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Norman Anderson

Publications and source records attributed to Norman Anderson.

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ChatGPT: Friend or Foe When Comprehending and Changing Unfamiliar Code

A rapidly growing body of research is examining how LLMs influence developers when they code. To date, this research has tended to focus on productivity and code quality outcomes, rather than the underlying cognitive processes involved in programming. To address this gap, we report on the results of an exploratory laboratory study of ten advanced student developers (five with support from AI and five without) who had to make a non-trivial extension to a sizable software system. Leveraging Polya's four problem-solving phases and 25 inductively-generated codes detailing distinct problem-solving behaviors as the primary lenses, we examined: (1) how AI impacted the problem-solving approach the developers used to solve the programming task, and (2) how AI impacted their progress when they became stuck. For the analysis, we triangulated data across multiple sources (e.g., think-aloud, code changes, web searches, and LLM prompts). Unexpectedly, while developers in the AI group repeatedly turned to the AI tool to offload certain aspects of the process, all detailed problem-solving behaviors appeared in both groups. We also found that nine out of ten participants found themselves stuck in their work, but with key differences in how they became stuck and unstuck. We highlight seven distinct causes for being stuck and highlight how AI in some cases helped and in other cases hindered becoming unstuck.

cs.SE

Towards a Cognitive-Support Tool for Threat Hunters

Cybersecurity increasingly relies on threat hunters to proactively identify adversarial activity, yet the cognitive work underlying threat hunting remains underexplored or insufficiently supported by existing tools. Building on prior studies that examined how threat hunters construct and share mental models during investigations, we derived a set of design propositions to support their cognitive and collaborative work. In this paper, we present the Threat Hunter Board, a prototype tool that operationalizes these design propositions by enabling threat hunters to externalize reasoning, organize investigative leads, and maintain continuity across sessions. Using a design science paradigm, we describe the solution design rationale and artifact development. In addition, we propose six design heuristics that form a solution-evaluation framework for assessing cognitive support in threat hunting tools. An initial evaluation using a cognitive walkthrough provides early evidence of feasibility, while future work will focus on user-based validation with professional threat hunters.

cs.CR

Fuzzy to Clear: Elucidating the Threat Hunter Cognitive Process and Cognitive Support Needs

With security threats increasing in frequency and severity, it is critical that we consider the important role of threat hunters. These highly-trained security professionals learn to see, identify, and intercept security threats. Many recent works and existing tools in cybersecurity are focused on automating the threat hunting process, often overlooking the critical human element. Our study shifts this paradigm by emphasizing a human-centered approach to understanding the lived experiences of threat hunters. By observing threat hunters during hunting sessions and analyzing the rich insights they provide, we seek to advance the understanding of their cognitive processes and the tool support they need. Through an in-depth observational study of threat hunters, we introduce a model of how they build and refine their mental models during threat hunting sessions. We also present 23 themes that provide a foundation to better understand threat hunter needs and suggest five actionable design propositions to enhance the tools that support them. Through these contributions, our work enriches the theoretical understanding of threat hunting and provides practical insights for designing more effective, human-centered cybersecurity tools.

cs.CR