Devang Dhanuka

Devang Dhanuka

Large Language Models (LLMs) are increasingly being integrated into software development processes, with the potential to transform team workflows and productivity. This paper investigates how LLMs affect team collaboration throughout the Software Development Life Cycle (SDLC). We reframe and update a prior study with recent developments as of 2025, incorporating new literature and case studies. We outline the problem of collaboration hurdles in SDLC and explore how LLMs can enhance productivity, communication, and decision-making in a team context. Through literature review, industry examples, a team survey, and two case studies, we assess the impact of LLM-assisted tools (such as code generation assistants and AI-powered project management agents) on collaborative software engineering practices. Our findings indicate that LLMs can significantly improve efficiency (by automating repetitive tasks and documentation), enhance communication clarity, and aid cross-functional collaboration, while also introducing new challenges like model limitations and privacy concerns. We discuss these benefits and challenges, present research questions guiding the investigation, evaluate threats to validity, and suggest future research directions including domain-specific model customization, improved integration into development tools, and robust strategies for ensuring trust and security.

Nidhi Rastogi, Shirid Pant, Devang Dhanuka et al.

Explainable AI (XAI) holds significant promise for enhancing the transparency and trustworthiness of AI-driven threat detection in Security Operations Centers (SOCs). However, identifying the appropriate level and format of explanation, particularly in environments that demand rapid decision-making under high-stakes conditions, remains a complex and underexplored challenge. To address this gap, we conducted a three-month mixed-methods study combining an online survey (N1=248) with in-depth interviews (N2=24) to examine (1) how SOC analysts conceptualize AI-generated explanations and (2) which types of explanations are perceived as actionable and trustworthy across different analyst roles. Our findings reveal that participants were consistently willing to accept XAI outputs, even in cases of lower predictive accuracy, when explanations were perceived as relevant and evidence-backed. Analysts repeatedly emphasized the importance of understanding the rationale behind AI decisions, expressing a strong preference for contextual depth over a mere presentation of outcomes on dashboards. Building on these insights, this study re-evaluates current explanation methods within security contexts and demonstrates that role-aware, context-rich XAI designs aligned with SOC workflows can substantially improve practical utility. Such tailored explainability enhances analyst comprehension, increases triage efficiency, and supports more confident responses to evolving threats.