{"id":204,"date":"2021-11-12T10:08:06","date_gmt":"2021-11-12T07:08:06","guid":{"rendered":"https:\/\/quecst.qcri.org\/blog\/?p=204"},"modified":"2021-11-12T10:08:06","modified_gmt":"2021-11-12T07:08:06","slug":"manual-and-automatic-methods-for-user-needs-detection-in-requirements-engineering-key-concepts-and-challenges","status":"publish","type":"post","link":"https:\/\/acua.qcri.org\/blog\/manual-and-automatic-methods-for-user-needs-detection-in-requirements-engineering-key-concepts-and-challenges\/","title":{"rendered":"Manual and Automatic Methods for User Needs Detection in Requirements Engineering: Key Concepts and Challenges"},"content":{"rendered":"
\"Manual
Manual and Automatic Methods for User Needs Detection in Requirements Engineering: Key Concepts and Challenges<\/a><\/figcaption><\/figure>\n

User needs inform designers and developers of essential functionalities for requirements engineering. In this work, we summarize key concepts and challenges relating to manual and automatic user needs detection methods. We discuss six challenges with manual and eight challenges with automated
\nmethods.<\/p>\n

A. Challenges of Manual Methods
\n\u2022 A01: Limited sample sizes.
\n\u2022 A02: Budgetary constraints.
\n\u2022 A03: Time constraints.
\n\u2022 A04: Lack of scalability.
\n\u2022 A05: Difficulty of needs articulation.
\n\u2022 A06: Human bias.<\/p>\n

B. Challenges of Automated Methods
\n\u2022 B01: Inadequate datasets.
\n\u2022 B02: Irrelevant information.
\n\u2022 B03: Lack of informativeness.
\n\u2022 B04: Social desirability bias.
\n\u2022 B05: Lack of standards.
\n\u2022 B06: Lack of applicability.
\n\u2022 B07: Inability to determine meaning.
\n\u2022 B08: Need for human involvement.<\/p>\n

Despite the promise of automated methods, the challenges imply that artificial intelligence and machine learning are not yet mature enough to replace manual methods, such as interviews and focus groups, for discovering user needs in requirements engineering.<\/p>\n

Salminen, J., Jung, S.G., and\u00a0Jansen, B. J\u00a0(2021)\u00a0Manual and Automatic Methods for User Needs Detection in Requirements Engineering: Key Concepts and Challenges<\/a>. The International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME2021). 7-8 October. Mauritius. p. 1-7.<\/p>\n","protected":false},"excerpt":{"rendered":"

User needs inform designers and developers of essential functionalities for requirements engineering. In this work, we summarize key concepts and challenges relating to manual and automatic user needs detection methods. We discuss six challenges with manual and eight challenges with automated methods. A. Challenges of Manual Methods \u2022 A01: Limited sample sizes. \u2022 A02: Budgetary… Continue reading Manual and Automatic Methods for User Needs Detection in Requirements Engineering: Key Concepts and Challenges<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[26,39],"tags":[],"yoast_head":"\nManual and Automatic Methods for User Needs Detection in Requirements Engineering: Key Concepts and Challenges - Team Acua<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/acua.qcri.org\/blog\/manual-and-automatic-methods-for-user-needs-detection-in-requirements-engineering-key-concepts-and-challenges\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Manual and Automatic Methods for User Needs Detection in Requirements Engineering: Key Concepts and Challenges - Team Acua\" \/>\n<meta property=\"og:description\" content=\"User needs inform designers and developers of essential functionalities for requirements engineering. In this work, we summarize key concepts and challenges relating to manual and automatic user needs detection methods. We discuss six challenges with manual and eight challenges with automated methods. A. 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