CN115237817B - A dynamic model detection method and device for data exchange and shared low-code process - Google Patents

Abstract

The application relates to the field of data exchange, and provides a dynamic model detection method and a device for a data exchange sharing low code process, wherein the method comprises the following steps: standardizing the original flow according to a preset standardization rule to obtain a standard flow; converting the standard process passing through static detection into a dynamic model; and generating dynamic constraint of the standard process passing the static detection, detecting whether the dynamic model meets the dynamic constraint, and outputting a dynamic model detection result. The method has the advantages that the standardized original process is subjected to static detection and dynamic detection, the first type bad taste and the second type bad taste are identified, the final detection result is output, the data exchange shared low-code process under various complex scenes can be processed, and meanwhile, the efficiency and the accuracy of bad taste identification in the data exchange shared low-code process are improved.

Description

A dynamic model detection method and device for data exchange and shared low-code process

technical field

The embodiments of the present application relate to the field of data exchange, and in particular, relate to a dynamic model detection method and device for data exchange and shared low-code flow.

Background technique

Data exchange sharing low-code process is to assemble low-code process services with different functions into larger services through data exchange to meet user needs. Data exchange and shared low-code process is not a simple combination of services. Unreasonable data exchange will lead to processes that cannot be executed or can be executed but cannot achieve business goals, that is, the generation of "bad taste".

The common way of data exchange and sharing low-code process is to model through BPMN. In the low-code process of data exchange and sharing based on BPMN modeling, the identification of bad smell has become an urgent problem to be solved. However, in the process of identifying bad smells based on BPMN modeling, on the one hand, due to the different sources of bad smells, the occurrence scenarios are complex and changeable. To determine bad smells, it is necessary to summarize the common types of bad smells, and the constraints in the process It is very difficult to make an accurate description; on the other hand, it is impossible to accurately identify bad smells and provide specific bad smell scenarios for easy traceability; in addition, due to the complex process execution scenarios and many types of bad smells, how to quickly identify multiple bad smells in complex processes, became a business difficulty.

Contents of the invention

The embodiment of the present application is to provide a dynamic model detection method and device for data exchange and shared low-code flow, aiming to solve the problem that the process constraints are difficult to accurately describe when bad smells are identified in the data exchange shared low-code flow, and bad smells cannot be accurately and quickly identified The problem.

The first aspect of the embodiment of the present application provides a dynamic model detection method for data exchange and shared low-code flow, including:

Standardize the original process according to the preset standardization rules to obtain the standard process;

Transform the standard process through static inspection into a dynamic model;

Generate the dynamic constraints of the standard process that passed the static detection, and detect whether the dynamic model satisfies the dynamic constraints, and output the dynamic model detection result.

Optionally, the original process is standardized according to preset standardization rules to obtain a standard process, including:

Enter said original process;

extracting original information in the original process, where the original information includes nodes and connections;

Converting the original information in the original process into corresponding standard process information according to preset standardization rules, the standard process information constitutes the standard process, and the standard process can be correctly applied to the static detection, and The dynamic model and the dynamic constraints are generated.

Optionally, converting the standard flow through static detection into a dynamic model, including:

performing the static detection on the standard process to obtain a static detection result;

When the static detection result is that the process does not have the first type of bad smell, the standard process corresponding to the static detection result is regarded as the standard process that has passed the static test;

According to the preset dynamic model detection tool, select the corresponding modeling system;

According to the modeling system, the standard process passing the static detection is converted into the dynamic model.

Optionally, performing the static detection on the standard process to obtain a static detection result, including:

Static rule analysis is performed on the standard process to obtain a set of information to be detected, and the set of information to be detected corresponds to the first type of bad taste;

Perform static rule detection on the information set to be detected to obtain the static detection result, the static detection result includes the process does not have the first type of bad smell and the process has the first type of bad smell;

If the static detection result shows that the process has the first type of bad smell, the type and scene of the first type of bad smell in the process are output at the same time, and the final detection result is obtained according to the static detection result.

Optionally, generating the dynamic constraints of the standard flow through static detection, and detecting whether the dynamic model satisfies the dynamic constraints, and outputting a dynamic model detection result, including:

According to the preset dynamic model detection tool and the dynamic constraint template, generate the dynamic constraint of the standard flow through the static detection, the dynamic constraint corresponds to the second type of bad taste;

According to the preset dynamic model detection tool, detect whether the dynamic model satisfies the dynamic constraint, and output the dynamic model detection result.

Optionally, outputting the dynamic model detection result includes:

When the dynamic model satisfies the dynamic constraint, the output detection result of the dynamic model is that the process does not have the second type of bad smell;

When the dynamic model does not satisfy the dynamic constraints, the output of the dynamic model detection result is that the process has a second type of bad smell, and at the same time output the type of the second type of bad smell in the process;

The final detection result is generated according to the dynamic model detection result and/or the static detection result.

Optionally, generating the final detection result according to the dynamic model detection result and/or the static detection result includes:

In the case where the static detection result is that the process has the first type of bad smell, the final detection result is that the process has the first type of bad smell and the type and scene of the first type of bad smell;

When the dynamic model detection result is that the process has a second type of bad smell, the final detection result is that the process has a second type of bad smell and the type of the second type of bad smell;

In the case that the dynamic model detection result is that the process does not have the second type of bad smell, the final detection result is that the process does not have bad smell.

The second aspect of the embodiment of the present application provides a dynamic model detection device for data exchange and shared low-code flow, including:

The standardization module is used to standardize the original process according to the preset standardization rules to obtain the standard process;

The dynamic model generation module is used to convert the standard process passing the static detection into a dynamic model;

A dynamic constraint generating module, configured to generate the dynamic constraints of the standard flow through static detection;

A dynamic model detection module, configured to detect whether the dynamic model satisfies the dynamic constraints, and output a dynamic model detection result.

Optionally, the device also includes;

A static detection module, configured to perform the static detection on the standard process to obtain a static detection result;

The result analysis module is configured to generate a final detection result according to the dynamic model detection result and the static detection result.

Optionally, the standardization module also includes:

an input sub-module for inputting the original process;

An extracting submodule, configured to extract original information in the original process, the original information including start nodes, end nodes, task nodes, policy nodes, parallel gateway nodes, sub-process nodes, exclusive gateway nodes, storage nodes, and connections ;

The conversion sub-module is used to convert the original information in the original process into corresponding standard process information according to preset standardization rules, and the standard process information includes a finite node set, an event set, an activity set, and a gateway set , data collection, and elements in the connection object collection;

A generation sub-module is used to compose the standard process information into the standard process, the standard process can be correctly applied to the static detection, and generate the dynamic model and the dynamic constraints.

Wherein, the static detection module also includes:

The static rule analysis submodule is used to perform static rule analysis on the standard process to obtain a set of information to be detected, and the set of information to be detected corresponds to the first type of bad taste;

A static rule detection submodule, configured to perform static rule detection on the information set to be detected to obtain the static detection result, the static detection result including the process does not have the first type of bad smell and the process has the first type of bad smell;

The static detection result output submodule is used to output the type and scene of the first type of bad smell in the process at the same time when the static detection result is that the process has the first type of bad smell, and according to the static detection result Get the final test result.

Wherein, the dynamic model generation module also includes:

A confirmation submodule, configured to regard the standard process corresponding to the static test result as the standard process that passed the static test when the static test result shows that the process does not have the first type of bad smell;

The selection sub-module is used to select the corresponding modeling system according to the preset dynamic model detection tool;

The conversion sub-module is used to convert the standard process passing the static detection into the dynamic model according to the modeling system.

Wherein, the dynamic model detection module also includes:

The first dynamic result output submodule is configured to output the dynamic model detection result that the process does not have the second type of bad smell when the dynamic model satisfies the dynamic constraint;

The second dynamic result output submodule is used to output the detection result of the dynamic model that the process has a second type of bad smell when the dynamic model does not satisfy the dynamic constraint, and output the second type of bad smell in the process at the same time. Type bad taste type.

Wherein, the result analysis module also includes:

The first result analysis sub-module is used to: when the static detection result is that the process has the first type of bad smell, the final detection result is that the process has the first type of bad smell and the type of the first type of bad smell and scene;

The second result analysis sub-module, when the dynamic model detection result is that the process has a second type of bad smell, the final detection result is that the process has a second type of bad smell and the type of the second type of bad smell;

The third result analysis sub-module, when the dynamic model detection result is that the process does not have the second type of bad smell, the final detection result is that the process does not have bad smell.

Beneficial effect:

This application provides a dynamic model detection method and device for data exchange and shared low-code flow, including: standardizing the original flow according to preset standardization rules to obtain a standard flow; converting the standard flow through static detection into a dynamic model; generating Passing the dynamic constraints of the standard process of the static detection, and detecting whether the dynamic model satisfies the dynamic constraints, and outputting a dynamic model detection result. This application detects the first type of bad smell and the second type of bad smell by performing two stages of static detection and dynamic detection on the standardized original process, and outputs the final detection result, which has the following effects:

(1) This application summarizes the common types of bad taste, and at the same time accurately describes the dynamic constraints in the process, which improves the accuracy of bad taste recognition.

(2) This application standardizes different configurations and different types of original processes according to the preset standardization rules, and can handle data exchange sharing low-code processes in various complex scenarios.

(3) This application recognizes the first type of bad smell and the second type of bad smell through the combination of static detection and dynamic detection, realizing multi-stage detection and multi-checker detection, effectively improving the low-code flow of data exchange and sharing Efficiency in bad taste recognition.

Description of drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the accompanying drawings that need to be used in the description of the embodiments of the present application. Obviously, the accompanying drawings in the following description are only some embodiments of the present application , for those skilled in the art, other drawings can also be obtained according to these drawings without paying creative labor.

Fig. 1 is a flowchart of a dynamic model detection method for data exchange sharing low-code flow proposed by an embodiment of the present application;

Fig. 2a is a common bad taste classification diagram proposed by an embodiment of the present application;

Fig. 2b is a data bad smell classification diagram proposed by an embodiment of the present application;

Fig. 2c is a structural bad taste classification diagram proposed by an embodiment of the present application;

Fig. 2d is a self-defined bad taste classification diagram proposed by an embodiment of the present application;

Fig. 2e is a configuration bad smell classification diagram proposed by an embodiment of the present application;

Fig. 3 is a schematic diagram of the Kripke structure of the start event startEvent proposed by an embodiment of the present application;

FIG. 4 is a schematic diagram of a dynamic model detection device for data exchange sharing low-code flow proposed by an embodiment of the present application.

detailed description

The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of them. Based on the embodiments in this application, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the scope of protection of this application.

In order to better understand the methods provided in the embodiments of the present application, firstly, the technical terms appearing below are explained.

Data exchange shared low-code process, or service assembly, refers to the assembly of simple low-code service processes into complex service processes. Different services come from different platforms or systems, and provide various functions to the outside world. By assembling together different services that provide a single function, a larger and more feature-rich service is formed.

BPMN (Business Process Model and Notation) has become a widely used business process modeling language. Data exchange and shared low-code process based on BPMN, with richer semantics. Standard Business Process Model Notation (BPMN) provides the ability to describe business processes in graphical notation and enables users to express these processes in a standard way.

Model checking technology is an automatic verification technology that checks whether the behavior of the system has predictive properties by searching the finite state space of the system model, and is the main method of formal verification. The basic idea of model checking is that, for a finite state model, it is checked whether the model satisfies the given constraints by means of state space search rather than mathematical proof. Model checking uses the state transition system (M) to describe the system model, and uses the temporal logic formula (φ) to describe the constraints. Model checking checks whether a temporal logic formulation (φ) holds over the entire state transition system (M) in a finite state space. Since the state space of M is finite, checking whether M satisfies the constraint φ can be done in finite time.

Model checking is mainly divided into three steps: modeling, specification and verification. Modeling is the translation of finite-state concurrent systems into models used by model checking tools. Constraints are generally described using linear sequential logic formulas LTL or computational tree logic formulas CTL. Verification is that the model checking tool executes the system model M, performs an exhaustive search on the entire state space, and checks whether the attribute constraint φ is satisfied. If it is satisfied, no error is output, otherwise an erroneous path is output as a counterexample that the system model M does not satisfy the attribute constraint φ, so as to trace the source of the error.

In related technologies, in the process of identifying bad smells based on BPMN modeling, on the one hand, due to the different sources of bad smells, the occurrence scenarios are complex and changeable. To determine bad smells, it is necessary to summarize the common types of bad smells, and to analyze the types of bad smells in the process. On the other hand, it is impossible to accurately identify bad smells and provide specific bad smell scenarios for easy traceability; in addition, due to the complexity of process execution scenarios and many types of bad smells, how to quickly identify multiple bad smells in complex processes Taste has become a difficult point in business.

In view of this, the embodiment of the present application proposes a dynamic model detection method for data exchange and sharing low-code process, aiming at solving the problem that process constraints are difficult to accurately describe when bad smells are identified in data exchange and shared low-code processes, and bad smells cannot be accurately and quickly detected. problem of identification. Figure 1 shows a flow chart of a dynamic model detection method for data exchange sharing low-code processes, as shown in Figure 1, and the specific steps are as follows:

S101. Standardize the original process according to preset standardization rules to obtain a standard process.

S102. Convert the standard process passing the static test into a dynamic model.

S103. Generate dynamic constraints of the standard process that pass the static detection, and detect whether the dynamic model satisfies the dynamic constraints, and output a dynamic model detection result.

When step S101 is specifically implemented, since the information in different original processes (including process elements and process configurations, etc.) Lay the foundation for subsequent static detection and dynamic detection. Therefore, the embodiment of the present application defines a preset standardization rule, which is used to uniformly convert the information in the original process into standardized information available later.

First, define the standard process as follows:

Based on the BPMN process, the standard process information in a standard process is defined as the elements in the finite node set N, event set E, activity set A, gateway set G, data set D and connection object set F.

Among them, for a finite set of nodes N, N = E∪A∪G;

For event set E, E ⊆ N, E = startEvent∪endEvent, where startEvent is the start event set, endEvent is the end event set;

For an activity set A, A ⊆ N, A = task∪subProcess, where task is a set of task activities, and subProcess is a set of subprocess activities.

For a set of gateways G, G ⊆ N, G = exclusiveGateway∪parallelGateway∪inclusiveGateway. Where exclusiveGateway is an exclusive gateway set, exclusiveGateway = exclusiveSplitGateway∪exclusiveJoinGateway; parallelGateway is a parallel gateway set, parallelGateway = parallelSplitGateway∪parallelJoinGateway; inclusiveGateway is an inclusive gateway set, inclusiveGateway = inclusiveSplitGateway∪inclusiveJoinGateway;

For a data set D, D = dataObject∪dataInput∪dataOutput∪dataStore. dataObject is a collection of data objects, dataInput is a collection of input objects, dataOutput is a collection of output objects, and dataStore is a collection of storage objects;

For a set of connection objects F, F = sequenceFlow∪messageFlow. sequenceFlow is a collection of sequence flows, sequenceFlow ⊆ N × N. messageFlow is a collection of information flows, messageFlow ⊆ startEvent× dataObject∪dataInput × task∪task × dataOutput∪task × dataStore.

Then, the original information in the original process can be divided into start node, end node, task node, policy node, parallel gateway node, sub-process node, exclusive gateway node, storage node and connection.

Table 1 shows the corresponding relationship between the original process information and the standard process information in the preset standardization rules, and the original process information and the standard process information can be converted correspondingly based on the contents of Table 1.

Table 1 Correspondence between original process information and standard process information in preset standardization rules

raw process information Standard Process Information start node (id, name, outgoing) startEvent(id, name, outgoing) end node (id, name, incoming) endEvent(id, name, incoming) task node (id, name, inputParameter, outputParameter, incoming, outgoing) task (id, name, dataInput, dataOutput, incoming, outgoing, messageFlow) policy node none Parallel gateway node (id, name, incoming, outgoing) parallelGateway(id, name, incoming, outgoing) Subprocess node (id, name) subProcess(id, name) Exclusive gateway node (id, name, default, incoming, outgoing) exclusiveGateway(id, name, default, incoming, outgoing) storage node (id, inputParameter) dataStore(id, name) connection(id, sourceRef, targetRef) sequenceFlow(id, sourceRef, targetRef)

After defining the above-mentioned original process information, standard process information, and preset standardization rules, execute the above S101, specifically inputting the original process, then extracting the above-mentioned original information in the original process, and extracting the extracted information from the original process The original information is converted into corresponding standard process information according to preset standardization rules; finally, the standard process information is composed into the standard process.

For example, extract the start node information in the original process, the start node information includes id and name, and the sub-element information includes outgoing, based on the preset standardization rules, convert it to the startEvent in the standard process information, including id and name, and the sub-element information includes outgoing, the startEvent information as part of the standard process. Other standard process information is also generated in a similar manner, which will not be repeated here.

It is worth noting that when defining the above-mentioned standard process, standard process information, and preset standardization rules, it refers to various tools and templates in subsequent static detection and dynamic detection (such as preset dynamic model detection tools, dynamic constraint templates, etc.) To ensure that the standard process converted based on the above preset standardization rules can play a role in the subsequent static detection and dynamic detection process.

When step S102 is specifically implemented, the standard process of passing the static detection is converted into a dynamic model. The embodiment of the present application adopts a combination of static detection and dynamic detection to identify bad smells. Static detection does not require process execution and can be used to detect the first type of bad smell. Dynamic detection requires process execution and can be used to detect all bad smells. However, due to the difficulty of process execution and time-consuming problems, the embodiment of the present application does not directly use dynamic detection to identify all types of bad smells, but uses dynamic model detection to detect the second type of bad smells. By combining static detection and Dynamic detection can improve the accuracy and efficiency of bad taste identification.

First of all, the embodiment of the present application defines and classifies common bad tastes. Figures 2a-2e show the classification diagrams of common bad tastes defined in the embodiments of the present application. As shown in Figure 2a, common bad tastes are defined as the following four categories :

Data bad smells, as shown in Figure 2b, include parallel operation conflicts, continuous operation conflicts, missing data, data redundancy, concurrent data dependencies, initial data redundancy, and other bad smells;

The bad smell of the structure, as shown in Figure 2c, includes the lack of start nodes and end nodes, multiple start nodes and end nodes, loops, connection errors, lack of default paths, repeated connections, and other bad smells;

Custom bad smells, as shown in Figure 2d, include bad smells such as missing storage, the execution order of nodes does not meet business requirements, and lack of task execution nodes;

Configuration bad smells, as shown in Figure 2e, include configuration bad smells: absence of node and data object names and duplication of node and data object names bad smells.

Subsequently, the bad smells that can be identified without process execution are classified as the first type of bad smells for subsequent static detection; among the above bad smells that only appear when the process is executed, they are classified as the second type of bad smells for use in Follow-up dynamic detection. Among them, the bad smell of sub-process errors cannot be detected through static or dynamic detection. The embodiment of the present application is aimed at the detection of other types of bad smells except for the bad smell of sub-process errors in Figures 2a-2e.

Since static detection is simpler than dynamic detection and does not require process execution, the embodiment of this application needs to perform static detection on the standard process before performing dynamic model detection on it. If the static detection result has determined that the original process has a bad smell, then There is no need for subsequent dynamic detection, which can effectively improve the identification efficiency of bad smells and reduce the cost of the process. When there is a standard process that passes the static inspection, the subsequent dynamic model inspection is performed.

Among them, the static detection is carried out according to the following methods:

Perform static rule analysis on the standardized process, analyze the standard process information according to the preset inspection function corresponding to the first type of bad taste, and output the information set to be detected. For example, when performing static rule analysis of a bad smell that lacks a start node or an end node for a standard process, all standardized process information in the standard process is input, and the startEvent and endEvent information corresponding to the bad smell is output after parsing.

Then, static rule detection is performed on the set of information to be detected outputted after analysis, and the set of information to be detected is detected according to the preset check function corresponding to the first type of bad taste, and a static detection result is output, the static detection result includes that the process does not exist Type 1 bad smell (output True) and the process has type 1 bad smell (output False).

When the static detection result shows that the process has the first type of bad smell (output False), the type and scene of the first type of bad smell in the process are output at the same time, and the final detection result is obtained according to the static detection result. At this time, it means that the static detection has detected the bad taste, so there is no need to perform subsequent dynamic detection, and it can directly enter the result analysis module and output the final detection result.

When the static detection result shows that the flow does not have the first type of bad smell, the standard flow corresponding to the static detection result is regarded as the standard flow that passes the static detection, and step S102 is executed at this time, specifically as follows :

According to the preset dynamic model detection tool, select the corresponding modeling system. As mentioned above, when the embodiment of the present application defines the standard process, it refers to the preset dynamic model detection tool, modeling system and constraint template in the dynamic detection process, which is essentially to make the standard process information in the standard process Can be applied to the process of dynamic detection. Similarly, when performing dynamic modeling on the standard process that has passed the static inspection, it is also necessary to refer to the subsequent preset dynamic model inspection tools to select the modeling tool. In the embodiment of the present application, based on the follow-up preset dynamic model detection tool, a migration system (such as Kripke structure), finite automata (such as Büchi automaton) or Petri net can be selected to dynamically model the standard process that has passed static detection, and generate dynamic Model. The specific modeling tool selection is not limited in this application, and can be selected according to the subsequent preset dynamic model detection tools.

For example, if the Kripke structure is selected to model the standard process that has passed the static inspection, specifically, the standard process information in the standard process is converted based on the Kripke structure. Fig. 3 shows a schematic diagram of the Kripke structure of the start event startEvent. As shown in Fig. 3, the startEvent in the standard process information is converted into the model initial state init in the Kripke structure. Other standard process information is also converted based on the structure in the corresponding modeling method. For details, refer to the modeling method in the prior art, which will not be repeated in this application.

Through the preset modeling tools, the standard process that passes the static inspection is modeled, and finally a dynamic model is generated for subsequent dynamic inspection.

When specifically executing S103, it is necessary to generate the dynamic constraints of the standard process that passes the static detection, wherein the dynamic constraints are the constraints that describe the execution of the process using the linear sequential logic formula LTL or the computer tree logic formula CTL, which corresponds to the need to rely on the process The second type of bad smell comes from execution.

First, according to the second type of bad smell, the standard process information in the standard process that has passed the static detection is analyzed to obtain a set of standard process information corresponding to the second type of bad smell. Subsequently, according to the preset constraint template, determine the corresponding constraint description set in the standard process information corresponding to the second type of bad smell, and output the constraint description as the dynamic constraint of the second type of bad smell. As above and determine its dynamic constraints for all second-type bad tastes.

For example, if it is necessary to generate a dynamic description corresponding to the missing bad smell, it is necessary to first analyze the standard process information in the standard process that has passed the static detection to obtain the data set D corresponding to the bad smell, and then based on the preset constraint template, Determine the bad smell of storage loss and the constraint description corresponding to the data set D. Table 2 shows the constraint expression corresponding to the bad smell of storage loss in the preset constraint template, and output the constraint description as a dynamic constraint of the bad smell of storage loss.

Table 2 Constraint expressions corresponding to the bad smell of storage missing in the preset constraint template

enter output describe test result Inspection Method Standard BPMN process D. (([](w(n1)-> (X( !w(n1) U s(n1)))))) &&…&& (([](w(nk)-> (X( !w(nk ) U s(nk) ))))), n1, …, nk∈D,k=|D| True / False dynamic check

After the dynamic model and dynamic constraints are obtained, according to the preset dynamic model detection tool, it is detected whether the dynamic model satisfies the dynamic constraints, and the dynamic model detection result is output. Specifically, for each dynamic constraint description corresponding to the second type of bad smell, when the dynamic model of the finite state satisfies the dynamic constraint description, the output dynamic model detection result is that the process does not have the second type of bad smell (True); When the dynamic model of the finite state does not satisfy the dynamic constraint description, the output dynamic model detection result is that the process has the second type of bad taste (False), and at the same time output the specific type of the bad smell, which is convenient for subsequent manual analysis of the bad smell How the system performed when it happened.

Wherein, the preset dynamic model checking tool is the basis for defining the standard procedure and selecting the modeling tool, and the aforementioned defining standard procedure and selecting the modeling tool need to be applicable to the preset dynamic model checking tool. For example, a Spin model checker or a NuSMV model checker can be selected, and a specific preset dynamic model checker can be selected according to specific requirements, which is not limited in this application.

In addition, when the dynamic model detection result is output during the dynamic detection process, or the static detection result shows that the process has the first type of bad taste (that is, no subsequent dynamic detection is required), this embodiment will also use the dynamic model detection result and/or The static detection result generates the final detection result. Specific final test results include the following:

Case 1: When the static detection result is that the process has the first type of bad smell, the final detection result is that the process has the first type of bad smell, and at the same time output the type and scene of the first type of bad smell. At this time, it indicates that the original process has detected the existence of the first type of bad smell in the static detection process. In order to improve the detection efficiency and reduce the detection cost, no follow-up dynamic detection is performed, and the final detection result is directly output.

Case 2: When the dynamic model detection result is that the process has a second type of bad smell, the final detection result is that the process has a second type of bad smell, and at the same time output the type of the second type of bad smell. At this time, it indicates that the original process has passed the static test, and the original process does not have the first type of bad smell. When the subsequent dynamic model test is performed, it is detected that the original process has the second type of bad smell when the dynamic detection process is executed, and the final test result is output. .

Case 3: When the dynamic model detection result is that the process does not have the second type of bad smell, the final detection result is that the process does not have bad smell. At this time, it shows that the original process has passed the static test, and there is no first type of bad smell; then it has passed the dynamic model test, and there is no second type of bad smell, indicating that the original process does not have the common bad smell summarized above (the first type) bad taste + the second type of bad taste), and output the final detection result.

In this embodiment of the present application, the standardized original process is tested in two stages of static detection and dynamic detection to identify the first type of bad smell and the second type of bad smell, and output the final detection result, which has the following effects:

(1) This application summarizes the common types of bad taste, and at the same time accurately describes the dynamic constraints in the process, which improves the accuracy of bad taste recognition.

(2) This application standardizes different configurations and different types of original processes according to the preset standardization rules, and can handle data exchange sharing low-code processes in various complex scenarios.

(3) This application recognizes the first type of bad smell and the second type of bad smell through the combination of static detection and dynamic detection, realizing multi-stage detection and multi-checker detection, effectively improving the low-code flow of data exchange and sharing Efficiency in bad taste recognition.

Based on the same inventive concept, an embodiment of the present application provides a dynamic model detection device for data exchange and shared low-code flow. Figure 4 shows a schematic diagram of a dynamic model detection device for data exchange and shared low-code flow. Dynamic model checking fixtures that exchange shared low-code flows include:

The standardization module is used to standardize the original process according to the preset standardization rules to obtain the standard process;

The dynamic model generation module is used to convert the standard process passing the static detection into a dynamic model;

A dynamic constraint generating module, configured to generate the dynamic constraints of the standard flow through static detection;

A dynamic model detection module, configured to detect whether the dynamic model satisfies the dynamic constraints, and output a dynamic model detection result.

Optionally, the device also includes;

A static detection module, configured to perform the static detection on the standard process to obtain a static detection result;

The result analysis module is configured to generate a final detection result according to the dynamic model detection result and the static detection result.

Optionally, the standardization module also includes:

an input sub-module for inputting the original process;

An extracting submodule, configured to extract original information in the original process, the original information including start nodes, end nodes, task nodes, policy nodes, parallel gateway nodes, sub-process nodes, exclusive gateway nodes, storage nodes, and connections ;

The conversion sub-module is used to convert the original information in the original process into corresponding standard process information according to preset standardization rules, and the standard process information includes a finite node set, an event set, an activity set, and a gateway set , data collection, and elements in the connection object collection;

A generation sub-module is used to compose the standard process information into the standard process, the standard process can be correctly applied to the static detection, and generate the dynamic model and the dynamic constraints.

Wherein, the static detection module also includes:

The static rule analysis submodule is used to perform static rule analysis on the standard process to obtain a set of information to be detected, and the set of information to be detected corresponds to the first type of bad taste;

A static rule detection submodule, configured to perform static rule detection on the information set to be detected to obtain the static detection result, the static detection result including the process does not have the first type of bad smell and the process has the first type of bad smell;

The static detection result output submodule is used to output the type and scene of the first type of bad smell in the process at the same time when the static detection result is that the process has the first type of bad smell, and according to the static detection result Get the final test result.

Wherein, the dynamic model generation module also includes:

A confirmation submodule, configured to regard the standard process corresponding to the static test result as the standard process that passed the static test when the static test result shows that the process does not have the first type of bad smell;

The selection sub-module is used to select the corresponding modeling system according to the preset dynamic model detection tool;

The conversion sub-module is used to convert the standard process passing the static detection into the dynamic model according to the modeling system.

Wherein, the dynamic model detection module also includes:

The first dynamic result output submodule is configured to output the dynamic model detection result that the process does not have the second type of bad smell when the dynamic model satisfies the dynamic constraint;

The second dynamic result output submodule is used to output the detection result of the dynamic model that the process has a second type of bad smell when the dynamic model does not satisfy the dynamic constraint, and output the second type of bad smell in the process at the same time. Type bad taste type.

Wherein, the result analysis module also includes:

The first result analysis sub-module is used to: when the static detection result is that the process has the first type of bad smell, the final detection result is that the process has the first type of bad smell and the type of the first type of bad smell and scene;

The second result analysis sub-module, when the dynamic model detection result is that the process has a second type of bad smell, the final detection result is that the process has a second type of bad smell and the type of the second type of bad smell;

The third result analysis sub-module, when the dynamic model detection result is that the process does not have the second type of bad smell, the final detection result is that the process does not have bad smell.

In the above embodiments, all or part of them may be implemented by software, hardware, firmware or any combination thereof. When implemented using software, it may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on the computer, the processes or functions according to the embodiments of the present application will be generated in whole or in part. The computer can be a general purpose computer, a special purpose computer, a computer network, or other programmable devices. The computer instructions may be stored in or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from a website, computer, server or data center Transmission to another website site, computer, server, or data center by wired (eg, coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (eg, infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that can be accessed by a computer, or a data storage device such as a server or a data center integrated with one or more available media. The available medium may be a magnetic medium (for example, a floppy disk, a hard disk, or a magnetic tape), an optical medium (for example, DVD), or a semiconductor medium (for example, a Solid State Disk (SSD)).

It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that there is a relationship between these entities or operations. There is no such actual relationship or order between them. Furthermore, the term "comprises", "comprises" or any other variation thereof is intended to cover a non-exclusive inclusion such that a process, method, article, or apparatus comprising a set of elements includes not only those elements, but also includes elements not expressly listed. other elements of or also include elements inherent in such a process, method, article, or device. Without further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article or apparatus comprising said element.

Each embodiment in this specification is described in a related manner, the same and similar parts of each embodiment can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and for relevant parts, refer to part of the description of the method embodiment.

The technical solutions provided by this application have been introduced in detail above, and specific examples have been used in this paper to illustrate the principles and implementation methods of this application. The descriptions of the above examples are only used to help understand this application, and the content of this specification should not be understood For the limitation of this application. At the same time, for those of ordinary skill in the art, according to the present application, there will be changes in different forms in the specific implementation methods and application ranges, and it is not necessary and impossible to exhaustively list all the implementation methods here. Obvious changes or modifications are still within the protection scope of the present application.

Claims (9)

1. A dynamic model detection method for data exchange sharing low code flow is characterized by comprising the following steps:

standardizing the original flow according to a preset standardization rule to obtain a standard flow;

analyzing a static rule of the standard process to obtain an information set to be detected, wherein the information set to be detected corresponds to a first type of bad taste; performing static rule detection on an information set to be detected to obtain a static detection result, wherein the static detection result comprises that the first type bad taste does not exist in the process and the first type bad taste exists in the process;

when the static detection result indicates that the first type bad taste does not exist in the process, the standard process corresponding to the static detection result is regarded as the standard process passing the static detection, and the standard process passing the static detection is converted into a dynamic model;

generating the dynamic constraint of the standard flow passing through the static detection according to a preset dynamic model detection tool and a dynamic constraint template, wherein the dynamic constraint corresponds to a second type bad taste; and detecting whether the dynamic model meets the dynamic constraint or not according to the preset dynamic model detection tool, and outputting a dynamic model detection result.

2. The method for detecting a dynamic model of a data exchange shared low-code process, according to claim 1, wherein the step of standardizing an original process according to a preset standardization rule to obtain a standard process comprises:

inputting the original flow;

extracting original information in the original flow, wherein the original information comprises a starting node, an ending node, a task node, a strategy node, a parallel gateway node, a sub-flow node, an exclusive gateway node, a storage node and a connecting line;

converting the original information in the original flow into corresponding standard flow information according to a preset standardization rule, wherein the standard flow information comprises elements in a finite node set, an event set, an activity set, a gateway set, a data set and a connection object set;

and composing the standard process information into the standard process, wherein the standard process can be correctly applied to the static detection and the generation of the dynamic model and the dynamic constraint.

3. The method of claim 1, wherein converting the standard process through static detection into a dynamic model comprises:

selecting a corresponding modeling system according to the preset dynamic model detection tool;

and converting the standard process passing the static detection into the dynamic model according to the modeling system.

4. The method as claimed in claim 1, wherein when the static detection result indicates a first type bad smell exists in the process, the type and scene of the first type bad smell in the process are output simultaneously, and a final detection result is obtained according to the static detection result.

5. The method of claim 4, wherein outputting the dynamic model detection result comprises:

under the condition that the dynamic model meets the dynamic constraint, outputting the detection result of the dynamic model that the second type bad taste does not exist in the process;

under the condition that the dynamic model does not meet the dynamic constraint, outputting the detection result of the dynamic model to indicate that a second type of bad taste exists in the process, and simultaneously outputting the type of the second type of bad taste in the process;

and generating the final detection result according to the dynamic model detection result and/or the static detection result.

6. The method of claim 5, wherein generating the final detection result according to the dynamic model detection result and/or the static detection result comprises:

under the condition that the static detection result is that the first type bad taste exists in the process, the final detection result is that the first type bad taste exists in the process and the type and the scene of the first type bad taste exist in the process;

under the condition that the dynamic model detection result is that the second type bad taste exists in the process, the final detection result is that the second type bad taste exists in the process and the type of the second type bad taste exists in the process;

and under the condition that the dynamic model detection result indicates that the second type bad taste does not exist in the process, the final detection result indicates that the bad taste does not exist in the process.

7. A dynamic model detection device for data exchange sharing low code flow is characterized by comprising:

the standardization module is used for standardizing the original flow according to a preset standardization rule to obtain a standard flow;

the static detection module is used for analyzing a static rule of the standard process to obtain an information set to be detected, and the information set to be detected corresponds to the first type of bad taste; performing static rule detection on an information set to be detected to obtain a static detection result, wherein the static detection result comprises that the first type bad taste does not exist in the process and the first type bad taste exists in the process;

the dynamic model output module is used for regarding the standard flow corresponding to the static detection result as the standard flow passing the static detection and converting the standard flow passing the static detection into a dynamic model under the condition that the static detection result is that no first type bad taste exists in the flow;

the dynamic model detection module is used for generating the dynamic constraint of the standard flow passing through the static detection according to a preset dynamic model detection tool and a dynamic constraint template, and the dynamic constraint corresponds to a second type bad taste; and detecting whether the dynamic model meets the dynamic constraint or not according to the preset dynamic model detection tool, and outputting a dynamic model detection result.

8. The apparatus for dynamic model detection of data exchange sharing low code flow of claim 7, further comprising;

and the result analysis module is used for generating a final detection result according to the dynamic model detection result and the static detection result.

9. The apparatus for detecting dynamic models of data exchange sharing low code flow of claim 7, wherein said normalization module further comprises:

the input sub-module is used for inputting the original flow;

the extraction submodule is used for extracting original information in the original flow, and the original information comprises a start node, an end node, a task node, a strategy node, a parallel gateway node, a subprocess node, an exclusive gateway node, a storage node and a connecting line;

the conversion sub-module is used for converting the original information in the original flow into corresponding standard flow information according to a preset standardization rule, wherein the standard flow information comprises elements in a finite node set, an event set, an activity set, a gateway set, a data set and a connection object set;

and the generation submodule is used for forming the standard flow from the standard flow information, and the standard flow can be correctly applied to the static detection and the generation of the dynamic model and the dynamic constraint.

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