Situation-Oriented Software Requirements Specification and Model Generation

In this paper, we present a semi-automated software requirements specification (SRS) and model generation methodology in order to formally represent the requirements elicited in human-centered fashion presented in our earlier study. The term situation is defined as a 3-tuple \u3c d; A;E \u3e where d denotes human desire, A denotes the action vector, and E denotes the environment context vector. The probabilistic, timed situationtransition structure was derived using the observational data and was proposed to use as a source of new human-centered requirements elicitation. We illustrate the proposed methodology through some test cases with open access data sets and a comparison between existing SRS and the proposed SRS is given

Situation-Transition Structure and Its Applications in Software System Development

Observing, analyzing and understanding human factors is becoming a major concern in software development process in order to gain higher customer satisfaction. In this paper, we present a semi-automated methodology to generate the situation-transition structure which can be used to analyze the human behavior patterns in a specific domain. The term situation is defined as a 3-tuple \u3c d; A;E \u3e where d denotes human desire (mental state), A denotes the human actions vector, and E denotes the surrounding environment context vector. The situation-transition structure is a directed weighted graph where each node represents a unique situation or set of concurrent situations and an edge represents the transition from one situation to another. Data mining and machine learning techniques are used to generate situation-transition structure from raw observational data. We illustrate the proposed methodology through some case studies with open access datasets. The applications and advantages of situation-transition structure in software development are then asserted

A situation-driven framework for relearning of activities of daily living in smart home environments

Activities of Daily Living (ADLs) are sine qua non for self-care and improved quality of life. Self-efficacy is major challenge for seniors with early-stage dementia (ED) when performing daily living activities. ED causes deterioration of cognitive functions and thus impacts aging adults’ functioning initiative and performance of instrumental activities of daily living (IADLs). Generally, IADLs requires certain skills in both planning and execution and may involve sequence of steps for aging adults to accomplish their goals. These intricate procedures in IADLs potentially predispose older adults to safety-critical situations with life-threatening consequences. A safety-critical situation is a state or event that potentially constitutes a risk with life-threatening injuries or accidents. To address this problem, a situation-driven framework for relearning of daily living activities in smart home environment is proposed. The framework is composed of three (3) major units namely: a) goal inference unit – leverages a deep learning model to infer human goal in a smart home, b) situation-context generator – responsible for risk mitigation in IADLs, and c) a recommendation unit – to support decision making of aging adults in safety-critical situations. The proposed framework was validated against IADLs dataset collected from a smart home research prototype and the results obtained are promising

Situation-oriented requirements engineering

The establishment of smart environments, Internet of Things (IoT) and socio-technical systems has introduced many challenges to the software development process. One such main challenge is software requirements gathering which needs to address issues in a broader spectrum than traditional standalone software development. Consideration of bigger picture that includes software, its domain, the components of the domains and especially the interactions between the software and the surrounding domain components, including both human and other systems entities, is essential to gathering reliable requirements. However, most of the traditional Requirements Engineering approaches lack such comprehensive overlook of the overall view. The main objective of this work is to introduce a human-centered approach to Requirements Engineering in order to push the boundaries of traditional concepts to be more suitable for use in the development of modern socio-technical systems in smart environments. A major challenge of introducing a human-centered approach is to effectively identify the related human factors; especially, since each individual has unique desires, goals, behaviors. Our proposed solution is to use the observational data sets generated by smart environments as a resource to extract individual\u27s unique personalities and behaviors related to the software design. The concept of situations defined in our earlier study is used to represent the human and domain related aspects including human desires, goals, beliefs, interactions with the system and the constrained environment. In the first stage of this work, a computational model called situation-transition structure is developed to understand the discrete factors and behavior patterns of individuals through the observational data. During the second stage, the information mined from the situation transition structure is applied to propose new human-centered approaches to support main Requirements Engineering concepts: requirements elicitation, risk management, and prioritization. The pertinence of the proposed work is illustrated through some case studies. The conclusion asserts some of the future research direction