TY - GEN
T1 - A Tertiary Study on AI for Requirements Engineering
AU - Mehraj, Ali
AU - Zhang, Zheying
AU - Systä, Kari
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024
Y1 - 2024
N2 - Context and Motivation: Rapid advancements in Artificial Intelligence (AI) have significantly influenced requirements engineering (RE) practices. Problem: While many recent secondary studies have explored AI’s role in RE, a thorough understanding of the use of AI for RE (AI4RE) and its inherent challenges remains in its early stages.Principal Ideas: To fill this knowledge gap, we conducted a tertiary review on understanding how AI assists RE practices. Contribution: We analyzed 28 secondary studies from 2017 to September 2023 about using AI in RE tasks such as elicitation, classification, analysis, specification, management, and tracing. Our study reveals a trend of combining natural language process techniques with machine learning models like Latent Dirichlet Allocation (LDA) and Naive Bayes, and a surge in using large language models (LLMs) for RE. The study also identified challenges of AI4RE related to ambiguity, language, data, algorithm, and evaluation. The study gives topics for future research, particularly for researchers who want to start new research in this field.
AB - Context and Motivation: Rapid advancements in Artificial Intelligence (AI) have significantly influenced requirements engineering (RE) practices. Problem: While many recent secondary studies have explored AI’s role in RE, a thorough understanding of the use of AI for RE (AI4RE) and its inherent challenges remains in its early stages.Principal Ideas: To fill this knowledge gap, we conducted a tertiary review on understanding how AI assists RE practices. Contribution: We analyzed 28 secondary studies from 2017 to September 2023 about using AI in RE tasks such as elicitation, classification, analysis, specification, management, and tracing. Our study reveals a trend of combining natural language process techniques with machine learning models like Latent Dirichlet Allocation (LDA) and Naive Bayes, and a surge in using large language models (LLMs) for RE. The study also identified challenges of AI4RE related to ambiguity, language, data, algorithm, and evaluation. The study gives topics for future research, particularly for researchers who want to start new research in this field.
KW - Artificial intelligence
KW - Machine learning
KW - Natural language processing
KW - Requirements engineering
KW - Tertiary study
U2 - 10.1007/978-3-031-57327-9_10
DO - 10.1007/978-3-031-57327-9_10
M3 - Conference contribution
AN - SCOPUS:85190711575
SN - 9783031573262
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 159
EP - 177
BT - Requirements Engineering: Foundation for Software Quality
A2 - Mendez, Daniel
A2 - Mendez, Daniel
A2 - Moreira, Ana
A2 - Moreira, Ana
PB - Springer Science and Business Media Deutschland GmbH
T2 - International Working Conference on Requirements Engineering
Y2 - 8 April 2024 through 11 April 2024
ER -