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Paul Darius Mandl

Publications and source records attributed to Paul Darius Mandl.

2 recordsLinked to original sources

LLM Non-Determinism in Deterministic Processes: A Controlled Variation Space for Constraining the Propagation of Non-Determinism

Large Language Models are increasingly used in software development processes that rely on defined states, rules, and approvals. This creates a tension between the flexibility of LLMs and the need for reliable and traceable process states. We introduce Controlled Non-Determinism, a process-oriented approach that allows variation without letting it directly determine the authoritative process state. We place LLM-based processing within a controlled variation space that is bounded by a Deterministic Envelope. Before execution, we define the process-relevant properties of the result and the exit conditions. We also record the task configuration. These elements remain fixed during execution. At the end of the variation space, an exit check determines whether the result satisfies the predefined conditions and may become the next authoritative state. Different runs may produce different admissible results while the transition to an authoritative state remains controlled. We illustrate the approach with regulated software development and derive design guidelines for LLM-based changes whose results can be checked before they become part of the authoritative software state.

cs.SE↗

AI Exposure and AI Resilience: A Two-Dimensional Assessment Framework for Software and Software-Based Business Model

Artificial intelligence is changing both software production and the economics of software-based business models. Classical technology due diligence mainly examines technical properties such as architecture, scalability, and technical debt. These criteria do not fully capture how AI can affect a company's value proposition, competitive position, margins, or access to customers. This paper develops Artificial Intelligence Exposure and Resilience (AI-ER) as a two-dimensional assessment framework. AI exposure describes the pressure for change that AI creates for a business model. AI resilience describes the company's ability to absorb that pressure, adapt to changed conditions, and use AI in an economically viable way. Metrics for both dimensions are derived from current AI capabilities, their deployment conditions, and relevant research on business models and organizational adaptability. The model keeps exposure and resilience separate and adds an explicit assessment of evidence quality and confidence. It can be applied first with public information and later refined with internal evidence. The result is a traceable company profile that supports comparison without concealing uncertainty in the underlying evidence. The paper also specifies an initial score logic and a procedure for empirical validation.

cs.AI↗