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arxiv: 2606.05651 · v1 · pith:JQZTASJ5new · submitted 2026-06-04 · 📡 eess.SY · cs.SE· cs.SY

Development of a Structured Approach for Establishing Mission Engineering Requirements

Taylor C. Fazzini , Daniel R. Herber This is my paper

Pith reviewed 2026-06-28 00:30 UTC · model grok-4.3

classification 📡 eess.SY cs.SEcs.SY
keywords mission engineeringrequirements engineeringmission effectivenessBest-Worst Scalingsystems engineeringtraceabilitymission decompositioncomplexity factor
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The pith

A structured decomposition of mission intent into six categories plus Best-Worst Scaling and a complexity factor yields traceable Tier 1 and 2 requirements even without initial customer input.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper develops a method for approximating mission effectiveness when stakeholder requirements are missing or change during rapid development programs such as military acquisition or space projects. Legacy frameworks assume customer specifications exist upfront, leaving a gap the authors fill by breaking mission intent into mission context, functions, constraints, critical dimensions, effectiveness attributes, and architecture alternatives. The approach adds a feasibility assessment, uses Best-Worst Scaling to prioritize dimensions, and introduces a mission complexity factor that accounts for external difficulties, technology maturity, evidence standards, and utility. The output supplies a traceable chain from intent to requirements that can later integrate with modeling languages. A notional close air support mission demonstrates the steps.

Core claim

Mission effectiveness can be systematically defined or approximated by decomposing mission intent into mission context, functions, constraints, critical dimensions, effectiveness attributes, and architecture alternatives, followed by a feasibility assessment, Best-Worst Scaling prioritization of critical dimensions, and a quantitative mission complexity factor that captures external difficulties, technology maturity, evidence standards, and mission utility; the resulting structure supplies a traceable basis for deriving Tier 1 and 2 requirements.

What carries the argument

The six-category decomposition of mission intent combined with Best-Worst Scaling for prioritization and an introduced mission complexity factor that quantifies external impacts.

If this is right

  • Rapid programs can generate initial requirements from mission intent alone without waiting for complete customer input.
  • Traceability is maintained from high-level intent through prioritized dimensions to specific Tier 1 and 2 requirements.
  • The method supports later integration with UAF and SysML artifacts for model-based systems engineering.
  • Feasibility assessment and complexity factor allow quantitative comparison of architecture alternatives under varying external conditions.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The approach could extend to infrastructure or commercial projects where objectives evolve during bidding or early design.
  • Explicit handling of evidence standards and technology maturity might allow the complexity factor to serve as an input to risk registers or technology roadmaps.
  • If automated, the decomposition and scaling steps could feed directly into digital engineering environments that generate draft requirement documents.

Load-bearing premise

That systematically decomposing mission intent into the listed categories plus Best-Worst Scaling and the complexity factor can reliably approximate mission effectiveness in the absence of customer requirements.

What would settle it

Apply the method to derive requirements for a real close air support mission, then compare the resulting Tier 1 and 2 requirements and predicted effectiveness against observed mission outcomes and stakeholder-validated requirements.

read the original abstract

This paper addresses the question: How can mission effectiveness be systematically defined or approximated in the absence of customer requirements? Legacy requirements engineering frameworks presuppose customer input to define specifications but leave a gap in the process when stakeholder input is ill-defined or missing. Rapid build and development programs (such as military acquisition, space assets, infrastructure projects, etc.) often see requirement and objective evolutions throughout the proposal process, so a more adaptive method is needed. To address this gap, a structured approach is proposed that decomposes mission intent into mission context, functions, constraints, critical dimensions, effectiveness attributes, and architecture alternatives. This method conducts a mission feasibility assessment, prioritizes mission-critical dimensions using Best-Worst Scaling, and introduces a mission complexity factor to quantitatively understand the impacts of external mission difficulties, technology maturity, evidence and confidence standards, and mission utility. The resulting method provides a traceable basis for deriving Tier 1 and 2 requirements. The approach is structured to support future Unified Architecture Framework (UAF) and Systems Modeling Language (SysML) artifact integration. The proposed framework is demonstrated using a notional close air support mission example.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript proposes a structured approach to define or approximate mission effectiveness when customer requirements are absent or ill-defined. It decomposes mission intent into categories including context, functions, constraints, critical dimensions, effectiveness attributes, and architecture alternatives; applies Best-Worst Scaling to prioritize critical dimensions; and introduces a mission complexity factor to quantify impacts from external difficulties, technology maturity, evidence standards, and utility. The resulting framework is claimed to provide a traceable basis for deriving Tier 1 and 2 requirements and is illustrated via a notional close air support mission example, with intended support for UAF and SysML integration.

Significance. If the traceability construction holds under further development, the method could address a recognized gap in legacy requirements engineering for rapid-acquisition programs in defense, space, and infrastructure domains. The explicit decomposition categories and use of BWS constitute a systematic, reproducible process that is a strength relative to ad-hoc approaches; the notional demonstration illustrates the workflow even though it does not constitute validation.

major comments (2)
  1. [Abstract; notional close air support mission example] The central claim that the decomposition plus BWS and complexity factor 'provides a traceable basis for deriving Tier 1 and 2 requirements' is asserted in the abstract and method overview but is not demonstrated by an explicit mapping from the notional example outputs to concrete Tier 1/2 requirement statements; this traceability step is load-bearing for the paper's primary contribution.
  2. [Method description; mission complexity factor subsection] The mission complexity factor is introduced to 'quantitatively understand the impacts' of external factors, yet no equation, weighting scheme, or worked calculation appears in the description of the factor or its integration into the prioritization or requirements derivation; without this, the quantitative claim cannot be evaluated.
minor comments (2)
  1. [Notional example] The notional example would be clearer if it tabulated the Best-Worst Scaling pairwise comparisons or scores for the critical dimensions rather than describing them narratively.
  2. [Decomposition categories] Notation for the effectiveness attributes and architecture alternatives should be defined consistently (e.g., a table of symbols) to aid readers who wish to replicate the decomposition.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. The two major comments identify important gaps in demonstrating the core claims. We address each below and will revise the manuscript accordingly.

read point-by-point responses

  1. Referee: [Abstract; notional close air support mission example] The central claim that the decomposition plus BWS and complexity factor 'provides a traceable basis for deriving Tier 1 and 2 requirements' is asserted in the abstract and method overview but is not demonstrated by an explicit mapping from the notional example outputs to concrete Tier 1/2 requirement statements; this traceability step is load-bearing for the paper's primary contribution.

    Authors: We agree that the notional example illustrates the decomposition, BWS prioritization, and complexity factor but stops short of an explicit, step-by-step mapping to sample Tier 1 and Tier 2 requirement statements. This traceability link is central to the contribution. We will revise the example section to add a dedicated subsection that derives concrete Tier 1 and Tier 2 requirements directly from the prioritized outputs and complexity-adjusted scores, thereby making the traceability explicit. revision: yes

  2. Referee: [Method description; mission complexity factor subsection] The mission complexity factor is introduced to 'quantitatively understand the impacts' of external factors, yet no equation, weighting scheme, or worked calculation appears in the description of the factor or its integration into the prioritization or requirements derivation; without this, the quantitative claim cannot be evaluated.

    Authors: The current manuscript introduces the mission complexity factor conceptually through its four constituent elements but does not supply an explicit equation, weighting scheme, or numerical example of how the factor modifies the BWS scores or feeds into requirement derivation. We acknowledge this limits evaluation of the quantitative aspect. We will add a new subsection that defines a multiplicative complexity adjustment formula, specifies how each element is scored, and includes a worked numerical example integrated with the close air support case. revision: yes

Circularity Check

0 steps flagged

No significant circularity identified

full rationale

The paper is a methods proposal that decomposes mission intent into listed categories, applies Best-Worst Scaling, and introduces a complexity factor to generate a traceable basis for Tier 1/2 requirements. No equations, fitted parameters, predictions derived from model outputs, or self-citations appear in the provided text. The central claim is that the described decomposition yields traceability; this is presented as a new structured approach rather than a derivation that reduces to its own inputs by construction. The notional example serves only as illustration. No load-bearing step matches any enumerated circularity pattern.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim rests on the untested premise that the chosen decomposition categories plus Best-Worst Scaling and the new complexity factor are sufficient to approximate effectiveness without customer input. No free parameters or formal axioms are stated in the abstract.

axioms (1)
  • domain assumption The listed decomposition categories (mission context, functions, constraints, critical dimensions, effectiveness attributes, architecture alternatives) are sufficient to capture mission intent for requirements derivation.
    Invoked when the paper states the method decomposes mission intent into these elements to conduct feasibility assessment.
invented entities (1)
  • mission complexity factor no independent evidence
    purpose: To quantitatively understand the impacts of external mission difficulties, technology maturity, evidence and confidence standards, and mission utility.
    Introduced in the abstract as part of the method; no independent evidence or falsifiable prediction is provided.

pith-pipeline@v0.9.1-grok · 5732 in / 1393 out tokens · 29156 ms · 2026-06-28T00:30:15.044998+00:00 · methodology

discussion (0)

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Reference graph

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