TY - JOUR
T1 - A systematic mapping to investigate the application of machine learning techniques in requirement engineering activities
AU - Hassan, Shoaib
AU - Li, Qianmu
AU - Aurangzeb, Khursheed
AU - Yasin, Affan
AU - Khan, Javed Ali
AU - Anwar, Muhammad Shahid
N1 - © 2024 The Author(s). CAAI Transactions on Intelligence Technology published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology and Chongqing University of Technology. This is an open access article under the Creative Commons Attribution-NonCommercial-No Derivatives CC BY-NC-ND licence, https://creativecommons.org/licenses/by-nc-nd/4.0/
PY - 2024/6/10
Y1 - 2024/6/10
N2 - Over the past few years, the application and usage of Machine Learning (ML) techniques have increased exponentially due to continuously increasing the size of data and computing capacity. Despite the popularity of ML techniques, only a few research studies have focused on the application of ML especially supervised learning techniques in Requirement Engineering (RE) activities to solve the problems that occur in RE activities. The authors focus on the systematic mapping of past work to investigate those studies that focused on the application of supervised learning techniques in RE activities between the period of 2002–2023. The authors aim to investigate the research trends, main RE activities, ML algorithms, and data sources that were studied during this period. Forty‐five research studies were selected based on our exclusion and inclusion criteria. The results show that the scientific community used 57 algorithms. Among those algorithms, researchers mostly used the five following ML algorithms in RE activities: Decision Tree, Support Vector Machine, Naïve Bayes, K‐nearest neighbour Classifier, and Random Forest. The results show that researchers used these algorithms in eight major RE activities. Those activities are requirements analysis, failure prediction, effort estimation, quality, traceability, business rules identification, content classification, and detection of problems in requirements written in natural language. Our selected research studies used 32 private and 41 public data sources. The most popular data sources that were detected in selected studies are the Metric Data Programme from NASA, Predictor Models in Software Engineering, and iTrust Electronic Health Care System.
AB - Over the past few years, the application and usage of Machine Learning (ML) techniques have increased exponentially due to continuously increasing the size of data and computing capacity. Despite the popularity of ML techniques, only a few research studies have focused on the application of ML especially supervised learning techniques in Requirement Engineering (RE) activities to solve the problems that occur in RE activities. The authors focus on the systematic mapping of past work to investigate those studies that focused on the application of supervised learning techniques in RE activities between the period of 2002–2023. The authors aim to investigate the research trends, main RE activities, ML algorithms, and data sources that were studied during this period. Forty‐five research studies were selected based on our exclusion and inclusion criteria. The results show that the scientific community used 57 algorithms. Among those algorithms, researchers mostly used the five following ML algorithms in RE activities: Decision Tree, Support Vector Machine, Naïve Bayes, K‐nearest neighbour Classifier, and Random Forest. The results show that researchers used these algorithms in eight major RE activities. Those activities are requirements analysis, failure prediction, effort estimation, quality, traceability, business rules identification, content classification, and detection of problems in requirements written in natural language. Our selected research studies used 32 private and 41 public data sources. The most popular data sources that were detected in selected studies are the Metric Data Programme from NASA, Predictor Models in Software Engineering, and iTrust Electronic Health Care System.
KW - data analysis
KW - machine learning
KW - software engineering
UR - http://www.scopus.com/inward/record.url?scp=85195468891&partnerID=8YFLogxK
U2 - 10.1049/cit2.12348
DO - 10.1049/cit2.12348
M3 - Article
SN - 2468-2322
SP - 1
EP - 22
JO - CAAI Transactions on Intelligence Technology
JF - CAAI Transactions on Intelligence Technology
M1 - 12348
ER -