A systematic mapping to investigate the application of machine learning techniques in requirement engineering activities
Abstract 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...
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| Format: | Article |
| Language: | English |
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Wiley
2024-12-01
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| Series: | CAAI Transactions on Intelligence Technology |
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| Online Access: | https://doi.org/10.1049/cit2.12348 |
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| _version_ | 1841543341729120256 |
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| author | Shoaib Hassan Qianmu Li Khursheed Aurangzeb Affan Yasin Javed Ali Khan Muhammad Shahid Anwar |
| author_facet | Shoaib Hassan Qianmu Li Khursheed Aurangzeb Affan Yasin Javed Ali Khan Muhammad Shahid Anwar |
| author_sort | Shoaib Hassan |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-d94ec9cb7d8f4a0d869bb4995bdf8ef3 |
| institution | Kabale University |
| issn | 2468-2322 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | CAAI Transactions on Intelligence Technology |
| spelling | doaj-art-d94ec9cb7d8f4a0d869bb4995bdf8ef32025-01-13T14:05:51ZengWileyCAAI Transactions on Intelligence Technology2468-23222024-12-01961412143410.1049/cit2.12348A systematic mapping to investigate the application of machine learning techniques in requirement engineering activitiesShoaib Hassan0Qianmu Li1Khursheed Aurangzeb2Affan Yasin3Javed Ali Khan4Muhammad Shahid Anwar5School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing Jiangsu ChinaSchool of Computer Science and Engineering Nanjing University of Science and Technology Nanjing Jiangsu ChinaDepartment of Computer Engineering College of Computer and Information Sciences King Saud University Riyadh Saudi ArabiaSchool of Software Northwestern Polytechnical University Xian Shaanxi ChinaDepartment of Computer Science School of Physics, Engineering & Computer Science University of Hertfordshire Hatfield UKDepartment of AI and Software Gachon University Seongnam‐si Seongnam South KoreaAbstract 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.https://doi.org/10.1049/cit2.12348data sourcesmachine learningrequirement engineeringsupervised learning algorithms |
| spellingShingle | Shoaib Hassan Qianmu Li Khursheed Aurangzeb Affan Yasin Javed Ali Khan Muhammad Shahid Anwar A systematic mapping to investigate the application of machine learning techniques in requirement engineering activities CAAI Transactions on Intelligence Technology data sources machine learning requirement engineering supervised learning algorithms |
| title | A systematic mapping to investigate the application of machine learning techniques in requirement engineering activities |
| title_full | A systematic mapping to investigate the application of machine learning techniques in requirement engineering activities |
| title_fullStr | A systematic mapping to investigate the application of machine learning techniques in requirement engineering activities |
| title_full_unstemmed | A systematic mapping to investigate the application of machine learning techniques in requirement engineering activities |
| title_short | A systematic mapping to investigate the application of machine learning techniques in requirement engineering activities |
| title_sort | systematic mapping to investigate the application of machine learning techniques in requirement engineering activities |
| topic | data sources machine learning requirement engineering supervised learning algorithms |
| url | https://doi.org/10.1049/cit2.12348 |
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