Mithila Sivakumar

Mithila Sivakumar, Martin Lochner, Shiva Nejati et al.

Stakeholder interviews are an important source of information for requirements elicitation, yet many relevant requirements remain implicit in such conversations. Stakeholders frequently describe workflows, challenges, and operational practices without explicitly articulating the software capabilities that could address them. Recent work has considered the use of LLMs to analyze conversational data and extract requirements from stakeholder interviews. Existing approaches, however, primarily focus on identifying explicitly stated requirements, leaving implicit opportunities largely unexplored. In this paper, we present LENS (LLM-Enabled Needs Discovery from Stakeholder Interviews), an approach that analyzes stakeholder interview transcripts to both extract explicit requirements and infer additional latent requirements. LENS performs this inference by reasoning over stakeholder statements together with contextual information about organizational tools and infrastructure. Both extracted and inferred requirements are represented as user stories and linked to transcript excerpts to ensure traceability. We conduct a preliminary evaluation of LENS using twelve stakeholder interview transcripts collected in an industrial setting involving cybersecurity operations. We show that LENS achieves an average F1-score of 84.4% for extracting explicit requirements, while, on average, 75% of the latent requirements identified by LENS were perceived as providing useful automation or time-saving potential by domain experts.

Mithila Sivakumar, Alvine Boaye Belle, Jinjun Shan et al.

In the ever-evolving landscape of software engineering, the emergence of large language models (LLMs) and conversational interfaces, exemplified by ChatGPT, is nothing short of revolutionary. While their potential is undeniable across various domains, this paper sets out on a captivating expedition to investigate their uncharted territory, the exploration of generating safety cases. In this paper, our primary objective is to delve into the existing knowledge base of GPT-4, focusing specifically on its understanding of the Goal Structuring Notation (GSN), a well-established notation allowing to visually represent safety cases. Subsequently, we perform four distinct experiments with GPT-4. These experiments are designed to assess its capacity for generating safety cases within a defined system and application domain. To measure the performance of GPT-4 in this context, we compare the results it generates with ground-truth safety cases created for an X-ray system system and a Machine-Learning (ML)-enabled component for tire noise recognition (TNR) in a vehicle. This allowed us to gain valuable insights into the model's generative capabilities. Our findings indicate that GPT-4 demonstrates the capacity to produce safety arguments that are moderately accurate and reasonable. Furthermore, it exhibits the capability to generate safety cases that closely align with the semantic content of the reference safety cases used as ground-truths in our experiments.

Mithila Sivakumar, Alvine B. Belle, Kimya Khakzad Shahandashti et al.

The execution failure of cyber-physical systems (e.g., autonomous driving systems, unmanned aerial systems, and robotic systems) could result in the loss of life, severe injuries, large-scale environmental damage, property destruction, and major economic loss. Hence, such systems usually require a strong justification that they will effectively support critical requirements (e.g., safety, security, and reliability) for which they were designed. Thus, it is often mandatory to develop compelling assurance cases to support that justification and allow regulatory bodies to certify such systems. In such contexts, detecting assurance deficits, relying on patterns to improve the structure of assurance cases, improving existing assurance case notations, and (semi-)automating the generation of assurance cases are key to develop compelling assurance cases and foster consumer acceptance. We therefore explore challenges related to such assurance enablers and outline some potential directions that could be explored to tackle them.