From d19a5bcd05f1ed4236d83991bf25fbad72fccb09 Mon Sep 17 00:00:00 2001
From: Pamela Dyer <pamela.dyer@uconn.edu>
Date: Sat, 25 Sep 2021 13:01:45 -0700
Subject: [PATCH] 2 into 3
---
...oduction_Platform_Customer_Interactions.mp | 59 ++++++++-
...ction_Platform_Supply_Chain_Interaction.mp | 47 ++++++-
...rrelation_and_Fusion_Process_Ungoverned.mp | 103 ++++++++-------
...and_Fusion_Process_with_Data_Governance.mp | 120 +++++++++++-------
.../Supply_Chain_with_Two_Cyber_Threats.mp | 81 ++++++++++--
models/Example31_Petri_Net.mp | 2 +-
.../Example32_ATMWithdrawal_StatechartView.mp | 2 +-
...ple33_FiniteStateDiagram_PathAnnotation.mp | 2 +-
...xample34_FiniteStateDiagram_PathDiagram.mp | 2 +-
.../Example35_Authentication_SystemReuse.mp | 2 +-
models/Example36_Compiler1_ComponentReuse.mp | 8 +-
models/Example37_Compiler2_ComponentReuse.mp | 8 +-
..._Merging_Root_Events_to_Reduce_Run_Time.mp | 2 +-
13 files changed, 317 insertions(+), 121 deletions(-)
diff --git a/models/Application_examples/Agile_Production_Platform_Customer_Interactions.mp b/models/Application_examples/Agile_Production_Platform_Customer_Interactions.mp
index def5c56..cc1830c 100644
--- a/models/Application_examples/Agile_Production_Platform_Customer_Interactions.mp
+++ b/models/Application_examples/Agile_Production_Platform_Customer_Interactions.mp
@@ -1,3 +1,60 @@
+/* Model of Agile Production Platform with Customer Interactions
+
+Created collaboratively in Teams by Kristin Giammarco,
+ Michael Collins, and E.Griffor on the 20th of
+ August, 2020.
+Modified collaboratively in Teams by Kristin Giammarco and
+ Joshua Beaver on the 17th of September, 2020.
+Edited by Pamela Dyer in September, 2021.
+
+Purpose: To illustrate a model that facilitates agreement
+on a baseline typical use case for a Customer placing an
+order using the "Agile Production Platform" (APP). Here
+the order is for face shields in response to the COVID-19
+pandemic.
+
+Description: This model demonstrates using MP to find both
+desired and undesired scenarios of Personal Protective
+Equipment (PPE) supply chains within the APP. The APP
+represents the linkage platform being designed to enable
+agile production / building of alternative supply chains.
+
+Specifically in this model, the focus is on the delivered
+quality of 5,000 medical face shields (a Customer placed
+the order using the APP). Running this model at Scope 1
+generates 4 event traces, described as follows:
+ Trace 1: The order is delivered to the Customer and
+ meets their expectations (Favorable Outcome).
+ Trace 2: The order is delivered to the Customer and
+ does not meet their expectations (Unfavorable
+ Outcome, as well as Unexpected Emergent Behavior
+ that manifested as a counterfeit part which made
+ its way into the APP supply chain).
+ Trace 3: The order is not delivered to the Customer
+ (Unfavorable Outcome, as well as Expected
+ Emergent Behavior).
+ Trace 4: The Customer is notified by the APP that the
+ order cannot be completed at this time because
+ no alternative supply chain can be found
+ (Unfavorable Outcome).
+
+References:
+Beaver, Joshua. "Analyzing Emergent Behavior of Supply
+Chains for Personal Protective Equipment in Response to
+COVID-19." NPS Master's Thesis, Monterey, CA:
+September 2021.
+
+Search terms: behavior, supply chain; coordination, event;
+SAY statement; trace annotation; event sharing; table;
+behavior, unexpected; behavior, emergent;
+behavior, agile production platform
+
+Instructions: Run for Scope 1.
+ Scope 1: 4 traces in less than 1 sec.
+
+==========================================================*/
+
+/****I have not edited anything between this next line and the SCHEMA line****/
/***********************************************************************************
Agile_Production_Platform_Customer_Interactions
@@ -105,7 +162,7 @@ some service provider?
Postcondition: The Customer receives 5,000 face shields (not shown in the model yet).
*************************************************************************************/
-
+/****Up to this line just above should be moved up/edited??****/
SCHEMA Agile_Production_Platform_Customer_Interactions
diff --git a/models/Application_examples/Agile_Production_Platform_Supply_Chain_Interaction.mp b/models/Application_examples/Agile_Production_Platform_Supply_Chain_Interaction.mp
index dc317a9..e9e777f 100644
--- a/models/Application_examples/Agile_Production_Platform_Supply_Chain_Interaction.mp
+++ b/models/Application_examples/Agile_Production_Platform_Supply_Chain_Interaction.mp
@@ -1,5 +1,48 @@
-/* Author: Josh Beaver
-*/
+/* Model of Agile Production Platform with Supply Chain Interaction
+
+Created collaboratively in Teams by Kristin Giammarco,
+ Michael Collins, and E.Griffor on the 20th of
+ August, 2020.
+Modified collaboratively in Teams by Kristin Giammarco and
+ Joshua Beaver on the 17th of September, 2020.
+Edited by Pamela Dyer in September, 2021.
+
+Purpose: To illustrate a model that focuses on the
+interrelations of supply chains with the "Agile Production
+Platform" (APP) as the intermediary.
+
+Description: This model demonstrates using MP to find both
+desired and undesired scenarios resulting from interactions
+among supply chains. Raw material suppliers, component
+suppliers, finished product suppliers, the customer, and
+the APP are all taken into account. In general, the goal
+of the model is to check that each phase of supplier is
+able to produce the expected product. Here the product
+is a general term. When run at Scope 1, there are 55 traces
+generated. An example of Expected Emergent Behavior coming
+out of this is: the component supplier was still at maximum
+capacity even though the APP pooled more suppliers at all
+levels of production, so the order was not fulfilled (an
+Unfavorable Outcome). An example of Unexpected Emergent
+Behavior coming out of this model is: a nonconforming
+material still making its way to the customer, either
+through a miscommunication or lack of inspection (also an
+Unfavorable Outcome).
+
+References:
+Beaver, Joshua. "Analyzing Emergent Behavior of Supply
+Chains for Personal Protective Equipment in Response to
+COVID-19." NPS Master's Thesis, Monterey, CA:
+September 2021.
+
+Search terms: behavior, supply chain; coordination, event;
+behavior, unexpected; behavior, emergent;
+behavior, agile production platform
+
+Instructions: Run for Scope 1.
+ Scope 1: 55 traces in less than 1 sec.
+
+==========================================================*/
SCHEMA Agile_Production_Platform_Supply_Chain_Interaction
diff --git a/models/Application_examples/Correlation_and_Fusion_Process_Ungoverned.mp b/models/Application_examples/Correlation_and_Fusion_Process_Ungoverned.mp
index 61e35ba..5e51d09 100644
--- a/models/Application_examples/Correlation_and_Fusion_Process_Ungoverned.mp
+++ b/models/Application_examples/Correlation_and_Fusion_Process_Ungoverned.mp
@@ -1,48 +1,65 @@
-/* Ungoverned Correlation Model August 2021
- created by F Watson 8/20/2021
-
- Purpose:
- To model AIS reporting data flows showing existing data path behaviors
-
- Description:
- Models the data flow from sensors all the way to the human analyst at a tactical location.
-
- Details
- AIS_Sensor: represents an example AIS GPS SENSOR
- Aggregation_Service: Represents a local AIS Aggregator like a "Radar" system or local AIS Broadcast
- Global_Distribution_Mechanism: Receives data from multiple aggregation services, coalates the data, and rebroadcasts the data globally.
-
- Source A: Represents raw AIS tracks that have not been modified in any way prior to dissemination
- Source B: Represents raw AIS tracks grouped into geographic areas for limited broadcast
- Source C: Represents Custom filtering and combining of AIS tracks based on unique fields
- Correlator functions: Receive_Source_Status
- Process_Source_Status
+/* Model of Correlation and Fusion Process Ungoverned
+
+Created by Frank Watson on the 20th of August, 2021.
+Edited by Pamela Dyer in September, 2021.
+
+Purpose: To model AIS reporting data flows showing existing
+data path behaviors.
+
+Description: Models the data flow from sensors all the way
+to the human analyst at a tactical location.
+
+ Details
+ AIS_Sensor: represents an example AIS GPS SENSOR
+ Aggregation_Service: Represents a local AIS Aggregator
+ like a "Radar" system or local AIS Broadcast
+ Global_Distribution_Mechanism: Receives data from
+ multiple aggregation services, coalates the data,
+ and rebroadcasts the data globally.
+
+ Source A: Represents raw AIS tracks that have not been
+ modified in any way prior to dissemination
+ Source B: Represents raw AIS tracks grouped into
+ geographic areas for limited broadcast
+ Source C: Represents Custom filtering and combining
+ of AIS tracks based on unique fields
+ Correlator functions: Receive_Source_Status
+ Process_Source_Status
Evaluate_Source_Status
Display_Correlated_Tracks
- Analyst functions: View_Correlated_Tracks
- Analyze_Tracks_Position_Accuracy
- Correct_Track_Positions
-
- The events are representative of actual AIS data report flow to operational analysts in the Fleet today.
-
- References:
-
- Example 49 Histogram showing number of traces with probabilities within certain intervals, from Auguston, M. "Monterey Phoenix
- System and Software Architecture and Workflow Modeling Language Manual"
- (Version 4). 2020. Available online: https://wiki.nps.edu/display/MP/Documentation
-
- Example 40. A global report is assembled from the set of all available event traces, from Auguston, M. "Monterey Phoenix
- System and Software Architecture and Workflow Modeling Language Manual"
- (Version 4). 2020. Available online: https://wiki.nps.edu/display/MP/Documentation
-
- Search terms: behavior, data correlation and fusion; probability, Type 1; data governance
-
- Instructions:
- Run for scope 1: 8 traces in < 1 sec
-
-
-
-*/
+ Analyst functions: View_Correlated_Tracks
+ Analyze_Tracks_Position_Accuracy
+ Correct_Track_Positions
+
+ The events are representative of actual AIS data report
+ flow to operational analysts in the Fleet today.
+
+References:
+Watson, Frank. "Design Methodologies for 21st Century
+ Battlefield Object Correlation and Fusion." NPS
+ Master's Thesis, Monterey, CA: September 2021.
+
+Example 49. Histogram showing number of traces with
+ probabilities within certain intervals, from Auguston,
+ M. "Monterey Phoenix System and Software Architecture
+ and Workflow Modeling Language Manual" (Version 4).
+ 2020. Available online:
+ https://wiki.nps.edu/display/MP/Documentation
+
+Example 40. A global report is assembled from the set of
+ all available event traces, from Auguston, M.
+ "Monterey Phoenix System and Software Architecture
+ and Workflow Modeling Language Manual" (Version 4).
+ 2020. Available online:
+ https://wiki.nps.edu/display/MP/Documentation
+
+Search terms: behavior, data correlation and fusion;
+probability, Type 1; data governance
+
+Instructions: Run for Scope 1.
+ Scope 1: 8 traces in less than 1 sec.
+
+==========================================================*/
SCHEMA Correlation_and_Fusion_Process_Ungoverned
diff --git a/models/Application_examples/Correlation_and_Fusion_Process_with_Data_Governance.mp b/models/Application_examples/Correlation_and_Fusion_Process_with_Data_Governance.mp
index 6c10f07..51d14e9 100644
--- a/models/Application_examples/Correlation_and_Fusion_Process_with_Data_Governance.mp
+++ b/models/Application_examples/Correlation_and_Fusion_Process_with_Data_Governance.mp
@@ -1,52 +1,78 @@
-/* Applied Data Governance Framework Model
-created by F Watson 8/20/2021
-
- Purpose:
- To model AIS reporting data flows showing updated data path behaviors with the application of a data governance framework
-
- Description:
- Models the data flow from sensors all the way to the human analyst at a tactical location incorporating a data governance framework.
-
- Details
- AIS_Sensor: represents an example AIS GPS SENSOR
- Aggregation_Service: Represents a local AIS Aggregator like a "Radar" system or local AIS Broadcast
- Global_Distribution_Mechanism: Receives data from multiple aggregation services, coalates the data, and rebroadcasts the data globally.
-
- Source A: Represents raw AIS tracks that have not been modified in any way prior to dissemination
- Source B: Represents raw AIS tracks grouped into geographic areas for limited broadcast
- Source C: Represents Custom filtering and combining of AIS tracks based on unique fields
-
- Data Governance Framework applies evaluation rubric to data sources and only allows sources that are uncorrupted to be transmitted to the correlator.
- The Data_Governance_Framework receivees sources, evaluates source corruption levels, applies a rubric evaluation, and transmits approved sources to the correlator function
-
-
- Correlator functions: Receive_Source_Status
- Process_Source_Status
+/* Model of Correlation and Fusion Process with Data Governance Framework
+
+Created by Frank Watson on the 20th of August, 2021.
+Edited by Pamela Dyer in September, 2021.
+
+Purpose: To model AIS reporting data flows showing updated
+data path behaviors with the application of a data
+governance framework.
+
+Description: Models the data flow from sensors all the way
+to the human analyst at a tactical location incorporating
+a data governance framework.
+
+ Details
+ AIS_Sensor: represents an example AIS GPS SENSOR
+ Aggregation_Service: Represents a local AIS Aggregator
+ like a "Radar" system or local AIS Broadcast
+ Global_Distribution_Mechanism: Receives data from
+ multiple aggregation services, coalates the data,
+ and rebroadcasts the data globally.
+
+ Source A: Represents raw AIS tracks that have not been
+ modified in any way prior to dissemination
+ Source B: Represents raw AIS tracks grouped into
+ geographic areas for limited broadcast
+ Source C: Represents Custom filtering and combining
+ of AIS tracks based on unique fields
+
+ Data Governance Framework applies evaluation rubric
+ to data sources and only allows sources that are
+ uncorrupted to be transmitted to the correlator.
+ The Data_Governance_Framework receives sources,
+ evaluates source corruption levels, applies a rubric
+ evaluation, and transmits approved sources to the
+ correlator function.
+
+ Correlator functions: Receive_Source_Status
+ Process_Source_Status
Evaluate_Source_Status
Display_Correlated_Tracks
- Analyst functions: View_Correlated_Tracks
- Analyze_Tracks_Position_Accuracy
- Correct_Track_Positions
-
- The events are representative of actual AIS data report flow to operational analysts, with the insertion of a data governance framework.
-
- References:
-
- Example 49 Histogram showing number of traces with probabilities within certain intervals, from Auguston, M. "Monterey Phoenix
- System and Software Architecture and Workflow Modeling Language Manual"
- (Version 4). 2020. Available online: https://wiki.nps.edu/display/MP/Documentation
-
- Example 40. A global report is assembled from the set of all available event traces, from Auguston, M. "Monterey Phoenix
- System and Software Architecture and Workflow Modeling Language Manual"
- (Version 4). 2020. Available online: https://wiki.nps.edu/display/MP/Documentation
-
- Search terms: behavior, data correlation and fusion; probability, Type 1; data governance
-
- Instructions:
- Run for scope 1: 4 traces in < 1 sec
-
-
-*/
+ Analyst functions: View_Correlated_Tracks
+ Analyze_Tracks_Position_Accuracy
+ Correct_Track_Positions
+
+ The events are representative of actual AIS data report
+ flow to operational analysts, with the insertion of a
+ data governance framework.
+
+References:
+Watson, Frank. "Design Methodologies for 21st Century
+ Battlefield Object Correlation and Fusion." NPS
+ Master's Thesis, Monterey, CA: September 2021.
+
+Example 49. Histogram showing number of traces with
+ probabilities within certain intervals, from Auguston,
+ M. "Monterey Phoenix System and Software Architecture
+ and Workflow Modeling Language Manual" (Version 4).
+ 2020. Available online:
+ https://wiki.nps.edu/display/MP/Documentation
+
+Example 40. A global report is assembled from the set of
+ all available event traces, from Auguston, M.
+ "Monterey Phoenix System and Software Architecture
+ and Workflow Modeling Language Manual" (Version 4).
+ 2020. Available online:
+ https://wiki.nps.edu/display/MP/Documentation
+
+Search terms: behavior, data correlation and fusion;
+probability, Type 1; data governance
+
+Instructions: Run for Scope 1.
+ Scope 1: 4 traces in less than 1 sec.
+
+==========================================================*/
+
SCHEMA Correlation_and_Fusion_Process_with_Data_Governance
diff --git a/models/Application_examples/Supply_Chain_with_Two_Cyber_Threats.mp b/models/Application_examples/Supply_Chain_with_Two_Cyber_Threats.mp
index 8240310..316a18b 100644
--- a/models/Application_examples/Supply_Chain_with_Two_Cyber_Threats.mp
+++ b/models/Application_examples/Supply_Chain_with_Two_Cyber_Threats.mp
@@ -1,18 +1,71 @@
-/*
-Two-Cyber-Threats-Model
+/* Model of Supply Chain with Two Cyber Threats
+
Based on the "Baseline Supply Chain" model
-created by Nathaniel Alden with help from Rachel Talkington 2020-07-05
-updated by Nathaniel Alden and Kristin Giammarco 2020-12-16
-modified by Margaret Palmieri, added outcomes
-modified by Margaret Palmieri, added risk and probability
-modified by Kristin Giammarco 2021-01-14, regrouped attributes and added global report
-modified by Mikhail Auguston 2021-01-14, added calculations to global report
-modified by Kristin Giammarco 2021-02-25, cleaned up comments
-modified by Margaret Palmieri 2021-07-29, added Pipeline use case, updated global report for two scenarios
-modified by Kristin Giammarco 2021-08-01, revised global report model for two scenarios
-modified by Margaret Palmieri 2021-08-03, updated attributes and model notes
-curated for model collection by Kristin Giammarco and Pamela Dyer, 2021-09-20
-*/
+Created by Nathaniel Alden with help from Rachel Talkington
+ on the 5th of July, 2020.
+Updated by Nathaniel Alden and Kristin Giammarco on the
+ 16th of December, 2020.
+Modified by Margaret Palmieri, added outcomes.
+Modified by Margaret Palmieri, added risk and probability.
+Modified by Kristin Giammarco on the 14th of January, 2021,
+ regrouped attributes and added global report.
+Modified by Mikhail Auguston on the 14th of January, 2021,
+ added calculations to global report.
+Modified by Kristin Giammarco on the 25th of February, 2021,
+ cleaned up comments.
+Modified by Margaret Palmieri on the 29th of July, 2021,
+ added Pipeline use case, updated global report for
+ two scenarios.
+Modified by Kristin Giammarco on the 1st of August, 2021,
+ revised global report model for two scenarios.
+Modified by Margaret Palmieri on the 3rd of August, 2021,
+ updated attributes and model notes.
+Curated for model collection by Kristin Giammarco and
+ Pamela Dyer on the 20th of September, 2021.
+
+Purpose: To illustrate the modeling of a supply chain
+that is threatened by two separate cyber-attacks, and to
+demonstrate calculating individual and total risk scores.
+
+Description: This model demonstrates performing detailed
+risk analysis on a supply chain potentially affected by
+two cyber threats. This is made possible by combining two
+MP models that each contain one threat: a cyber-attack on
+the barge, and a cyber-attack on the Colonial Pipeline.
+All three of these models can be found in (Palmieri 2021).
+
+This model first takes advantage of separate COORDINATE
+statements to calculate and display risk associated with
+each use case individually, and then performs calculations
+for the total risk to the supply chain from both cyber
+threats. Number attributes are used to determine both the
+likelihood and impact factors for each case, and then
+trace_risk_score combines them to generate the total risk
+to the supply chain from both cyber threats. Each trace
+that is above a certain threshold value is marked, and a
+global risk report is issued. This report contains the
+total risk across the total number of traces, highest risk,
+average risk, and a direction to sort traces by those
+marked first. Decision makers can reference models such as
+this when in need of assessing and comparing risk across
+multiple scenarios from multiple threats, especially when
+it is necessary to best allocate limited resources for
+investing in cyber security.
+
+References:
+Palmieri, Margaret. "Assessing and Visualizing Risk in
+Monterey Phoenix Through a Supply Chain Cyber-Attack Use
+Case." NPS Master's Thesis, Monterey, CA: September 2021.
+
+Search terms: behavior, supply chain; coordination, event;
+cyber threat; cyber-attack; event attribute, number; table;
+trace annotation; risk score; risk analysis; SAY statement;
+MARK command; report, global
+
+Instructions: Run for Scope 1.
+ Scope 1: 12 traces in less than 1 sec.
+
+==========================================================*/
SCHEMA Supply_Chain_with_Two_Cyber_Threats
diff --git a/models/Example31_Petri_Net.mp b/models/Example31_Petri_Net.mp
index d5aaaf5..200d5fa 100644
--- a/models/Example31_Petri_Net.mp
+++ b/models/Example31_Petri_Net.mp
@@ -1,4 +1,4 @@
-/* Example 32. Model of Petri Net
+/* Example 31. Model of Petri Net
Purpose: To demonstrate the modeling of a Petri net in MP.
diff --git a/models/Example32_ATMWithdrawal_StatechartView.mp b/models/Example32_ATMWithdrawal_StatechartView.mp
index fcd9a0c..b338aa4 100644
--- a/models/Example32_ATMWithdrawal_StatechartView.mp
+++ b/models/Example32_ATMWithdrawal_StatechartView.mp
@@ -1,4 +1,4 @@
-/* Example 33. Model of ATM Withdrawal with Statechart
+/* Example 32. Model of ATM Withdrawal with Statechart
Purpose: To demonstrate how to extract a Statechart view
from an MP model.
diff --git a/models/Example33_FiniteStateDiagram_PathAnnotation.mp b/models/Example33_FiniteStateDiagram_PathAnnotation.mp
index bdac758..2d6f049 100644
--- a/models/Example33_FiniteStateDiagram_PathAnnotation.mp
+++ b/models/Example33_FiniteStateDiagram_PathAnnotation.mp
@@ -1,4 +1,4 @@
-/* Example 34. Model of Finite State Diagram with Path Annotation
+/* Example 33. Model of Finite State Diagram with Path Annotation
Purpose: To demonstrate how to 1) model the behavior of a
finite state diagram and 2) generate event traces as
diff --git a/models/Example34_FiniteStateDiagram_PathDiagram.mp b/models/Example34_FiniteStateDiagram_PathDiagram.mp
index d83d015..006036c 100644
--- a/models/Example34_FiniteStateDiagram_PathDiagram.mp
+++ b/models/Example34_FiniteStateDiagram_PathDiagram.mp
@@ -1,4 +1,4 @@
-/* Example 35. Model of Finite State Diagram with Path Diagram
+/* Example 34. Model of Finite State Diagram with Path Diagram
Purpose: To demonstrate on a model of finite state diagram
behavior how to generate 1) path diagrams on each event trace
diff --git a/models/Example35_Authentication_SystemReuse.mp b/models/Example35_Authentication_SystemReuse.mp
index 2d498a5..250b2a4 100644
--- a/models/Example35_Authentication_SystemReuse.mp
+++ b/models/Example35_Authentication_SystemReuse.mp
@@ -1,4 +1,4 @@
-/* Example 36. Model of Authentication System
+/* Example 35. Model of Authentication System
Purpose: To demonstrate behavior reuse with the MAP
composition operation. Authentication system's behavior
diff --git a/models/Example36_Compiler1_ComponentReuse.mp b/models/Example36_Compiler1_ComponentReuse.mp
index 121a3c0..04f261b 100644
--- a/models/Example36_Compiler1_ComponentReuse.mp
+++ b/models/Example36_Compiler1_ComponentReuse.mp
@@ -1,16 +1,16 @@
-/* Example 37. Model of Compiler in Batch Processing Mode
+/* Example 36. Model of Compiler in Batch Processing Mode
Purpose: To demonstrate component reuse between models,
emphasizing the advantages of separation between the
specification of component behavior and the specification
of interactions between components.
-Description: Examples 37 and 38 are models of a bottom-up
+Description: Examples 36 and 37 are models of a bottom-up
parser with lexical analyzer based on regular expression
matching. It models the behavior of Lex/Yacc generated
compiler’s front end. This model represents an architecture
where lexer stores tokens in the intermediary data structure
-before parser starts to access it, and Example 38 is a model
+before parser starts to access it, and Example 37 is a model
where parser works with the lexer interactively. The Lexer
part models the behavior of a typical Lex machine. The
behavior of Stack is integrated into Parser’s behavior.
@@ -27,7 +27,7 @@ and the rest of the MP model. Interactions in MP
descriptions (event grammar rules), and such adjustment
can be done in a declarative fashion by coordinating the
reusable MP code and the MP code under development.
-Examples 37 and 38 show how it can be done using a model
+Examples 36 and 37 show how it can be done using a model
of a compiler.
References:
diff --git a/models/Example37_Compiler2_ComponentReuse.mp b/models/Example37_Compiler2_ComponentReuse.mp
index 975d1d8..65eacd7 100644
--- a/models/Example37_Compiler2_ComponentReuse.mp
+++ b/models/Example37_Compiler2_ComponentReuse.mp
@@ -1,14 +1,14 @@
-/* Example 38. Model of Compiler in Interactive Mode
+/* Example 37. Model of Compiler in Interactive Mode
Purpose: To demonstrate component reuse between models,
emphasizing the advantages of separation between the
specification of component behavior and the specification
of interactions between components.
-Description: Examples 37 and 38 are models of a bottom-up
+Description: Examples 36 and 37 are models of a bottom-up
parser with lexical analyzer based on regular expression
matching. It models the behavior of Lex/Yacc generated
-compiler’s front end. Example 37 represents an
+compiler’s front end. Example 36 represents an
architecture where lexer stores tokens in the intermediary
data structure before parser starts to access it, and this
is a model where parser works with the lexer interactively.
@@ -26,7 +26,7 @@ and the rest of MP model. Interactions in MP (coordination
operations) are separated from the behavior descriptions
(event grammar rules), and such adjustment can be done in
a declarative fashion by coordinating the reusable MP code
-and the MP code under development. Examples 37 and 38 show
+and the MP code under development. Examples 36 and 37 show
how it can be done using a model of a compiler.
References:
diff --git a/models/Example38_Merging_Root_Events_to_Reduce_Run_Time.mp b/models/Example38_Merging_Root_Events_to_Reduce_Run_Time.mp
index a5dfc94..ad6f22b 100644
--- a/models/Example38_Merging_Root_Events_to_Reduce_Run_Time.mp
+++ b/models/Example38_Merging_Root_Events_to_Reduce_Run_Time.mp
@@ -1,4 +1,4 @@
-/* Example 39. Model of Merging Root Events to Reduce Run Time
+/* Example 38. Model of Merging Root Events to Reduce Run Time
Purpose: To demonstrate how to organize a hierarchy of
derivations in order to reduce run time for larger models.
--
GitLab