6DVF: A Framework for the Development and Evaluation of Mobile Data Visualisations

Artificial Intelligence (AI) systems have gained significant traction in the recent past, creating new challenges in requirements engineering (RE) when building AI software systems. RE for AI practices have not been studied much and have scarce empirical studies. Additionally, many AI software solutions tend to focus on the technical aspects and ignore human-centered values. In this paper, we report on a case study for eliciting and modeling requirements using our framework and a supporting tool for human-centred RE for AI systems. Our case study is a mobile health application for encouraging type-2 diabetic people to reduce their sedentary behavior. We conducted our study with three experts from the app team - a software engineer, a project manager and a data scientist. We found in our study that most human-centered aspects were not originally considered when developing the first version of the application. We also report on other insights and challenges faced in RE for the health application, e.g., frequently changing requirements.

The surge in data availability and processing power has made it possible for Artificial Intelligence (AI) to advance at a faster rate. However, the different nature of AI systems has posed significant new challenges to Requirements Engineering (RE). Literature has shown that AI systems do not use current RE methods. It was also found that data scientists are taking the role of the requirements engineers resulting in software that does not focus on users needs. Building AI software with a human-centric approach has proven to produce more ethical, transparent, inclusive and non-bias outcomes. This research will look into adjusting current RE methodologies to fit into AI systems from a human-centric perspective. The project will aim to establish requirements specifications for human-centric AI and map them into a modeling language. A platform will be used to visually model and present requirements. Finally, I plan to conduct a case study to evaluate the modeling language. To date, I have conducted a Systematic Literature Review (SLR) to find current RE methodologies and challenges in AI and currently in the planning phase of a survey to find adopted practices in the industry.

In traditional approaches to building software systems (that do not include an Artificial Intelligent (AI) or Machine Learning (ML) component), Requirements Engineering (RE) activities are well-established and researched. However, building software systems with one or more AI components may depend heavily on data with limited or no insight into the system’s workings. Therefore, engineering such systems poses significant new challenges to RE. Our search showed that literature has focused on using AI to manage RE activities, with limited research on RE for AI (RE4AI). Our study’s main objective was to investigate current approaches in writing requirements for AI/ML systems, identify available tools and techniques used to model requirements, and find existing challenges and limitations. We performed a Systematic Literature Review (SLR) of current RE4AI methods and identified 27 primary studies. Using these studies, we analysed the key tools and techniques used to specify and model requirements and found several challenges and limitations of existing RE4AI practices. We further provide recommendations for future research, based on our analysis of the primary studies and mapping to industry guidelines in Google PAIR). The SLR findings highlighted that present RE applications were not adaptive to manage most AI/ML systems and emphasised the need to provide new techniques and tools to support RE4AI.