Fetching the paper…
Reading the bibliography…
Recently, various illustrative examples have shown the impressive ability of generative large language models (LLMs) to perform NLP related tasks.
Software requirements and specifications: A survey of needs and languages
Abbott RJ, Moorhead D · 1981
Earlier work this paper cites.
The role of natural language in requirements engineering
Ryan K · 1993
Earlier work this paper cites.
The fundamental nature of requirements engineering activities as a decision-making process
Aurum A, Wohlin C · 2003
Earlier work this paper cites.
Engineering and managing software requirements, vol. 1
Aurum A, Wohlin C · 2005
Earlier work this paper cites.
An approach to constructing feature models based on requirements clustering
Chen K, Zhang W, Zhao H, Mei H · 2005
Earlier work this paper cites.
Introduction to information retrieval, vol. 39
Schütze H, Manning CD, Raghavan P · 2008
Earlier work this paper cites.
Natural language processing with Python: analyzing text with the natural language toolkit
Bird S, Klein E, Loper E · 2009
Earlier work this paper cites.
Requirements engineering: fundamentals, principles, and techniques
Pohl K · 2010
Earlier work this paper cites.
Natural Language Annotation for Machine Learning: A guide to corpus-building for applications
Pustejovsky J, Stubbs A · 2012
Earlier work this paper cites.
User feedback in the appstore: An empirical study
Pagano D, Maalej W · 2013
Earlier work this paper cites.
Toward data-driven requirements engineering
Maalej W, Nayebi M, Johann T, Ruhe G · 2015
Earlier work this paper cites.
Deep learning
LeCun Y, Bengio Y, Hinton G · 2015
Earlier work this paper cites.
Acquiring creative requirements from the crowd: Understanding the influences of personality and creative potential in Crowd RE
Murukannaiah PK, Ajmeri N, Singh MP · 2016
Earlier work this paper cites.
A survey of app store analysis for software engineering
Martin W, Sarro F, Jia Y, Zhang Y, Harman M · 2016
Earlier work this paper cites.
Mining requirements knowledge from collections of domain documents
Lian X, Rahimi M, Cleland-Huang J, Zhang L, Ferrai R, Smith M · 2016
Earlier work this paper cites.
On the automatic classification of app reviews
Maalej W, Kurtanović Z, Nabil H, Stanik C · 2016
Earlier work this paper cites.
Understanding “software-defined” from an OS perspective: technical challenges and research issues
Mei H · 2017
Earlier work this paper cites.
Mining twitter feeds for software user requirements
Williams G, Mahmoud A · 2017
Earlier work this paper cites.
Natural language requirements processing: a 4D vision
Ferrari A, Dell’Orletta F, Esuli A, Gervasi V, Gnesi S, et al · 2017
Earlier work this paper cites.
Pure: A dataset of public requirements documents
Ferrari A, Spagnolo GO, Gnesi S · 2017
Earlier work this paper cites.
An Aspect-Based Unsupervised Approach for Classifying Non-Functional Requirements on Software Reviews
Wang Y, Zhang J · 2017
Earlier work this paper cites.
The content analysis guidebook
Neuendorf KA · 2017
Cited alongside, same era.
An exploratory study of twitter messages about software applications
Guzman E, Alkadhi R, Seyff N · 2017
Cited alongside, same era.
Natural language requirements processing: from research to practice
Ferrari A · 2018
Cited alongside, same era.
Mining non-functional requirements from app store reviews
Jha N, Mahmoud A · 2019
Cited alongside, same era.
Recommending new features from mobile app descriptions
Jiang H, Zhang J, Li X, Ren Z, Lo D, Wu X, et al · 2019
Cited alongside, same era.
App store effects on software engineering practices
Al-Subaihin AA, Sarro F, Black S, Capra L, Harman M · 2019
Cited alongside, same era.
Understanding in-app advertising issues based on large scale app review analysis
Gao C, Zeng J, Lo D, Xia X, King I, Lyu MR · 2022
Later among the works it cites.
PRCBERT: Prompt Learning for Requirement Classification using BERT-based Pretrained Language Models
Luo X, Xue Y, Xing Z, Sun J · 2022
Later among the works it cites.
A systematic process for Mining Software Repositories: Results from a systematic literature review
Vidoni M · 2022
Later among the works it cites.
Automatic Terminology Extraction and Ranking for Feature Modeling
Zhang J, Chen S, Hua J, Niu N, Liu C · 2022
Later among the works it cites.
Analysing app reviews for software engineering: a systematic literature review
Dąbrowski J, Letier E, Perini A, Susi A · 2022
Later among the works it cites.
Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Norbert: Transfer learning for requirements classification
Hey T, Keim J, Koziolek A, Tichy WF · 2020
Cited alongside, same era.
Identifying security issues for mobile applications based on user review summarization
Tao C, Guo H, Huang Z · 2020
Cited alongside, same era.
Generating question titles for stack overflow from mined code snippets
Gao Z, Xia X, Grundy J, Lo D, Li YF · 2020
Cited alongside, same era.
Managing privacy in the digital economy
Wang C, Zhang N, Wang C · 2021
Cited alongside, same era.
Emerging app issue identification via online joint sentiment-topic tracing
Gao C, Zeng J, Wen Z, Lo D, Xia X, King I, et al · 2021
Cited alongside, same era.
Classifying mobile applications using word embeddings
Ebrahimi F, Tushev M, Mahmoud A · 2021
Cited alongside, same era.
Wang Y, Mishra S, Alipoormolabashi P, Kordi Y, Mirzaei A, Naik A, et al · 2022
Later among the works it cites.
Analyzing user perspectives on mobile app privacy at scale
Nema P, Anthonysamy P, Taft N, Peddinti ST · 2022
Later among the works it cites.
Domain-Specific Analysis of Mobile App Reviews Using Keyword-Assisted Topic Models
Tushev M, Ebrahimi F, Mahmoud A · 2022
Later among the works it cites.
Opinion mining for software development: a systematic literature review
Lin B, Cassee N, Serebrenik A, Bavota G, Novielli N, Lanza M · 2022
Later among the works it cites.
Strategies, Benefits and Challenges of App Store-inspired Requirements Elicitation
Ferrari A, Spoletini P · 2023
Closest in time.
Machine learning for software engineering: A tertiary study
Kotti Z, Galanopoulou R, Spinellis D · 2023
Closest in time.
Nlp-based automated compliance checking of data processing agreements against gdpr
Amaral O, Azeem MI, Abualhaija S, Briand LC · 2023
Closest in time.
A survey of large language models
Zhao WX, Zhou K, Li J, Tang T, Wang X, Hou Y, et al · 2023
Closest in time.
A brief overview of ChatGPT: The history, status quo and potential future development
Wu T, He S, Liu J, Sun S, Liu K, Han QL, et al · 2023
Closest in time.
ChatGPT: potential, prospects, and limitations
Zhou J, Ke P, Qiu X, Huang M, Zhang J · 2023
Closest in time.
Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing
Liu P, Yuan W, Fu J, Jiang Z, Hayashi H, Neubig G · 2023
Closest in time.
Exploring the feasibility of chatgpt for event extraction
Gao J, Zhao H, Yu C, Xu R · 2023
Closest in time.
Exploring privacy requirements gap between developers and end users
Zhang J, Hua J, Niu N, Chen S, Savolainen J, Liu C · 2023
Closest in time.
A Review of ChatGPT Applications in Education, Marketing, Software Engineering, and Healthcare: Benefits, Drawbacks, and Research Directions
Fraiwan M, Khasawneh N · 2023
Closest in time.
An Analysis of the Automatic Bug Fixing Performance of ChatGPT
Sobania D, Briesch M, Hanna C, Petke J · 2023
Closest in time.
No More Manual Tests? Evaluating and Improving ChatGPT for Unit Test Generation
Yuan Z, Lou Y, Liu M, Ding S, Wang K, Chen Y, et al · 2023
Closest in time.