{"id":4518,"date":"2026-01-19T14:18:21","date_gmt":"2026-01-19T06:18:21","guid":{"rendered":"https:\/\/guides.visual-paradigm.com\/ru\/docs\/streamlining-the-software-lifecycle-integrating-ai-use-case-modeling-with-visual-paradigms-all-in-one-platform\/6-scenario-analysis-and-logic-validation\/"},"modified":"2026-01-19T15:13:41","modified_gmt":"2026-01-19T07:13:41","slug":"6-scenario-analysis-and-logic-validation","status":"publish","type":"docs","link":"https:\/\/guides.visual-paradigm.com\/ru\/docs\/streamlining-the-software-lifecycle-integrating-ai-use-case-modeling-with-visual-paradigms-all-in-one-platform\/6-scenario-analysis-and-logic-validation\/","title":{"rendered":"6. Scenario Analysis and Logic Validation"},"content":{"rendered":"

By the time you reach Module 6, the project has progressed from high-level vision \u2192 detailed use cases \u2192 refined relationships \u2192 structured specifications \u2192 rich behavioral (Activity & Sequence), structural (Class), and data (ERD) models. At this point, the models are comprehensive and visually expressive\u2014but they still live in the world of design intent<\/em>. The critical question now becomes:<\/p>\n

\u201cDoes the system actually work correctly under all realistic combinations of conditions, inputs, and events?\u201d<\/strong><\/p>\n

This is the purpose of Scenario Analysis and Logic Validation<\/strong>: systematically breaking down complex use cases into concrete, testable scenarios; mapping every meaningful combination of conditions and decisions; and verifying that the specified behavior, edge cases, exceptions, and business rules are complete, consistent, and free of logical gaps or contradictions.<\/p>\n

This module focuses on two tightly integrated techniques:<\/p>\n

    \n
  1. Scenario Breakdown<\/strong> \u2014 Converting narrative flows (especially those with multiple decisions, loops, or alternatives) into a clear set of distinct, named scenarios that cover happy paths, variations, and error conditions.<\/li>\n
  2. Decision Matrix \/ Decision Table Analysis<\/strong> \u2014 Creating tabular representations that systematically enumerate all unique combinations of conditions (inputs, states, guards) against the corresponding actions\/outcomes, ensuring 100% coverage of decision logic.<\/li>\n<\/ol>\n

    These activities are not optional polish\u2014they are essential quality gates. They expose hidden assumptions, missing cases, inconsistent rules, and ambiguous specifications before<\/em> implementation begins, dramatically reducing costly rework during development and testing.<\/p>\n

    Visual Paradigm\u2019s AI-Powered Use Case Modeling Studio<\/strong> supports this phase by:<\/p>\n

      \n
    • Automatically suggesting key decision points and branches from Activity & Sequence Diagrams<\/li>\n
    • Generating initial scenario lists and decision tables from use case flows<\/li>\n
    • Highlighting uncovered combinations or contradictory outcomes<\/li>\n
    • Allowing you to refine tables interactively (add conditions, merge equivalent rules, annotate business rationale)<\/li>\n
    • Exporting decision tables as test-case foundations (directly feeding into Module 7)<\/li>\n<\/ul>\n

      Practical Examples<\/h3>\n

      Example 1: GourmetReserve \u2013 Use Case: Book a Table<\/strong><\/p>\n

      Key Decision Points Identified by AI<\/strong>:<\/p>\n

        \n
      • Is the diner authenticated?<\/li>\n
      • Are tables available for the requested time & party size?<\/li>\n
      • Is a deposit required (party size, time slot, loyalty status)?<\/li>\n
      • Does the diner provide a valid promo code?<\/li>\n
      • Does payment succeed?<\/li>\n<\/ul>\n

        AI-Suggested Scenario Breakdown<\/strong> (partial list):<\/p>\n

          \n
        1. Happy path \u2013 authenticated, tables available, deposit waived (Gold loyalty), no promo, payment succeeds \u2192 Reservation confirmed<\/li>\n
        2. Happy path with promo \u2013 valid code applied \u2192 Reduced deposit<\/li>\n
        3. No tables available \u2192 Offered waitlist \u2192 Diner joins waitlist<\/li>\n
        4. Deposit required but payment fails (card declined) \u2192 Booking aborted, error shown<\/li>\n
        5. Late cancellation attempt (within 2 hours) \u2192 No-show fee applied on cancellation<\/li>\n<\/ol>\n

          Decision Table Generated by AI<\/strong> (simplified):<\/p>\n

          \n
          \n
          <\/div>\n<\/div>\n
          \n
          <\/div>\n\n\n\n\n\n\n\n\n\n
          Scenario<\/th>\nAuthenticated?<\/th>\nTables Available?<\/th>\nDeposit Required?<\/th>\nValid Promo?<\/th>\nPayment Succeeds?<\/th>\nOutcome<\/th>\n<\/tr>\n<\/thead>\n
          1<\/td>\nYes<\/td>\nYes<\/td>\nNo<\/td>\nNo<\/td>\n\u2014<\/td>\nConfirmed (no deposit)<\/td>\n<\/tr>\n
          2<\/td>\nYes<\/td>\nYes<\/td>\nYes<\/td>\nYes<\/td>\nYes<\/td>\nConfirmed (discounted deposit)<\/td>\n<\/tr>\n
          3<\/td>\nYes<\/td>\nNo<\/td>\n\u2014<\/td>\n\u2014<\/td>\n\u2014<\/td>\nWaitlist offered<\/td>\n<\/tr>\n
          4<\/td>\nYes<\/td>\nYes<\/td>\nYes<\/td>\nNo<\/td>\nNo<\/td>\nError: Payment failed<\/td>\n<\/tr>\n
          5<\/td>\nNo<\/td>\n\u2014<\/td>\n\u2014<\/td>\n\u2014<\/td>\n\u2014<\/td>\nRedirect to login<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
          <\/div>\n<\/div>\n<\/div>\n

          Your refinement<\/strong>:<\/p>\n

            \n
          • Add column: \u201cPeak hours (Fri\/Sat 7\u20139 pm)?\u201d \u2192 Influences deposit requirement<\/li>\n
          • Add rule: If party size \u2265 12 \u2192 Manual approval required (new outcome: \u201cPending manager review\u201d)<\/li>\n<\/ul>\n

            Example 2: SecureATM \u2013 Use Case: Withdraw Cash<\/strong><\/p>\n

            Decision Table (AI-generated excerpt)<\/strong>:<\/p>\n

            \n
            \n
            <\/div>\n<\/div>\n
            \n
            <\/div>\n\n\n\n\n\n\n\n\n\n
            Scenario<\/th>\nAuthenticated?<\/th>\nAmount \u2264 Daily Limit?<\/th>\nSufficient Funds?<\/th>\nSufficient Cash in ATM?<\/th>\nOutcome<\/th>\n<\/tr>\n<\/thead>\n
            1<\/td>\nYes<\/td>\nYes<\/td>\nYes<\/td>\nYes<\/td>\nCash dispensed, balance updated<\/td>\n<\/tr>\n
            2<\/td>\nYes<\/td>\nNo<\/td>\nYes<\/td>\nYes<\/td>\nError: Exceeds daily limit<\/td>\n<\/tr>\n
            3<\/td>\nYes<\/td>\nYes<\/td>\nNo<\/td>\nYes<\/td>\nError: Insufficient funds<\/td>\n<\/tr>\n
            4<\/td>\nYes<\/td>\nYes<\/td>\nYes<\/td>\nNo<\/td>\nError: ATM temporarily unavailable<\/td>\n<\/tr>\n
            5<\/td>\nNo<\/td>\n\u2014<\/td>\n\u2014<\/td>\n\u2014<\/td>\nPrompt for PIN\/card retry<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
            <\/div>\n<\/div>\n<\/div>\n

            Refinement added<\/strong>:<\/p>\n

              \n
            • New column: \u201cHigh-value transaction (> $1,000)?\u201d \u2192 Triggers biometric check (extra condition)<\/li>\n
            • Outcome variant: Biometric fails \u2192 Transaction aborted, card retained, fraud alert sent<\/li>\n<\/ul>\n

              Example 3: CorpLearn \u2013 Use Case: Take Final Assessment<\/strong><\/p>\n

              Decision Table<\/strong>:<\/p>\n

              \n
              \n
              <\/div>\n<\/div>\n
              \n
              <\/div>\n\n\n\n\n\n\n\n\n\n
              Scenario<\/th>\nAll modules completed?<\/th>\nTime not expired?<\/th>\nCompliance acknowledgments done?<\/th>\nScore \u2265 80%?<\/th>\nRetakes remaining?<\/th>\nFinal Outcome<\/th>\n<\/tr>\n<\/thead>\n
              1<\/td>\nYes<\/td>\nYes<\/td>\nYes<\/td>\nYes<\/td>\n\u2014<\/td>\nPassed \u2192 Certificate issued<\/td>\n<\/tr>\n
              2<\/td>\nYes<\/td>\nYes<\/td>\nYes<\/td>\nNo<\/td>\nYes<\/td>\nFailed \u2192 Retake offered<\/td>\n<\/tr>\n
              3<\/td>\nYes<\/td>\nYes<\/td>\nYes<\/td>\nNo<\/td>\nNo<\/td>\nFailed \u2192 Course marked incomplete<\/td>\n<\/tr>\n
              4<\/td>\nYes<\/td>\nNo<\/td>\n\u2014<\/td>\n\u2014<\/td>\n\u2014<\/td>\nAuto-submitted \u2192 Score calculated<\/td>\n<\/tr>\n
              5<\/td>\nNo<\/td>\n\u2014<\/td>\n\u2014<\/td>\n\u2014<\/td>\n\u2014<\/td>\nCannot start assessment<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
              <\/div>\n<\/div>\n<\/div>\n

              Your refinement<\/strong>:<\/p>\n

                \n
              • Add condition: \u201cMandatory data privacy question answered correctly?\u201d \u2192 If No \u2192 Auto-fail regardless of score<\/li>\n
              • Merge equivalent rows where possible to keep table compact<\/li>\n<\/ul>\n

                Why This Step Is Critical (Even with AI Models)<\/h3>\n

                AI-generated diagrams and specifications are excellent at following patterns, but they can miss:<\/p>\n

                  \n
                • Domain-specific combinations (e.g., regulatory thresholds)<\/li>\n
                • Rare but catastrophic edge cases<\/li>\n
                • Contradictory business rules hidden across different flows<\/li>\n
                • Performance or concurrency implications of certain paths<\/li>\n<\/ul>\n

                  Scenario analysis and decision tables force explicit enumeration\u2014turning implicit assumptions into visible, reviewable logic. They directly become the foundation for comprehensive test cases (Module 7) and provide strong evidence of due diligence for audits or compliance reviews.<\/p>\n

                  By the end of Module 6, you will have:<\/p>\n

                    \n
                  • A curated set of named, prioritized scenarios covering normal, alternative, and exceptional behavior<\/li>\n
                  • Decision tables that prove complete coverage of every meaningful condition combination<\/li>\n
                  • Early detection (and resolution) of logical gaps, ambiguities, or inconsistencies<\/li>\n<\/ul>\n

                    This rigorous validation step ensures that the system doesn\u2019t just look good on paper\u2014it actually behaves correctly when subjected to real-world variability. With logic fully validated, you\u2019re ready to generate high-quality, traceable test cases in the final quality assurance phase.<\/p>\n","protected":false},"featured_media":0,"parent":4496,"menu_order":6,"comment_status":"open","ping_status":"closed","template":"","meta":{"_yoast_wpseo_title":"","_yoast_wpseo_metadesc":"","_eb_attr":"","neve_meta_sidebar":"","neve_meta_container":"","neve_meta_enable_content_width":"","neve_meta_content_width":0,"neve_meta_title_alignment":"","neve_meta_author_avatar":"","neve_post_elements_order":"","neve_meta_disable_header":"","neve_meta_disable_footer":"","neve_meta_disable_title":""},"doc_tag":[],"class_list":["post-4518","docs","type-docs","status-publish","hentry"],"yoast_head":"\n6. Scenario Analysis and Logic Validation - Visual Paradigm Guides Russian<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/guides.visual-paradigm.com\/ru\/docs\/streamlining-the-software-lifecycle-integrating-ai-use-case-modeling-with-visual-paradigms-all-in-one-platform\/6-scenario-analysis-and-logic-validation\/\" \/>\n<meta property=\"og:locale\" content=\"ru_RU\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"6. Scenario Analysis and Logic Validation - Visual Paradigm Guides Russian\" \/>\n<meta property=\"og:description\" content=\"By the time you reach Module 6, the project has progressed from high-level vision \u2192 detailed use cases \u2192 refined relationships \u2192 structured specifications \u2192 rich behavioral (Activity & Sequence), structural (Class), and data (ERD) models. At this point, the models are comprehensive and visually expressive\u2014but they still live in the world of design intent. The critical question now becomes: \u201cDoes the system actually work correctly under all realistic combinations of conditions, inputs, and events?\u201d This is the purpose of Scenario Analysis and Logic Validation: systematically breaking down complex use cases into concrete, testable scenarios; mapping every meaningful combination of conditions and decisions; and verifying that the specified behavior, edge cases, exceptions, and business rules are complete, consistent, and free of logical gaps or contradictions. This module focuses on two tightly integrated techniques: Scenario Breakdown \u2014 Converting narrative flows (especially those with multiple decisions, loops, or alternatives) into a clear set of distinct, named scenarios that cover happy paths, variations, and error conditions. Decision Matrix \/ Decision Table Analysis \u2014 Creating tabular representations that systematically enumerate all unique combinations of conditions (inputs, states, guards) against the corresponding actions\/outcomes, ensuring 100% coverage of decision logic. These activities are not optional polish\u2014they are essential quality gates. They expose hidden assumptions, missing cases, inconsistent rules, and ambiguous specifications before implementation begins, dramatically reducing costly rework during development and testing. Visual Paradigm\u2019s AI-Powered Use Case Modeling Studio supports this phase by: Automatically suggesting key decision points and branches from Activity & Sequence Diagrams Generating initial scenario lists and decision tables from use case flows Highlighting uncovered combinations or contradictory outcomes Allowing you to refine tables interactively (add conditions, merge equivalent rules, annotate business rationale) Exporting decision tables as test-case foundations (directly feeding into Module 7) Practical Examples Example 1: GourmetReserve \u2013 Use Case: Book a Table Key Decision Points Identified by AI: Is the diner authenticated? Are tables available for the requested time & party size? Is a deposit required (party size, time slot, loyalty status)? Does the diner provide a valid promo code? Does payment succeed? AI-Suggested Scenario Breakdown (partial list): Happy path \u2013 authenticated, tables available, deposit waived (Gold loyalty), no promo, payment succeeds \u2192 Reservation confirmed Happy path with promo \u2013 valid code applied \u2192 Reduced deposit No tables available \u2192 Offered waitlist \u2192 Diner joins waitlist Deposit required but payment fails (card declined) \u2192 Booking aborted, error shown Late cancellation attempt (within 2 hours) \u2192 No-show fee applied on cancellation Decision Table Generated by AI (simplified): Scenario Authenticated? Tables Available? Deposit Required? Valid Promo? Payment Succeeds? Outcome 1 Yes Yes No No \u2014 Confirmed (no deposit) 2 Yes Yes Yes Yes Yes Confirmed (discounted deposit) 3 Yes No \u2014 \u2014 \u2014 Waitlist offered 4 Yes Yes Yes No No Error: Payment failed 5 No \u2014 \u2014 \u2014 \u2014 Redirect to login Your refinement: Add column: \u201cPeak hours (Fri\/Sat 7\u20139 pm)?\u201d \u2192 Influences deposit requirement Add rule: If party size \u2265 12 \u2192 Manual approval required (new outcome: \u201cPending manager review\u201d) Example 2: SecureATM \u2013 Use Case: Withdraw Cash Decision Table (AI-generated excerpt): Scenario Authenticated? Amount \u2264 Daily Limit? Sufficient Funds? Sufficient Cash in ATM? Outcome 1 Yes Yes Yes Yes Cash dispensed, balance updated 2 Yes No Yes Yes Error: Exceeds daily limit 3 Yes Yes No Yes Error: Insufficient funds 4 Yes Yes Yes No Error: ATM temporarily unavailable 5 No \u2014 \u2014 \u2014 Prompt for PIN\/card retry Refinement added: New column: \u201cHigh-value transaction (> $1,000)?\u201d \u2192 Triggers biometric check (extra condition) Outcome variant: Biometric fails \u2192 Transaction aborted, card retained, fraud alert sent Example 3: CorpLearn \u2013 Use Case: Take Final Assessment Decision Table: Scenario All modules completed? Time not expired? Compliance acknowledgments done? Score \u2265 80%? Retakes remaining? Final Outcome 1 Yes Yes Yes Yes \u2014 Passed \u2192 Certificate issued 2 Yes Yes Yes No Yes Failed \u2192 Retake offered 3 Yes Yes Yes No No Failed \u2192 Course marked incomplete 4 Yes No \u2014 \u2014 \u2014 Auto-submitted \u2192 Score calculated 5 No \u2014 \u2014 \u2014 \u2014 Cannot start assessment Your refinement: Add condition: \u201cMandatory data privacy question answered correctly?\u201d \u2192 If No \u2192 Auto-fail regardless of score Merge equivalent rows where possible to keep table compact Why This Step Is Critical (Even with AI Models) AI-generated diagrams and specifications are excellent at following patterns, but they can miss: Domain-specific combinations (e.g., regulatory thresholds) Rare but catastrophic edge cases Contradictory business rules hidden across different flows Performance or concurrency implications of certain paths Scenario analysis and decision tables force explicit enumeration\u2014turning implicit assumptions into visible, reviewable logic. They directly become the foundation for comprehensive test cases (Module 7) and provide strong evidence of due diligence for audits or compliance reviews. By the end of Module 6, you will have: A curated set of named, prioritized scenarios covering normal, alternative, and exceptional behavior Decision tables that prove complete coverage of every meaningful condition combination Early detection (and resolution) of logical gaps, ambiguities, or inconsistencies This rigorous validation step ensures that the system doesn\u2019t just look good on paper\u2014it actually behaves correctly when subjected to real-world variability. 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Scenario Analysis and Logic Validation - Visual Paradigm Guides Russian","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/guides.visual-paradigm.com\/ru\/docs\/streamlining-the-software-lifecycle-integrating-ai-use-case-modeling-with-visual-paradigms-all-in-one-platform\/6-scenario-analysis-and-logic-validation\/","og_locale":"ru_RU","og_type":"article","og_title":"6. Scenario Analysis and Logic Validation - Visual Paradigm Guides Russian","og_description":"By the time you reach Module 6, the project has progressed from high-level vision \u2192 detailed use cases \u2192 refined relationships \u2192 structured specifications \u2192 rich behavioral (Activity & Sequence), structural (Class), and data (ERD) models. At this point, the models are comprehensive and visually expressive\u2014but they still live in the world of design intent. The critical question now becomes: \u201cDoes the system actually work correctly under all realistic combinations of conditions, inputs, and events?\u201d This is the purpose of Scenario Analysis and Logic Validation: systematically breaking down complex use cases into concrete, testable scenarios; mapping every meaningful combination of conditions and decisions; and verifying that the specified behavior, edge cases, exceptions, and business rules are complete, consistent, and free of logical gaps or contradictions. This module focuses on two tightly integrated techniques: Scenario Breakdown \u2014 Converting narrative flows (especially those with multiple decisions, loops, or alternatives) into a clear set of distinct, named scenarios that cover happy paths, variations, and error conditions. Decision Matrix \/ Decision Table Analysis \u2014 Creating tabular representations that systematically enumerate all unique combinations of conditions (inputs, states, guards) against the corresponding actions\/outcomes, ensuring 100% coverage of decision logic. These activities are not optional polish\u2014they are essential quality gates. They expose hidden assumptions, missing cases, inconsistent rules, and ambiguous specifications before implementation begins, dramatically reducing costly rework during development and testing. Visual Paradigm\u2019s AI-Powered Use Case Modeling Studio supports this phase by: Automatically suggesting key decision points and branches from Activity & Sequence Diagrams Generating initial scenario lists and decision tables from use case flows Highlighting uncovered combinations or contradictory outcomes Allowing you to refine tables interactively (add conditions, merge equivalent rules, annotate business rationale) Exporting decision tables as test-case foundations (directly feeding into Module 7) Practical Examples Example 1: GourmetReserve \u2013 Use Case: Book a Table Key Decision Points Identified by AI: Is the diner authenticated? Are tables available for the requested time & party size? Is a deposit required (party size, time slot, loyalty status)? Does the diner provide a valid promo code? Does payment succeed? AI-Suggested Scenario Breakdown (partial list): Happy path \u2013 authenticated, tables available, deposit waived (Gold loyalty), no promo, payment succeeds \u2192 Reservation confirmed Happy path with promo \u2013 valid code applied \u2192 Reduced deposit No tables available \u2192 Offered waitlist \u2192 Diner joins waitlist Deposit required but payment fails (card declined) \u2192 Booking aborted, error shown Late cancellation attempt (within 2 hours) \u2192 No-show fee applied on cancellation Decision Table Generated by AI (simplified): Scenario Authenticated? Tables Available? Deposit Required? Valid Promo? Payment Succeeds? Outcome 1 Yes Yes No No \u2014 Confirmed (no deposit) 2 Yes Yes Yes Yes Yes Confirmed (discounted deposit) 3 Yes No \u2014 \u2014 \u2014 Waitlist offered 4 Yes Yes Yes No No Error: Payment failed 5 No \u2014 \u2014 \u2014 \u2014 Redirect to login Your refinement: Add column: \u201cPeak hours (Fri\/Sat 7\u20139 pm)?\u201d \u2192 Influences deposit requirement Add rule: If party size \u2265 12 \u2192 Manual approval required (new outcome: \u201cPending manager review\u201d) Example 2: SecureATM \u2013 Use Case: Withdraw Cash Decision Table (AI-generated excerpt): Scenario Authenticated? Amount \u2264 Daily Limit? Sufficient Funds? Sufficient Cash in ATM? Outcome 1 Yes Yes Yes Yes Cash dispensed, balance updated 2 Yes No Yes Yes Error: Exceeds daily limit 3 Yes Yes No Yes Error: Insufficient funds 4 Yes Yes Yes No Error: ATM temporarily unavailable 5 No \u2014 \u2014 \u2014 Prompt for PIN\/card retry Refinement added: New column: \u201cHigh-value transaction (> $1,000)?\u201d \u2192 Triggers biometric check (extra condition) Outcome variant: Biometric fails \u2192 Transaction aborted, card retained, fraud alert sent Example 3: CorpLearn \u2013 Use Case: Take Final Assessment Decision Table: Scenario All modules completed? Time not expired? Compliance acknowledgments done? Score \u2265 80%? Retakes remaining? Final Outcome 1 Yes Yes Yes Yes \u2014 Passed \u2192 Certificate issued 2 Yes Yes Yes No Yes Failed \u2192 Retake offered 3 Yes Yes Yes No No Failed \u2192 Course marked incomplete 4 Yes No \u2014 \u2014 \u2014 Auto-submitted \u2192 Score calculated 5 No \u2014 \u2014 \u2014 \u2014 Cannot start assessment Your refinement: Add condition: \u201cMandatory data privacy question answered correctly?\u201d \u2192 If No \u2192 Auto-fail regardless of score Merge equivalent rows where possible to keep table compact Why This Step Is Critical (Even with AI Models) AI-generated diagrams and specifications are excellent at following patterns, but they can miss: Domain-specific combinations (e.g., regulatory thresholds) Rare but catastrophic edge cases Contradictory business rules hidden across different flows Performance or concurrency implications of certain paths Scenario analysis and decision tables force explicit enumeration\u2014turning implicit assumptions into visible, reviewable logic. They directly become the foundation for comprehensive test cases (Module 7) and provide strong evidence of due diligence for audits or compliance reviews. By the end of Module 6, you will have: A curated set of named, prioritized scenarios covering normal, alternative, and exceptional behavior Decision tables that prove complete coverage of every meaningful condition combination Early detection (and resolution) of logical gaps, ambiguities, or inconsistencies This rigorous validation step ensures that the system doesn\u2019t just look good on paper\u2014it actually behaves correctly when subjected to real-world variability. 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