요구 공학을 이용한 DAQ System의 비기능적 요구사항 추출 지원
Using Requirements Engineering to support Non-Functional Requirements Elicitation for DAQ System
0
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
최근의 기계 학습 연구에서는 데이터의 품질 및 완전성을 고려하기 위해 요구 공학 관점에서 데이터에 대한 비기능적 요구사항 도출이 제안되고 있으며, 특히 기계 학습에서의 데이터 요구사항을 요구공학자들이 정의하였다. 본 연구에서는 데이터 전처리 이전에 데이터를 수집하고 저장하는 데이터수집(DAQ) 단계에서 데이터 요구사항을 도출하였다. 이를 통해 기존 DAQ 시스템에서 필요로 하는모든 데이터 요구사항과 이를 만족시키는 태스크(기능)의 유무, 그리고 요구사항과 기능 간의 관계를표현할 수 있다. 또한 추출된 요구사항을 체계적으로 표현하고, 그 관계를 정의할 수 있어 소프트웨어설계 문서의 작성이 가능하고 소프트웨어 설계 및 유지 보수 측면에서 체계적인 접근 및 방향성을설정할 수 있다. 본 연구에서는 기존 DAQ 시스템 사례를 이용하여 요구 공학적 접근을 위한 시나리오와 유스 케이스(Use case)를 작성하고, 이를 기반으로 사례별 데이터 요구사항을 추출하고 목표 모델링을 통해 요구사항, 기능, 목표 간의 관계를 도식화한다. 연구 결과를 통해 요구 공학을 이용한 DAQ 시스템에서 시스템이 필요로 하는 비기능적 요구사항, 특히 데이터의 요구사항을 추출할 수 있었다.
Abstract
In recent machine learning studies, in order to consider the quality and completeness of data, derivation of non-functional requirements for data has been proposed from the viewpoint of requirements engineering. In particular, requirements engineers have defined data requirements in machine learning. In this study, data requirements were derived at the data acquisition (DAQ) stage, where data is collected and stored before data preprocessing. Through this, it is possible to express the requirements of all data required in the existing DAQ system, the presence of tasks (functions) satisfying them, and the relationship between the requirements and functions. In addition, it is possible to elicit requirements and to define the relationship, so that a software design document can be produced, and a systematic approach and direction can be established in terms of software design and maintenance. This research using existing DAQ system cases, scenarios and use cases for requirements engineering approach are created, and data requirements for each case are extracted based on them, and the relationship between requirements, functions, and goals is illustrated through goal modeling. Through the research results, it was possible to extract the non-functional requirements of the system, especially the data requirements, from the DAQ system using requirements engineering.
참고문헌 (12)
- Vogelsang, Andreas, "Requirements Engineering for Machine Learning: Perspectives from Data Scientists", 2019IEEE 27th International Requirements Engineering Conference Workshops (REW) (2019), IEEE
- Horkoff, "Non-functional requirements for machine learning : Challenges and new directions", 2019 IEEE 27th International Requirements Engineering Conference(RE), pp.386~391 (2019)
- Abdallah, Mohammed, "A survey on data acquisition systems DAQ", 2009 International Conference on Computing, Engineering and Information (2009), IEEE
- Vogelsang, Andreas, "Requirements Engineering for Machine Learning: Perspectives from Data Scientists", 2019IEEE 27th International Requirements Engineering Conference Workshops (REW) (2019), IEEE
- Van Lamsweerde, Axel, "Goal-oriented requirements engineering:A guided tour", Proceedings fifth IEEE international symposium on requirements engineering (2001), IEEE
- Yu, Eric, "Why goal-oriented requirements engineering", Proceedings of the 4th International Workshop on Requirements Engineering: Foundations of Software Quality, 15 (1998)
- Anton, Annie I, "Goal-based requirements analysis", Proceedings of the second international conference on requirements engineering (1996), IEEE
- ISO, "IEC 25012:2008-Software engineering. Software product quality requirements and evaluation (SQuaRE)", ISO, 2009, Data quality model, Report
- 장경애, "고객의 요구사항에 기반한 데이터품질 평가속성 및 우선순위 도출", 정보처리학회 논문지, 4(12), pp.549~560 (2015), 한국정보처리학회[DOI]
- Li, Bei, "Experiences of building a medical data acquisition system based on two-level modeling", International journal of medical informatics, 112, pp.114~122 (2018)
- A Software Quality Model for Asynchronous Store-and-Forward Telemedicine Systems, "Gresse von Wangenheim, C.", UFSC, ~, pp.Technical Report INCoD (2011), UFSC 005[DOI]
- Donzelli, Paolo, "Handling the knowledge acquired during the requirements engineering process: a case study", Proceedings of the 14th international conference on Software engineering and knowledge engineering (2002)