification documentation is created over the years. Comprehending functionality and its associated context from large volumes of specification documents is a highly complex task. To address this problem, we propose to digitalize the requirement specification documents into processable models. This paper discusses the salient aspects involved in the digitalization of requirements knowledge from diverse requirement specification documents. It proposes an AI engine for the automatic transformation of diverse text-based requirement specifications into machine-processable models using NLP techniques and the generation of context-sensitive user stories. The paper describes the key requirement abstractions and concepts essential in an industrial scenario, the conceptual meta-model, and DizReq engine (AI engine for digitalizing requirements) implementation for automatically transforming diverse requirement specifications into user stories embedding the business context. The evaluation results from digitalizing specifications of an IT product suite are discussed: mean feature extraction efficiency is 40 features/file, mean user story extraction efficiency is 71 user stories/file, feature extraction accuracy is 94%, and requirement extraction accuracy is 98%.

Thu 13 Oct

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10:00 - 12:00
Technical Session 24 - Human AspectsResearch Papers / Journal-first Papers / NIER Track at Gold A
Chair(s): Silvia Abrahão Universitat Politècnica de València
10:00
20m
Research paper
Constructing a System Knowledge Graph of User Tasks and Failures from Bug Reports to Support Soap Opera Testing
Research Papers
Yanqi Su Australian National University, Zheming Han , Zhenchang Xing Australian National University, Xin Xia Huawei Software Engineering Application Technology Lab, Xiwei (Sherry) Xu CSIRO Data61, Liming Zhu CSIRO’s Data61; UNSW, Qinghua Lu CSIRO’s Data61
10:20
20m
Research paper
Data Augmentation for Improving Emotion Recognition in Software Engineering Communication
Research Papers
Mia Mohammad Imran Virginia Commonwealth University, Yashasvi Jain Drexel University, Preetha Chatterjee Drexel University, USA, Kostadin Damevski Virginia Commonwealth University
Pre-print
10:40
10m
Vision and Emerging Results
End-to-End Rationale Reconstruction
NIER Track
Mouna Dhaouadi University of Montreal, Bentley Oakes Université de Montréal, Michalis Famelis Université de Montréal
Pre-print
10:50
20m
Paper
Towards digitalization of requirements: Generating context-sensitive user stories from diverse specifications
Journal-first Papers
Padmalata Nistala Tata Consultancy Services Research, Asha Rajbhoj TCS Research, Vinay Kulkarni Tata Consultancy Services Research, Shivani Soni TCS Research, Kesav Vithal Nori IIIT Hyderabad, Raghu Reddy IIT Hyderabad
Link to publication DOI
11:10
20m
Paper
Which neural network makes more explainable decisions? An approach towards measuring explainabilityVirtual
Journal-first Papers
Mengdi Zhang Singapore Management University, Singapore, Jun Sun Singapore Management University, Jingyi Wang Zhejiang University
Link to publication DOI
11:30
20m
Paper
Automatically Identifying the Quality of Developer Chats for Post Hoc UseVirtual
Journal-first Papers
Preetha Chatterjee Drexel University, USA, Kostadin Damevski Virginia Commonwealth University, Nicholas A. Kraft UserVoice, Lori Pollock University of Delaware
Link to publication Media Attached