Investigating ChatGPT's Potential to Assist in Requirements Elicitation Processes

Natural Language Processing (NLP) for Requirements Engineering (RE) (NLP4RE) seeks to apply NLP tools, techniques, and resources to the RE process to increase the quality of the requirements. There is little research involving the utilization of Generative AI-based NLP tools and techniques for requirements elicitation. In recent times, Large Language Models (LLM) like ChatGPT have gained significant recognition due to their notably improved performance in NLP tasks. To explore the potential of ChatGPT to assist in requirements elicitation processes, we formulated six questions to elicit requirements using ChatGPT. Using the same six questions, we conducted interview-based surveys with five RE experts from academia and industry and collected 30 responses containing requirements. The quality of these 36 responses (human-formulated + ChatGPT-generated) was evaluated over seven different requirements quality attributes by another five RE experts through a second round of interview-based surveys. In comparing the quality of requirements generated by ChatGPT with those formulated by human experts, we found that ChatGPT-generated requirements are highly Abstract, Atomic, Consistent, Correct, and Understandable. Based on these results, we present the most pressing issues related to LLMs and what future research should focus on to leverage the emergent behaviour of LLMs more effectively in natural language-based RE activities.Comment: Accepted at SEAA 2023. 8 pages, 5 figure

RE-centric Recommendations for the Development of Trustworthy(er) Autonomous Systems

Complying with the EU AI Act (AIA) guidelines while developing and implementing AI systems will soon be mandatory within the EU. However, practitioners lack actionable instructions to operationalise ethics during AI systems development. A literature review of different ethical guidelines revealed inconsistencies in the principles addressed and the terminology used to describe them. Furthermore, requirements engineering (RE), which is identified to foster trustworthiness in the AI development process from the early stages was observed to be absent in a lot of frameworks that support the development of ethical and trustworthy AI. This incongruous phrasing combined with a lack of concrete development practices makes trustworthy AI development harder. To address this concern, we formulated a comparison table for the terminology used and the coverage of the ethical AI principles in major ethical AI guidelines. We then examined the applicability of ethical AI development frameworks for performing effective RE during the development of trustworthy AI systems. A tertiary review and meta-analysis of literature discussing ethical AI frameworks revealed their limitations when developing trustworthy AI. Based on our findings, we propose recommendations to address such limitations during the development of trustworthy AI.Comment: Accepted at [TAS '23]{First International Symposium on Trustworthy Autonomous Systems

Robotic Software Development using DevOps

Background: Due to the complexity involved in robotic software development, the progress in the field has been slow. Component-based software engineering was observed to have a strong influence on the improvement of the robotic software development process and its adoption achieved good results. DevOps was seen to be compatible and produced efficient results in software engineering. Objectives: The aim of this thesis work is to present the potential usage of DevOps practices in robotic software development. 15 DevOps practices were selected from prior research from software engineering and mapped to the robotic software development process and checked for success in terms of applicability and effectiveness. Methods: By performing a research synthesis of the literature, the usage of DevOps practices in robotic software development is proposed and presented. Interview based survey was performed by approaching industry experts on robotic software development to get their response on the results of the research synthesis. Results: The applicability of the DevOps practices in robotic software development is presented and the implications of the potential usage of the practices in the proposed manner are discussed. The potential advantages and limitations of the proposed mapping are discussed and presented. Conclusions: DevOps, like other software development frameworks, has various observable advantages when applied in robotic software development. The interviews confirmed the need for DevOps to be adapted into robotic software development and the benefits it has

Robotic Software Development using DevOps

Background: Due to the complexity involved in robotic software development, the progress in the field has been slow. Component-based software engineering was observed to have a strong influence on the improvement of the robotic software development process and its adoption achieved good results. DevOps was seen to be compatible and produced efficient results in software engineering. Objectives: The aim of this thesis work is to present the potential usage of DevOps practices in robotic software development. 15 DevOps practices were selected from prior research from software engineering and mapped to the robotic software development process and checked for success in terms of applicability and effectiveness. Methods: By performing a research synthesis of the literature, the usage of DevOps practices in robotic software development is proposed and presented. Interview based survey was performed by approaching industry experts on robotic software development to get their response on the results of the research synthesis. Results: The applicability of the DevOps practices in robotic software development is presented and the implications of the potential usage of the practices in the proposed manner are discussed. The potential advantages and limitations of the proposed mapping are discussed and presented. Conclusions: DevOps, like other software development frameworks, has various observable advantages when applied in robotic software development. The interviews confirmed the need for DevOps to be adapted into robotic software development and the benefits it has