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id	Vol-3170/paper1
wikidataid	Q117351500→Q117351500
title	Formal Specification and Validation of a Data-driven Software System for Fire Risk Prediction
pdfUrl	https://ceur-ws.org/Vol-3170/paper1.pdf
dblpUrl	https://dblp.org/rec/conf/apn/StrandKP22
volume	Vol-3170→Vol-3170
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Formal Specification and Validation of a Data-driven Software System for Fire Risk Prediction

Formal Specification and Validation of a Data-driven
Software System for Fire Risk Prediction
Ruben D. Strand1 , Laure Petrucci2 and Lars M. Kristensen3
1
Department of Safety, Chemistry, and Biomedical laboratory sciences,
Western Norway University of Applied Sciences, Haugesund, Norway
2
LIPN, CNRS UMR 7030, Université Sorbonne Paris Nord, Villetaneuse, France
3
Department of Computer science, Electrical engineering and Mathematical sciences,
Western Norway University of Applied Sciences, Bergen, Norway

Abstract
Long periods of dry and cold weather conditions significantly increase fire risks for wooden buildings.
Recent advances in predictive fire risk models combined with publicly available cloud-based weather
data services have enabled the development of smart software systems for location-oriented fire risk
notification. We have developed a Coloured Petri Net (CPN) model specifying the software architec-
ture of a microservice-based predictive fire risk notification system. The CPN model captures the set
of micro-services provided via REST APIs and the interaction between the constituent services for lo-
cation tracking and subscription, fire risk computation, and data harvesting. As part of the work, we
present a general modelling approach and pattern for REST-based APIs. We apply simulation and state
space exploration to validate and verify key behavioural properties of the predictive fire risk notification
system.

Keywords
Coloured Petri nets, modelling, verification, microservices and REST APIs, software systems architec-
ture, fire risk prediction

1. Introduction
The pervasive presence of cloud-based data services provides access to a wide range of data
sources that enable the development of data-driven applications supporting decision making
and control. Prominent examples of such data sources includes weather data measurements and
weather forecasts which can be used by authorities in the context of early warning systems,
including smart systems for fire risk predictions.
The winter period in certain regions of Norway is characterised by longer periods with dry
and cold weather conditions which combined with the high density of wooden houses poses a
significant threat to many cities in Norway. An example of this was the incident on January 18th
2014, when at that date, the largest fire in Norway since World War II developed in the village
of Lærdalsøyri at night-time [1]. Forty buildings were lost, including four historic buildings, in
a fire that only developed between houses [2].
It is generally known that the winter in cold climate regions brings along increased fire
frequencies [1, 3]. This was originally identified by Pirsko and Fons [4] which suggested

PNSE’22, International Workshop on Petri Nets and Software Engineering, Bergen, Norway, 2022
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ambient dew point during the winter as an explanation for the increased fire frequency in
buildings. More recently, Log [5] proposed indoor relative humidity as a fire risk indicator and
developed a mathematical model [6] that predicts indoor fuel moisture content (FMC) for a
wooden house. The model correlates the FMC with the time to flash-over (TTF) [7], resulting
in the possibility of indicating fire development. Combined with forecast weather data, the
TTF can be predicted for the upcoming days, enabling proactive emergency planning. Further,
by combining the TTF with the influence of wind, a combined fire risk can be expressed, as
presented in [8]. The basic idea underlying the model of Log is to use outdoor temperature
and relative humidity to estimate (compute) indoor relative humidity which in turn enables
computation of the wooden fuel moisture content (FMC). In Norway, measured weather data
and weather forecast data (including temperature and relative humidity) are available via
cloud-based REST APIs of the Norwegian Meteorological Institute (MET) [9, 10].
The work presented in this paper is conducted in the context of the DYNAMIC research
project [11] which has as a main objective to develop a cloud-based predictive fire risk indication
system based upon the fire risk prediction models developed within the project. In earlier work,
we have validated the predictive fire risk indication model [12] demonstrating that it can provide
trustworthy fire risk indications, that a combination of measurements and forecast data can
be used, and that the weather data available from MET [9, 10] can be used to obtain fire risk
indications of the correct order of magnitude (minutes). Furthermore, in earlier work [12] we
implemented a simple first version of the system, which served as a basis for the development
of a prototype of the fire risk notification system based on a micro-service software architecture.
This prototype was developed through an iterative process in parallel with developing a first
version of the CPN model. However, the development of the CPN model was based on the
prototype system, hence developed a bit behind the prototype. This resulted in a supportive
development process, as the CPN modelling continuously revealed problems and identified
possible pitfalls within the prototype. This assured a more robust and complete development
of the system. The prototype system and the CPN model were developed by different people,
which was considered to strengthen the development process. The prototype implementation
was implemented using the Heroku [13] and Amazon EC2 cloud platforms and the Spring Boot
framework [14] in combination with MongoDB noSQL databases to realise the micro-services.
In addition, a web-based front-end application was prototyped using the React single-page
web application framework, and a mobile front-end was prototyped using the Xamarin cross-
platform application framework [15]. The current paper reports on a second version of the CPN
model, which took place after the supportive development process. The updated CPN model
will serve as basis for the final system implementation.

Contribution. The contribution of this paper is three-fold: Firstly, to take a step back from
the initial prototype implementation and develop a formal specification of the software system
architecture and the constituent micro-services ; Secondly, to develop some general modelling
patterns for REST-based micro-services ; and thirdly to verify the system services and the
behavioural interaction between the micro-services. A main benefit of the constructed CPN
model is that it provides an implementation independent specification of the fire risk notification
system and because of the initial prototyping we ensure the implementability of the system.

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Related work. Several works in the recent literature have addressed formal modelling of
(micro-) services. Most of these works do not benefit from the data handling offered by Coloured
Petri Nets nor provide a structured hierarchical design.
In [16], services are modelled using Timed Petri Nets (TPNs), thus focusing on the process flow
and discarding the data exchanges. An automatically generated TPN then allows for verifying
properties of an input microservice specified in the CONDUCTOR orchestration language. A
similar approach for self-adaptive systems using high-level Petri nets was conveyed in [17].
The Saga patterns are extended with concurrency features in [18] via workflow nets. They are
further translated into reference nets embedded in the RENEW tool which provides simulation
features. It also suffers from the restricted data representation and analysis capabilities. Services
are organised using simple Petri net patterns in [19]. In [20], the authors propose a Coloured
Petri net model for RESTful services, with a particular focus on composition issues. However, it
does not exhibit a hierarchical model nor a general architecture for micro-services models.
A CPN-based case-study verification of a cloud-based information integration architecture
was presented in [21]. Although modelling the different layers of the cloud-based architecture
and verifying specific model properties, emphasis was not given to the communication, data
exchange, between the layers. However, [22] presents a structuring of data and color sets
which has similarities with the general communication between the REST-based micro-services
presented in this paper. In their work, they considered the automatic generation of a CPN model
for a distributed automation architecture.

Overview. The rest of this paper is organised as follows. Section 2 gives an overview of
the fire risk notification system by presenting the top-level modules of the CPN model and
exemplifying the basic interaction between the micro-services when the system is being executed.
In Sections 3-5 we present how the three micro-services (business logic, fire risk computation,
and data harvesting) have been modelled, as well as our general approach to modelling REST-
based APIs. In Section 6 we formulate key behavioural properties for the fire risk notification
system and explain how they have been validated and verified using simulation and state space
exploration. Finally, in Section 7, we sum up conclusions and discuss future work.

2. Fire Risk Notification System and CPN Model Overview
The top-level CPN module of the system is presented in Figure 1. It consists of the substitu-
tion transitions Client Applications, Fire Risk Notification System (FRNS), External Weather Data
Services and associated socket places. The Client Applications represent any front-end service
used by the clients to communicate with the FRNS, i.e. a web browser or mobile application.
The FRNS represents the main fire risk notification system, consisting of 22 submodules. The
substitution transition External Weather Data Services is representing weather data sources
providing historical and forecast weather data.
The services communicate by producing and consuming tokens on the connected socket
places. The tokens represent data transferred between the services. Each place is associated
with a specific color set, allowing only a certain type or combination of data to be present.
Considering a request from the Client Applications, a token is produced onto the place Request,

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Figure 1: The top-level module of the CPN model, with the front-end client applications at the left,
the main software component FRNS in the middle, and the external weather data services at the right.

connecting this application with the FRNS. In turn, the FRNS consumes the token, processes the
request and responds by producing a token onto the place Response. The response token is then
consumed by the Client Applications. The FRNS either responds to a request from the Client
Applications, as described, or it notifies clients through scheduled services. This latter service
results in one or more tokens being produced at the place Notification. The remaining two places
in Figure 1 are the Data Request and Data Response. These are the places connecting the Fire
Risk Notification System and the External Weather Data Services. Tokens are produced at these
places whenever the FRNS needs historical and forecast weather data for fire risk computations.
Expanding on the system, Figure 2 shows the Fire Risk Notification System submodule. The
module consists of three substitution transitions and associated socket places. The substitution
transitions represent the individual services of Business Logic Controller Service (BLCS), The
Fire Risk Computation Service (FRCS) and The Data Harvesting Service (DHS). The services
are modelled as loosely coupled micro-services, communicating through defined REST API
endpoints, in accordance with the previously developed prototype [23]. The BLCS and DHS
micro-services are connected to the overall system in Figure 1 through the places with equal
naming. Hence, the BLCS communicates with the Client Applications and the FRCS, while the
DHS communicates with the external weather data sources and the FRCS. The services are only
aware of the existence of directly connected services.
The BLCS is the middleware, separating the Client Applications and the FRCS. It processes
and responds to requests from the clients by further requesting the FRCS. In addition to the
RESTful services, it also handles the fixed interval process of notifying clients in case of high
estimated fire risks. To receive a notification for a location, a client must be subscribed to that
location. The subscription database, handled by the BLCS, keeps track of these subscriptions.
The FRCS is responsible for estimating the fire risks at considered (tracked) locations, which
is primarily a scheduled service. Upon computing the fire risks, the FRCS requests weather data
from the DHS. Then, if weather data is received, the FRCS predicts the upcoming fire risks and
updates the local fire risk database. When requested by the BLCS, the FRCS can return estimated
fire risks from the local database. Hence, the fire risk database is continuously updated.

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Figure 2: The FRNS submodule, with the BLCS at the left, the FRCS in the middle and the DHS at the
right. The far left and right places correspond to the places in Figure 1.

The DHS receives weather data requests from the FRCS. It handles a weather database
containing locations and associated weather data. The database is subject to scheduled updates,
so when the FRCS requests weather data, the DHS can respond with recently fetched forecasts
and measurements for all the locations within the database. Hence, the DHS functions similarly
to the FRCS, by means of scheduled updates of local databases. The existence of locations is
synchronised between the databases.
Figure 3 presents a sequence diagram for the main functions of the system. In general,
clients may: (1) start or stop tracking of a new location; (2) subscribe or unsubscribe to existing
locations to handle personal notifications; or (3) request fire risk for locations existing within
the system. In order to provide fire risk notifications, the system needs to continuously monitor
the current and future fire risk. This is achieved by a scheduled update, running every six hours.
It starts by an update of the weather database at DHS, then the FRCS requests the recently
fetched weather data, recomputes all fire risks and updates the fire risk database. Then, the
BLCS requests all recently computed fire risks and determine which clients to notify, if any,
based on certain fire risk criteria and the subscriptions within the subscription database.

Abstraction of data. Abstractions are made to the model to arrive at an appropriate level of
detail. Although some details in data are useful for modelling in simulation purposes, they are
not necessarily relevant when doing verification. The included data need only to correspond
with the modelling objective. Further, abstraction of unnecessary data is important to reduce
the size of the state space, ensuring a finite space and allowing a more exhaustive verification.
The abstractions made to the CPN model primarily relate to the payload of the messages.
When the different services communicate, the purpose of the communication is more important

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Figure 3: A sequence diagram for selected requests and processes, herein tracking, subscription and
risk requests, as well as the scheduled update of databases and associated notification.

than the content, e.g., the verification only requires to distinguish between the absence of a
notification of fire risk or its sending, but not on the actual levels of risk. Hence, risk levels are

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abstracted and emphasis is given to properly capture the purpose of the interactions between
the constituent services. A consequence of this is that we also abstract from the actual values
related to weather data and only consider the absence or presence of weather data for locations.

3. Business Logic Controller Service
The BLCS which is modelled by the submodule of the Business Logic Controller Service substitu-
tion transition (see Fig. 2) consists of two substitution transitions and associated sub-modules,
as well as the subscription database. The two modules represent (1) the processing of client
requests and (2) the scheduled update and associated notifications. Figure 4 shows the module
responsible for the processing of client requests, the BLCS Handle Request. It is connected to the
FRNS module in Figure 2 through the places with similar naming. It contains three transitions
representing the different incoming client requests, that is, tracking, subscription and single
fire risk request. A request either results in an immediate response in the case of a subscription
request, or the requesting of further information from the FRCS in the case of tracking or single
fire risk request, as can be seen from Figure 4.

Figure 4: Presenting the sub-module responsible for handling requests within the BLCS, the BLCS
Handle Request. Represented through respective substitution transitions, from the top, the handling of
tracking, subscription and single fire risk requests.

Considering a request from the Client Applications, a token is produced onto the place Re-
quest, connecting this application with the BLCS. The colour set associated with this place
is ClientxFRNSRequest, which is a product colour set combining the index colour set Client

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1 colset Method = with GET | PUT | POST | DELETE;
2 colset StatusCode = with OK | CREATED | ACCEPTED | NOTFOUND;
3 colset Component = union BLCSTracking + BLCSUpdate + BLCSRequest;
4
5 val Cn = 2;
6 val Ln = 5;
7 colset Client = index Client with 1..Cn;
8 colset Location = index Loc with 1..Ln;
9 colset Locations = list Location;
10 colset ClientxLocation = product Client * Location;
11
12 colset FireRisk = with Risk | NA;
13 colset LocxFireRisk = product Location * FireRisk;
14 colset FireRisks = list LocxFireRisk;
15 colset FRCSResource = union FRCSFirerisks + FRCSLocation : Location;
16 colset FRNSResource = union FRNSLocation : Location + FRNSFirerisk : Location +
17 FRNSLocations : ClientxLocation + FRNSFirerisks : FireRisks;
18
19 colset FRNSRequest = record method : Method * resource : FRNSResource;
20 colset ClientxFRNSRequest = product Client * FRNSRequest;
21
22 colset FRNSResponse = record response : StatusCode * body : FRNSResource;
23 colset ClientxFRNSResponse = product Client * FRNSResponse;
24
25 colset FRCSReq = record method : Method * resource : FRCSResource;
26 colset FRCSRequest = product Component * FRCSReq;
27
28 colset FRCSResp = record response : StatusCode * body : FireRisks;
29 colset FRCSResponse = product Component * FRCSResp;

Figure 5: Colour sets definitions used to model the state of the BLCS.

with the record colour set FRNSRequest, as can be seen from Figure 5. Within the model, a
request includes the specification of a HTTP method as well as the resources needed to handle
the request. The resources are modelled through a union colour set, defining the REST API
endpoints (constructors) and the associated data (arguments). The resources are service specific,
defined for all interactions associated with the service. Hence, requests and responses use the
same resource within respective services. With respect to the place Request, the resource is
modelled by FRNSResource, and depending on the type of request and associated endpoint, the
carried data is either a location or a combination of a location and a client identifier.
The colour set ClientxFRNSResponse associated with place Response is similar to ClientxFRN-
SRequest, but instead makes use of the colour set FRNSResponse. Within the model, a response
is defined as a record set and includes a response and a body, associated with the colour sets
StatusCode and the aforementioned FRNSResource respectively. The response is used to indicate
the status post processing the request, while the body embeds response data, if any. At the place
Response, depending on the request, the resource is either a client and a location or a location
and a fire risk. For any request not specifically asking for a fire risk, the response (StatusCode)
represents the data needed by the client.

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If the BLCS needs to request the FRCS, a token is produced at the place FRCSRequest, while in
response, a token will be received at FRCSResponse. The colour sets associated with these places
have similar names as the places themselves, hence FRCSRequest and FRCSResponse. These
colour sets follow the same general structure as previously described for request and response
colour sets. However, distinguishing these union sets from the previously described colour
sets, is the inclusion of a Component. The Component identifies the type of request and from
where it originates. This can be used within the model to distinguish the different ongoing
interactions, as well as being useful when performing single-step and interactive simulations.
Then, the defined colour set becomes a union of the Component and the record set FRCSReq
or FRCSResp, as can be seen in Figure 5. The colour sets FRCSReq and FRCSResp follow the
previously presented design of requests and responses. The FRCSReq consists of a HTTP method
and the associated resource FRCSResource, while FRCSResp contains a response described by
the StatusCode and an associated body containing the explicitly stated FireRisks colour set.
To consider how a specific request is handled, Figure 6 presents the sub-module of Process
Tracking Request from Figure 4. At the top left, the familiar place Request can be seen, as well
as the other recently considered places at the remaining corners. Any client request, regardless
of its type, will occur at the top left. However, only the POST requests associated with resource
(endpoint) FRNSLocation will be considered within this module, hence the arc inscriptions. POST
requests are associated with clients requesting tracking of new locations. A requested location
tracking may either contain an unconsidered location or an existing location. Hence, guards at
the transitions connecting to the place Request consider whether the requested location already
exists. If the function hasLoc (has location) evaluates to true, it means the requested location
already exists within the subscription database and only the transition Request Location Exist
becomes enabled. If this is the case, a token is produced at the place Response, containing an
ACCEPTED response and the location in question. Similarly, the transition Process New Location
only becomes enabled if hasLoc is not true and there is an available token at place Idle.

Figure 6: The Process POST Request submodule. Presenting the BLCS handling of a tracking (POST)
request.

The Idle tokens are introduced to ensure the full consideration of a request or process, before
engaging the next. This is in accordance with the prototyped system as well as contributing to a

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reduced state space. When fired, the transition produces a token at FRCSRequest in accordance
with its associated colour set. As can be seen from the arc inscription, the token consists of the
component BLCSTracking, as well as the specified POST method and FRCSLocation resource.
When a tracking response is received at place FRCSResponse it contains the response CRE-
ATED, a body with the specific location and a FireRisk corresponding to NA (not available).
Together with the wait-token produced upon the firing of Process New Location, the FRCSRe-
sponse token enables the transition Process Response, which in turn may update the subscription
database through the function AddLocation, as well as producing a response to the place Re-
sponse. Note that it is evident from the component that the response originates from a tracking
request, hence the NA within the body of the response.

4. FireRisk Computation Service
The FRCS is responsible for the computation of fire risks and the handling of the FireRisk
DataBase. Fire risks are computed by use of weather data received from the DHS and computed
values are stored within the risk database, for later retrieval. The FRCS either responds to
requests from the BLCS or performs scheduled recomputations of the fire risks within the
database. Figure 7 presents the FRCS submodule, where the two substitution transitions FRC-
SProcessRequest and RecomputeFireRisks represent the processing of requests and scheduled
recomputations, respectively. The place FireRisk DataBase is associated with the colour set
FireRiskDB, which is a list of entries consisting of the combination of location and FireRisk. The
places DHSRequest and DHSResponse are constructed in accordance with the general design of
requests and responses. The associated resource is modelled by DHSResource, which based on
the endpoint (constructor), is either a location, a list of locations or a list of combinations of
location and weather data, as can be seen from Figure 8.

Figure 7: The FireRisk Computation Service submodule and associated substitution transitions and
database.

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Expanding the substitution transition FRCSProcessRequest gives the sub-module presented in
Figure 9. The module consists of two substitution transitions, representing the processing of
tracking requests (Process Tracking Request) and single fire risk requests (Process GET Request).
These are the only two types of requests processed by the FRCS, as the subscription requests
were limited to within the BLCS. If the recently considered tracking request from Section 3 is
considered, it would appear at the place FRCSRequest to be processed within the substitution
transition Process Tracking Request, which is similar to what was previously presented related
to Figure 6.

1 colset WeatherData = with FORECAST | MEASUREMENT | NOTPRESENT;
2 colset LocxWDxWD = product Location * WeatherData * WeatherData;
3 colset LocsxWDs = list LocxWDxWD;
4 var wd : LocsxWDs;
5 colset DHSResource = union DHSLocation : Location + DHSLocations : Locations +
6 DHSWD : LocsxWDs;
7 colset DHSRequest = record method : Method * resource : DHSResource;
8 colset DHSResponse = record status : StatusCode * body : DHSResource;

Figure 8: Colour set definitions used to model the state of the FRCS.

Figure 9: The sub-module of FRCS Process Request. It can be seen that the FRCS handles tracking
(POST) and single fire risk (GET) requests only.

The handling of the scheduled update of the FireRisk DataBase is presented in Figure 10 and
is the sub-module of the transition Recompute FireRisks from Figure 7. When firing, transition
Send Request requests weather data from the DHS, for all locations kept within the fire risk
database. The response received is a status OK and a list of locations and associated risks. The

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latter resource is represented by the DHSWD endpoint (constructor) and the variable wd, as can
be seen from the arc inscription and declarations in Figure 8. The received response results in
the update of the FireRisk DataBase through the updateFRDB function.

Figure 10: The sub-module Recompute FireRisks responsible for the scheduled update of the FireRisk
DataBase.

5. Data Harvesting Service
The DHS is responsible for retrieving weather data from external sources and handling the
Weather Data DataBase. The DHS either responds to requests from the FRCS or performs
scheduled updates of the weather data in the database by requesting external services. The data
fetched by the DHS is both historic and predicted weather data, requested from the FROST and
MET APIs of the Norwegian Meteorological Institute, respectively. Figure 11 presents the DHS
sub-module for processing requests. The three substitution transitions are Process POST Request,
Process GET Request and Process Delete Request. The POST request is related to the initiation of
location tracking and involves updating the local database, as previously described. The GET
request is a request for service specific data, in this case weather data needed by the FRCS to
perform fire risk computations. Yet unaddressed, is the delete request. Any subscription or
tracking of a location can be terminated, which involves deleting clients or locations from the
databases. Within the DHS, the delete request is only related to the termination of a tracking,
hence the deletion of a location within the weather database.
The DHS is responsible for harvesting weather data from the external services. Figure 12
presents the modelling of the weather data harvesting, through a single harvesting module.
The requesting of weather data from the two external services of FROST and MET, is modelled
through a single combined weather data request. In turn, this means that the weather data
response is a combined response, containing both historical and forecast weather data. The

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Figure 11: The sub-module responsible for handling requests within the DHS. The DHS either POST or
DELETE a location from its database, or fetches weather data from the Weather Data DataBase (GET).

Figure 12: The sub-module Harvest Weather Data, within the DHS, responsible for the harvesting of
weather data from external services. The modelling combines the requesting from two external services
into a single combined request.

tokens produced, associated with the communication with the external services, appear at
places Data Request and Data Response. The colour sets used are WSRequest and WSResponse

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1 colset WSResource = union Forecast : Locations + Measurement : Locations;
2 colset WSRequest = record method : Method * resource : WSResource;
3 colset WSResponse = record status : StatusCode * body : WSResource;
4
5 colset WSResource = union Forecast : Locations + Measurement : Locations;
6 colset WSResources = list WSResource;
7 colset WSRequest = record method : Method * resource : WSResources;
8 colset WSResponse = record status : StatusCode * body : WSResources;

Figure 13: Colour set definitions used to model the state of the DHS.

and follow the aforementioned request and response structure. The request contains a HTTP
method and a resource, while the response contains a status and a body. The resource is
WSResource and represent both forecast and historic weather data for one or more locations, as
can be seen from Figure 13. At last, a weather data response results in the update of weather
data within the database, which in turn can be requested by the FRCS.

6. Model Validation and Verification
To validate the constructed CPN model, we initially performed single-step execution combined
with interactive and automatic simulation. The aim of using simulation was to confirm that the
basic behaviour of the CPN model was as intended, including the interaction between micro-
services. Testing the CPN model using simulation identified a number of smaller modelling
errors which could easily be corrected.
To perform a more exhaustive verification of the CPN model, we report in this section on how
we have used the state space exploration facilities of CPN Tools in conjunction with the state-
and action-oriented ASK-CTL library [24, 25] to verify key behavioral properties of the fire risk
notification system using temporal logic. We adopted an incremental verification approach
similar to the one developed in [26], where we gradually verified the services of the fire risk
notification system. In particular, we started with the verification of the location tracking
service and then incrementally enabled subscription, fire risk computation, and data harvesting
until we finally considered the verification of some system-wide behavioural properties. In
the following paragraphs we present the behavioural properties considered for the different
services. We assume that the reader is familiar with the basic CTL temporal operators AG
(always/globally) and EF (reachable/exists future). In the specification of the CTL properties,
we use 𝐿 to denote the set of locations and 𝐶 to denote the set of clients.

Tracking properties. Clients send requests to start and stop the tracking of locations which
in turn determines the locations for which weather data is being harvested and for which fire
risks are being computed. For tracking we consider the following properties.

T-P1 ∀𝑐 ∈ 𝐶, 𝑙 ∈ 𝐿 : AG EF (𝑐 requests tracking of 𝑙). This property states that it is always
possible for any client to initiate the tracking of any location. The proposition 𝑐 requests

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tracking of 𝑙 can be implemented by considering the binding of the transition in the Client
module corresponding to the event of requesting a location to be tracked.

T-P2 ∀𝑐 ∈ 𝐶, 𝑙 ∈ 𝐿 : AG ((𝑐 requests tracking of 𝑙) ⇒ EF (𝑙 is being tracked)). This prop-
erty states that if a client requests tracking of a location, then there exists a future state
in which this location is being tracked. Checking the location can be implemented by
considering the tokens present on the places representing the databases of the three
micro-services.

T-P3 ∀𝑐 ∈ 𝐶, 𝑙 ∈ 𝐿 : AG EF (𝑐 stops tracking of 𝑙). This is the dual property of T-P1 for
stopping the tracking of locations.

T-P4 ∀𝑐 ∈ 𝐶, 𝑙 ∈ 𝐿 : AG (𝑐 stops tracking of 𝑙) ⇒ EF (𝑙 is not being tracked). This is the
dual property of T-P2 for stopping the tracking of locations.

Since some components in the CPN model are not synchronised, we consider the EF operator
in the properties rather than the stronger AF (always eventually) operator. Extending our work
to verify the stronger always eventually properties would require the application of fairness
assumptions. This could be achieved by e.g. using linear temporal logic (LTL) and encoding the
exclusion of non-fair executions in the formulae being verified. However, there is currently no
support for LTL modelling in CPN Tools.

Subscription properties. Clients subscribe to a location in order to receive fire risk notifica-
tions for that location. For subscription we consider the following properties.

S-P1 ∀𝑐 ∈ 𝐶, 𝑙 ∈ 𝐿 : AG EF (𝑐 requests subscription to 𝑙). This property states that it is
always possible for any client to request subscription to any location. The proposition 𝑐
requests subscription to 𝑙 can be implemented by considering the binding of the transition
in the Client module corresponding to the event of requesting a subscription.

S-P2 ∀𝑐 ∈ 𝐶, 𝑙 ∈ 𝐿 : AG ((𝑐 requests subscription to 𝑙) ⇒ EF (𝑐 is subscribed to 𝑙)). This
property states that if a client requests subscription to a location, then there exists a future
state in which the client is subscribed to that location. Checking the client subscription
to the location can be implemented by considering the tokens present on the Subscription
Database place (see Figure 2).

S-P3 ∀𝑐 ∈ 𝐶, 𝑙𝑜𝑐𝑠 ⊆ 𝐿 : AG EF (𝑐 is subscribed to all locations in 𝑙𝑜𝑐𝑠). This property states
that it is always possible for a client to be subscribed to any subset of locations.

S-P4 ∀𝑐 ∈ 𝐶 : AG (𝑐 is subscribed to a subset of 𝐿). This property ensures that a client
cannot have multiple subscriptions to the same location.

S-P5 ∀𝑐 ∈ 𝐶, 𝑙 ∈ 𝐿 : AG EF (𝑐 unsubscribes to 𝑙). This is the dual property of S-P1 but for
unsubscribing to a location.

S-P6 ∀𝑐 ∈ 𝐶, 𝑙 ∈ 𝐿 : AG ((𝑐 unsubscribes to 𝑙) ⇒ EF (𝑐 is not subscribed to 𝑙)). This is the
dual property of S-P2 but for unsubscribing to a location.

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Fire risk properties. Fire risk is to be computed by the fire risk computation service for a
given location when this location is being tracked. For the fire risk computation service, we
consider the following properties.

F-P1 ∀𝑙 ∈ 𝐿 : AG EF (firerisk is computed for 𝑙). This property states that for any location
it is always possible to compute the fire risk. The fire risk being computed for a given
location can be checked from the Firerisk Database place.

F-P2 ∀𝑙 ∈ 𝑙 : AG EF (firerisk recompute). This property states that it is always possible to
recompute the fire risk for any given location, and hence that fire risk can be periodically
recomputed. Re-computation of fire risks corresponds to the occurrence of the Recompute
FireRisks transition in Figure 10.

F-P3 ∀𝑐 ∈ 𝐶, 𝑙 ∈ 𝐿 : AG (c requests firerisk for 𝑙 ⇒ EF (c receives firerisk response for 𝑙)).
This property states that if a client 𝑐 requests a fire risk for a given location 𝑙, then it is
possible for 𝑐 to obtain the response.

Data harvesting properties. The data harvesting service is to retrieve weather data period-
ically for the currently tracked locations in order for the fire risk computation service to be able
to compute fire risks. For data harvesting we consider the following properties.

W-P1 ∀𝑙 ∈ 𝐿 : AG EF (weather data is stored for 𝑙). This property states that for any loca-
tion it is always possible to store weather data. That the weather data has been stored for
a given location can be checked from the Weatherdata Database place.

W-P2 ∀𝑙 ∈ 𝐿 : AG EF (weather data requested for 𝑙). This property states that it is always
possible to harvest data for a given location. The harvesting of data for a location
corresponds to an occurrence of the SendWeatherDataRequests transition in Figure 12.

Inter-service properties. In addition to the properties related to specific services above, we
also consider the following properties which rely on the collaboration between all micro-services
in the system.

A-P1 AG (no pending client requests ⇒ consistent data bases). This property states that if
no client is currently executing requests against the service, then the databases of the
micro-services are consistent in terms of storing information for the same set of locations.
That the databases are consistent can be checked by considering the markings of the
three places representing information stored in the databases (see Figure 2).

A-P2 ∀𝑐 ∈ 𝐶, 𝑙 ∈ 𝐿 : AG (𝑐 is subscribed to 𝑙) ⇒ EF (𝑐 receives fire risk notification for 𝑙).
This property states that if a client is subscribed to a location, then it is possible for the
client to receive fire risks for that location. That a client is subscribed to a location can be
retrieved from the place representing the subscription database while the reception of a
notification is represented by the occurrence of the corresponding transition in the Client
module.

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Experimental results. We verified the properties presented above for selected configurations
of the CPN model in terms of locations, clients, tracked locations, and subscriptions.
Table 1 summarises the statistics for the state space exploration and verification of properties.
The States and Arcs columns give the number of states and edges, respectively, in the state
space. The G-Time column provides the time (in seconds) used to generate the state space for
the given configuration while the V-Time column lists the time (in seconds) used for verification
of properties. The verification was undertaken on i5-PC 2.4 GHz PC with a 16Gb memory.
We use 𝐶𝑥 − 𝐿𝑦 to denote a configuration with 𝑥 clients and 𝑦 locations. A * indicates that
we have fixed the tracking and subscription such that all locations are tracked and all clients
subscribe to all locations. We use Request to specify configurations where we only consider
single requests from clients and Notify to specify configurations where we consider only the
system initiated notifications (and not individual client requests). It can be observed that for
configurations with only notification, the size of the state space does not grow when increasing
the number of locations. This is due to the subscriptions being fixed and the fact that a client is
notified about all subscribed locations in one single message.

Table 1
State space and verification statistics for configurations considered in the verification.

Configuration States Arcs G-Time V-Time
C1-L1 3,435 11,901 <1s <1s
C1-L2 215,181 739,797 2,093 s 555 s
C2-L1 274,581 1,238,395 9,100 s 1,404 s

C2-L2-* 14,556 62,535 27.7 s 26.6 s

C2-L3-*-Request 5,151 21,138 4s 10.5 s
C2-L3-*-Notify 216 687 <1s <1s
C3-L2-*-Request 18,654 91,596 49.5 s 67.0 s
C3-L2-*-Notify 372 1,311 <1s <1s

C3-L3-*-Request 54,894 276,378 480.9 s 339.8 s
C3-L3-*-Notify 372 1,311 <1s <1s

The verification of the model revealed an error related to the notification of clients with
respect to fire risks which was not identified during simulation. This demonstrates the added
value of undertaken state space exploration of the CPN model, in order to perform more
exhaustive verification of properties.

7. Conclusion
In this paper we have presented a formal specification of the micro-service based architecture
for the fire risk notification system being developed in the DYNAMIC research project. Our

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modelling patterns represent a general approach to modelling REST APIs using CPNs. In
addition, we have formally validated the system services and the interaction between the
micro-services using state space exploration and model checking.
The design of the fire risk notification system has been following an implementation-first
approach where two prototypes were implemented and tested. The purpose has been to first
validate the fire risk model itself, as to better understand the functional requirements and
software technology capabilities in the technical solution space. The final step has then been
to specify the software architecture and micro-services in an implementation-independent
manner using a CPN model. The CPN model presented in this paper will then serve as basis for
implementing a final version of the fire risk notification system.
In the present work we have considered only a limited number of system configurations.
Future work includes verification of a more complete set of system configurations using the
incremental methodology presented in [26]. As part of this, we may also investigate the use
of fairness assumptions in the verification to be able to verify eventual-type properties and
hence strengthen the properties being verified from the system. Furthermore, the constructed
CPN model may potentially be used to generate test-cases for the implementation using the
model-based approach presented in [27].

Acknowledgments
This study was partly funded by the Research Council of Norway, grant no 298993, Reducing
fire disaster risk through dynamic risk assessment and management (DYNAMIC). The study was
also supported by Haugaland Kraft Nett, Norwegian Directorate for Cultural Heritage and
Stavanger municipality. Part of this work was achieved while Lars M. Kristensen was visiting
Université Sorbonne Paris Nord as invited professor.

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 |authors=Ruben Dobler Strand,Lars Michael Kristensen,Laure Petrucci
 |dblpUrl=https://dblp.org/rec/conf/apn/StrandKP22
+|wikidataid=Q117351500
 }}
 ==Formal Specification and Validation of a Data-driven Software System for Fire Risk Prediction==

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Formal Specification and Validation of a Data-driven Software System for Fire Risk Prediction

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