Matching detection method and equipment for functional requirements, medium and product

Abstract

The invention relates to the technical field of artificial intelligence, and discloses a function demand matching detection method and device, a medium and a product. The method comprises the following steps: acquiring a function list, and extracting each piece of function demand information from the function list; obtaining a current scale and a current matching demand of the function module library, and obtaining a current similarity threshold according to the current scale and the current matching demand; and calculating to obtain a target similarity score between each piece of function demand information and each function module in the function module library, and obtaining a target function module corresponding to each piece of function demand information according to the target similarity score and a current similarity threshold. According to the method, the dynamic similarity threshold is set according to the scale of the function module library and the matching requirements, and the matching function modules are automatically searched based on the similarity threshold, so that the accuracy and efficiency of matching detection of the function requirements can be improved, and the probability of mismatching can be reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of artificial intelligence, and in particular to a function requirement matching detection method, device, medium and product. BACKGROUND

[0002] In the process of software development and system design, the analysis and matching of function requirements is a key link. Efficient and accurate matching detection of function requirements is of great significance to ensure the progress of software development and system design.

[0003] At present, the existing matching detection method of function requirements usually relies on manual comparison or simple keyword matching. However, the manual comparison method has the problems of high cost, low efficiency and long time-consuming, and the keyword matching method lacks semantic understanding, which easily leads to mis-matching, resulting in low accuracy of matching detection. SUMMARY

[0004] The present application provides a function requirement matching detection method, device, medium and product, which can improve the accuracy and efficiency of function requirement matching detection and reduce the probability of mis-matching.

[0005] According to one aspect of the present application, a function requirement matching detection method is provided, comprising:

[0006] obtaining a function list and extracting each function requirement information from the function list;

[0007] obtaining the current size and current matching requirement of the function module library, and obtaining the current similarity threshold according to the current size and the current matching requirement;

[0008] calculating the target similarity score between each function requirement information and each function module in the function module library, and obtaining the target function module corresponding to each function requirement information according to the target similarity score and the current similarity threshold.

[0009] According to another aspect of the present application, a function requirement matching detection device is provided, comprising:

[0010] a function requirement information extraction module for obtaining a function list and extracting each function requirement information from the function list;

[0011] a similarity threshold obtaining module for obtaining the current size and current matching requirement of the function module library, and obtaining the current similarity threshold according to the current size and the current matching requirement;

[0012] The similarity score calculation module is configured to calculate target similarity scores between each of the functional requirement information and each of the functional modules in the functional module library, and obtain target functional modules corresponding to each of the functional requirement information according to the target similarity scores and the current similarity threshold.

[0013] According to another aspect of the present application, there is provided an electronic device, comprising:

[0014] at least one processor; and

[0015] a memory in communication with the at least one processor; wherein

[0016] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to perform the functional requirement matching detection method according to any one of the embodiments of the present application.

[0017] According to another aspect of the present application, there is provided a computer readable storage medium storing a computer program, the computer program being configured to enable a processor to implement the functional requirement matching detection method according to any one of the embodiments of the present application when executed by the processor.

[0018] According to another aspect of the present application, there is provided a computer program product comprising a computer program, the computer program being configured to implement the functional requirement matching detection method according to any one of the embodiments of the present application when executed by a processor.

[0019] The technical solution of the embodiments of the present application obtains a functional list and extracts each functional requirement information from the functional list, obtains a current scale and a current matching requirement of the functional module library, and obtains a current similarity threshold according to the current scale and the current matching requirement, calculates target similarity scores between each of the functional requirement information and each of the functional modules in the functional module library, and obtains target functional modules corresponding to each of the functional requirement information according to the target similarity scores and the current similarity threshold. By setting a dynamic similarity threshold according to the scale and the matching requirement of the functional module library, and automatically searching for a matching functional module based on the similarity threshold, the accuracy and efficiency of the functional requirement matching detection can be improved, and the probability of false matching can be reduced.

[0020] It should be understood that the content described in this part is not intended to identify key or important features of the embodiments of the present application, nor to limit the scope of the present application. Other features of the present application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed in the embodiments description. Obviously, the drawings in the following description only show some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative effort based on these drawings.

[0022] Figure 1 is a flow chart of a functional requirement matching detection method according to the first embodiment of the present application;

[0023] Figure 2 is a flow chart of a functional requirement matching detection method according to the second embodiment of the present application;

[0024] Figure 3 is a structural schematic diagram of a functional requirement matching detection device according to the third embodiment of the present application;

[0025] Figure 4 is a structural schematic diagram of an electronic device for implementing the functional requirement matching detection method of the embodiments of the present application. DETAILED DESCRIPTION

[0026] In order to make the person skilled in the art better understand the present application, the following will combine the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative effort should be within the scope of the present application.

[0027] It should be noted that the terms "first", "second", "change" and the like in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or sequence. It should be understood that the terms used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not necessarily limit to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0028] Embodiment one

[0029] Figure 1A flowchart of a function requirement matching detection method provided for the first embodiment of the present application. The embodiment can be applied to the automatic matching detection of function requirements and existing function modules. The method can be executed by a function requirement matching detection device, which can be implemented in the form of hardware and / or software. Typically, the function requirement matching detection device can be configured in an electronic device, such as a computer device, a server, etc. As shown in FIG. 8, the method comprises: Figure 1

[0030] S110, obtaining a function list and extracting function requirement information from the function list.

[0031] In the embodiment, a function list uploaded by a user can be obtained, and the content of the function list is parsed according to a specified format to extract function requirement information. For example, the function list can include multiple function items, and each function item is composed of multiple fields. Thus, the field values corresponding to each field can be extracted, and the field values are spliced to obtain the function requirement information corresponding to the function item.

[0032] Optionally, the function requirement information can include a function name, a function description, and / or a technical requirement. The function requirement information can also include user information, such as a user identifier, a user position, etc., and a submission time, etc.

[0033] In the embodiment, by setting multi-dimensional function requirement information, the accuracy of function requirement description can be improved, and the accuracy of function requirement matching detection can be improved.

[0034] Optionally, extracting the function requirement information from the function list can include:

[0035] Preprocessing the function list to obtain key information, and using a regular expression method and a named entity recognition method to recognize key entities of the key information to obtain the function requirement information.

[0036] In an optional example, first, the function list is preprocessed by word segmentation, stop word removal, syntax analysis, etc. to extract key information. Then, a regular expression method is used to identify entities with a fixed format to obtain field values of part of the fields, such as technical parameters, etc. At the same time, the key information can be input into a pre-trained entity recognition model for named entity recognition to obtain field values of other part of the fields output by the entity recognition model. Finally, the field values obtained by the two methods are integrated to obtain the final function requirement information. The entity recognition model can be established based on a hidden Markov model, a support vector machine, a long short-term memory network, etc.

[0037] ​In this embodiment, by employing multiple entity recognition methods to extract functional requirement information, the extraction efficiency and accuracy of functional requirement information can be improved.

[0038] S120. Obtain the current size and current matching requirements of the functional module library, and obtain the current similarity threshold based on the current size and the current matching requirements.

[0039] In this embodiment, a dynamic similarity threshold can be set based on the real-time scale of the functional module library and real-time matching requirements. The functional module library can be a database storing developed functional modules, which may include configuration information such as module name, description, and technical parameters. Matching requirements can be user-defined matching detection criteria, such as high similarity matching, medium similarity matching, and low similarity matching.

[0040] In this embodiment, the current size of the functional module library can be obtained based on the current number of functional modules in the library and the preset correspondence between the number range and the size. The size can include large-scale, medium-scale, and small-scale. Simultaneously, the current matching requirements uploaded by the user can be obtained. Then, based on the preset correspondence between size, matching requirements, and similarity thresholds, the similarity threshold corresponding to the current size and current matching requirements can be found and used as the current similarity threshold.

[0041] S130. Calculate the target similarity score between each of the functional requirement information and each functional module in the functional module library, and obtain the target functional module corresponding to each of the functional requirement information based on the target similarity score and the current similarity threshold.

[0042] In this embodiment, a preset similarity calculation method, such as cosine similarity method or Euclidean distance method, can be used to calculate the target similarity score between the functional requirement information and the configuration information of each functional module. Then, it is determined whether each target similarity score is greater than the current similarity threshold. If so, the functional module corresponding to the target similarity score is determined as a candidate functional module. Finally, the candidate functional modules are sorted in descending order according to the target similarity score, and one or more candidate functional modules are selected as the target functional module in the order from front to back.

[0043] In one optional example, based on the target similarity score, the functional module corresponding to the highest target similarity score is selected as the target functional module, and the target functional module and its corresponding target similarity score are visualized. Simultaneously, a manual review interface is provided, allowing users to adjust or compensate for the matching detection results.

[0044] The technical scheme of the embodiment of the application obtains a function list and extracts each function requirement information from the function list; obtains a current scale and a current matching requirement of a function module library, and obtains a current similarity threshold according to the current scale and the current matching requirement; calculates a target similarity score between each function requirement information and each function module in the function module library, and obtains a target function module corresponding to each function requirement information according to the target similarity score and the current similarity threshold; by setting a dynamic similarity threshold according to the scale and the matching requirement of the function module library, and automatically searching for a matching function module based on the similarity threshold, the accuracy and efficiency of matching detection of function requirements can be improved, and the probability of false matching can be reduced.

[0045] Embodiment two

[0046] Figure 2 A flowchart of a function requirement matching detection method provided by the second embodiment of the application is shown in the figure. The embodiment is a further refinement of the above technical scheme, and the technical scheme in the embodiment can be combined with one or more of the above embodiments. As shown in the figure, the method comprises: Figure 2

[0047] S210, a function list is obtained, and each function requirement information is extracted from the function list.

[0048] S220, a current scale and a current matching requirement of a function module library are obtained.

[0049] S230, a current adjustment coefficient is obtained according to the current scale and the current matching requirement, and a current similarity threshold is obtained according to the current adjustment coefficient and a preset basic similarity threshold.

[0050] In an optional example, the current adjustment coefficient corresponding to the current scale and the current matching requirement can be found according to the correspondence between the preset scale, the matching requirement and the adjustment coefficient; then, the current adjustment coefficient can be multiplied by the preset basic similarity threshold, and the product can be added to the preset basic similarity threshold to obtain a sum as the current similarity threshold.

[0051] Optionally, obtaining a current adjustment coefficient according to the current scale and the current matching requirement can comprise:

[0052] obtaining a first adjustment coefficient according to the current scale and the correspondence between the preset scale and the adjustment coefficient;

[0053] obtaining a second adjustment coefficient according to the current matching requirement and the correspondence between the preset matching requirement and the adjustment coefficient;

[0054] ​According to the first adjustment coefficient and the second adjustment coefficient, a current adjustment coefficient is calculated.

[0055] In an optional example, a correspondence between the scale and the adjustment coefficient, and a correspondence between the matching demand and the adjustment coefficient can be pre-set. Thus, according to the current scale and the current matching demand, the first adjustment coefficient and the second adjustment coefficient can be obtained by searching the corresponding correspondences respectively. Then, the first adjustment coefficient and the second adjustment coefficient can be directly added or weighted summed to obtain a sum value as the current adjustment coefficient. The weight values corresponding to the first adjustment coefficient and the second adjustment coefficient can be pre-set.

[0056] In the embodiment, by generating the first adjustment coefficient and the second adjustment coefficient according to the real-time scale and the real-time matching demand respectively, and then generating the final adjustment coefficient according to the first adjustment coefficient and the second adjustment coefficient, the rationality and accuracy of the adjustment coefficient can be improved.

[0057] S240, performing semantic analysis on the current function demand information to obtain a first semantic representation vector, and obtaining a second semantic representation vector corresponding to the current function module.

[0058] In the embodiment, when calculating the similarity score, first, the current function demand information can be processed by using a term frequency-inverse document frequency model, an N-gram model, etc. to obtain a corresponding first semantic representation vector; at the same time, the second semantic representation vector corresponding to the current function module can be read from the function module library. The configuration information of the function module can be pre-analyzed to generate a corresponding semantic representation vector and stored in the function module library.

[0059] Optionally, the semantic analysis on the current function demand information to obtain the first semantic representation vector can include:

[0060] The semantic analysis on the current function demand information obtains a word vector and a sentence vector, and the first semantic representation vector is generated according to the word vector and the sentence vector.

[0061] In an optional example, different types of pre-training models can be combined to generate a word vector and a sentence vector corresponding to the current function demand information respectively; then, the word vector and the sentence vector can be weighted and fused to generate a multi-dimensional initial semantic representation vector; finally, the generated initial semantic representation vector is normalized to ensure the comparability of different dimensional features, so as to obtain the final first semantic representation vector.

[0062] In the embodiment, by combining the word vector and the sentence vector to generate the final semantic representation vector, the accuracy of the semantic representation vector can be improved, so that the accuracy of the matching detection can be improved.

[0063] S250, calculating initial similarity scores corresponding to each preset dimension between the first semantic representation vector and the second semantic representation vector, and performing weighted summation on each of the initial similarity scores to obtain a sum value as a target similarity score between the current functional requirement information and the current functional module.

[0064] Then, the cosine similarity method, the Euclidean distance method and the Jaccard similarity method can be used to calculate the similarity between the first semantic representation vector and the second semantic representation vector to obtain initial similarity scores of three preset dimensions. Then, the initial similarity scores of the three preset dimensions can be weighted and summed to obtain a sum value as the final target similarity score. The weight values corresponding to each preset dimension can be set in advance.

[0065] S260, obtaining a target functional module corresponding to each of the functional requirement information according to the target similarity score and the current similarity threshold.

[0066] In this embodiment, when a user submits a new functional requirement, the technical solution of the present application can be used to find a matching target functional module and obtain a target similarity score corresponding to the target functional module. Then, the user can manually determine whether the target functional module matches the newly submitted functional requirement. If it is determined that the target functional module matches the newly submitted functional requirement, it means that the newly submitted functional requirement is complete. If it is determined that the target functional module does not match the newly submitted functional requirement, the target functional module can be modified or a new functional module can be directly developed to obtain a functional module that matches the newly submitted functional requirement, and the functional module is added to the functional module library.

[0067] The technical solution of the embodiment of the present application obtains a current adjustment coefficient according to a current scale and a current matching requirement, and obtains a current similarity threshold according to the current adjustment coefficient and a preset basic similarity threshold. By setting a dynamic adjustment coefficient according to a real-time scale and a matching requirement, and generating a real-time similarity threshold according to the adjustment coefficient, the dynamic adjustment of the similarity threshold can be realized, and the accuracy of the similarity threshold can be improved. The semantic analysis of the current functional requirement information is performed to obtain a first semantic representation vector, and a second semantic representation vector corresponding to the current functional module is obtained. Initial similarity scores corresponding to each preset dimension between the first semantic representation vector and the second semantic representation vector are calculated, and each of the initial similarity scores is weighted and summed to obtain a sum value as a target similarity score between the current functional requirement information and the current functional module. By calculating the information similarity based on the semantic representation vector, and generating a final target similarity score by comprehensively considering the initial similarity scores of multiple dimensions, the accuracy of the target similarity score can be improved, and the accuracy of the matching detection of the functional requirement can be improved.

[0068] In this embodiment, the efficiency of function matching can be significantly improved through fast matching of semantic representation vectors, which is suitable for processing of large-scale function lists and has high efficiency. The matching method based on semantic analysis can more accurately understand the meaning of function requirements, reduce the probability of false matching caused by keyword matching, and has accuracy. Various semantic models and matching algorithms are supported, which can be flexibly configured according to specific requirements, and has flexibility. The function module library can be dynamically updated, supporting the addition and maintenance of new function modules, and has scalability.

[0069] Embodiment three

[0070] Figure 3 A structural schematic diagram of a function requirement matching detection device provided by the third embodiment of the application is shown in the figure. Figure 3 As shown in the figure, the device comprises a function requirement information extraction module 310, a similarity threshold value acquisition module 320 and a similarity score calculation module 330; wherein,

[0071] The function requirement information extraction module 310 is used to obtain a function list and extract each function requirement information from the function list;

[0072] The similarity threshold value acquisition module 320 is used to obtain the current scale and current matching requirement of the function module library, and obtain the current similarity threshold value according to the current scale and the current matching requirement;

[0073] The similarity score calculation module 330 is used to calculate the target similarity score between each function requirement information and each function module in the function module library, and obtain the target function module corresponding to each function requirement information according to the target similarity score and the current similarity threshold value.

[0074] The technical scheme of the embodiment of the application obtains a function list and extracts each function requirement information from the function list; obtains the current scale and current matching requirement of the function module library, and obtains the current similarity threshold value according to the current scale and the current matching requirement; calculates the target similarity score between each function requirement information and each function module in the function module library, and obtains the target function module corresponding to each function requirement information according to the target similarity score and the current similarity threshold value; by setting a dynamic similarity threshold value according to the scale and matching requirement of the function module library, and automatically searching for a matching function module based on the similarity threshold value, the accuracy and efficiency of function requirement matching detection can be improved, and the probability of false matching can be reduced.

[0075] Optionally, the similarity threshold value acquisition module 320 is specifically used to obtain a current adjustment coefficient according to the current scale and the current matching requirement, and obtain the current similarity threshold value according to the current adjustment coefficient and a preset basic similarity threshold value.

[0076] Optionally, the similarity threshold obtaining module 320 is further configured to obtain the first adjustment coefficient according to the current scale and a preset corresponding relationship between scales and adjustment coefficients.

[0077] According to the current matching demand and a preset corresponding relationship between matching demands and adjustment coefficients, a second adjustment coefficient is obtained.

[0078] According to the first adjustment coefficient and the second adjustment coefficient, a current adjustment coefficient is calculated.

[0079] Optionally, the functional requirement information extraction module 310 is specifically configured to preprocess the function list, obtain key information, and perform key entity recognition on the key information by using a regular expression method and a named entity recognition method to obtain each functional requirement information.

[0080] Optionally, the similarity score calculation module 330 is specifically configured to perform semantic analysis on the current functional requirement information, obtain a first semantic representation vector, and obtain a second semantic representation vector corresponding to the current functional module.

[0081] The initial similarity scores corresponding to each preset dimension between the first semantic representation vector and the second semantic representation vector are calculated, and the initial similarity scores are weighted and summed to obtain a sum value as a target similarity score between the current functional requirement information and the current functional module.

[0082] Optionally, the similarity score calculation module 330 is further configured to perform semantic analysis on the current functional requirement information, obtain a word vector and a sentence vector, and generate the first semantic representation vector according to the word vector and the sentence vector.

[0083] Optionally, the functional requirement information includes a function name, a function description, and / or a technical requirement.

[0084] The functional requirement matching detection device provided in the embodiment of the application can execute the functional requirement matching detection method provided in any embodiment of the application, and has the corresponding functional modules and beneficial effects of the execution method.

[0085] In the technical solution of the present disclosure, the collection, storage, use, processing, transmission, provision and disclosure of user personal information comply with relevant laws and regulations and do not violate public order and good customs.

[0086] Embodiment Four

[0087] Figure 4A structural diagram of an electronic device 40 that can be used to implement embodiments of the present application is shown. The electronic device 40 is intended to represent various forms of digital computers, such as laptops, desktops, tablets, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device 40 can also represent various forms of mobile devices, such as personal digital assistants, cellular telephones, smartphones, wearable devices (e.g., headsets, glasses, watches, etc.), and other similar computing devices. The components shown here, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the present application described and / or claimed in this document.

[0088] As shown, Figure 4 The electronic device 40 includes at least one processor 41, and memory, such as a Read-Only Memory (ROM) 42, a Random Access Memory (RAM) 43, etc., communicatively connected to the at least one processor 41, where the memory stores computer programs executable by the at least one processor. The processor 41 can perform various appropriate actions and processes according to the computer programs stored in the ROM 42 or loaded into the RAM 43 from the storage unit 48. Various programs and data required for the operation of the electronic device 40 can also be stored in the RAM 43. The processor 41, the ROM 42, and the RAM 43 are connected to each other through a bus 44. An Input / Output (I / O) interface 45 is also connected to the bus 44.

[0089] Various components in the electronic device 40 are connected to the I / O interface 45, including an input unit 46, such as a keyboard, a mouse, etc., an output unit 47, such as various types of displays, a speaker, etc., a storage unit 48, such as a magnetic disk, an optical disk, etc., and a communication unit 49, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 49 allows the electronic device 40 to exchange information / data with other devices through a computer network, such as the Internet, and / or various telecommunication networks.

[0090] The processor 41 can be various general and / or special purpose processing components with processing and computing capabilities. Some examples of the processor 41 include, but are not limited to, a central processing unit, a graphics processing unit, various special-purpose artificial intelligence computing chips, various processors running machine learning model algorithms, a digital signal processor, and any appropriate processor, controller, microcontroller, etc. The processor 41 performs various methods and processes described above, such as the matching detection method of functional requirements.

[0091] In some embodiments, the matching detection method of functional requirements can be implemented as a computer program tangibly embodied in a computer readable storage medium, e.g., storage unit 48. In some embodiments, parts or all of the computer program can be loaded and / or installed onto electronic device 40 via, e.g., ROM 42 and / or communication unit 49. When the computer program is loaded onto RAM 43 and executed by processor 41, one or more steps of the above-described matching detection method of functional requirements can be performed. Alternatively, in other embodiments, processor 41 can be configured to perform the matching detection method of functional requirements by other means, e.g., with the aid of firmware.

[0092] Various implementations of the systems and techniques described above can be realized in digital electronic circuitry, integrated circuitry, specially designed application specific integrated circuits, application specific standard products, system on a chip, complex programmable logic devices, computers, firmware, software, and / or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0093] Computer programs used to implement the methods of the present application can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the computer program, when executed by the processor of the machine, implements the functions / acts specified in the flowcharts and / or block diagrams. The computer program can be executed entirely on a machine, partially on a machine, partially on a machine and partially on a remote machine or entirely on a remote machine or server.

[0094] In the context of the present application, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer-readable storage medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. More specific examples of a machine-readable storage medium will include one or more lines of a program of instructions in a transitory signal, a portable computer diskette, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory, an optical fiber, a portable compact disc read-only memory, an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0095] To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device 40 having a display device (e.g., a cathode ray tube or a liquid crystal display) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device 40. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0096] The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network, a wide area network, a blockchain network, and the Internet.

[0097] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server is generally established by computer programs running on the respective computers and having a client-server relationship to each other. A server can be a cloud server.

[0098] The embodiment can also include a computer program product including a computer program which, when executed by a processor, implements the matching detection method of the functional requirements provided by any embodiment of the present application.

[0099] It should be understood that the various forms of flow shown above can be reordered, added to, or deleted from without departing from the scope of the present application. For example, the steps recited in the present application can be performed in parallel, in series, or in a different order, as long as the desired results of the technical solutions of the present application can be achieved, which are not limited herein.

[0100] The above detailed description does not constitute a limitation on the protection scope of the present application. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements, and improvements made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for matching and detecting functional requirements, characterized in that, include: Obtain the function list and extract the function requirement information from the function list; Obtain the current size and current matching requirements of the functional module library, and obtain the current similarity threshold based on the current size and the current matching requirements; The target similarity score between each of the functional requirement information and each functional module in the functional module library is calculated, and the target functional module corresponding to each of the functional requirement information is obtained based on the target similarity score and the current similarity threshold.

2. The method according to claim 1, characterized in that, Based on the current scale and the current matching requirement, obtain the current similarity threshold, including: Based on the current scale and the current matching requirement, obtain the current adjustment coefficient, and based on the current adjustment coefficient and the preset basic similarity threshold, obtain the current similarity threshold.

3. The method according to claim 2, characterized in that, Based on the current scale and the current matching requirements, obtain the current adjustment coefficient, including: Based on the current scale and the preset correspondence between the scale and the adjustment coefficient, obtain the first adjustment coefficient; Based on the current matching requirements and the correspondence between the preset matching requirements and the adjustment coefficients, the second adjustment coefficient is obtained; The current adjustment coefficient is calculated based on the first adjustment coefficient and the second adjustment coefficient.

4. The method according to claim 1, characterized in that, The functional requirement information is extracted from the functional list, including: The functional list is preprocessed to obtain key information, and key entities are identified by using regular expressions and named entity recognition methods to obtain the functional requirement information.

5. The method according to claim 1, characterized in that, The target similarity score between each of the aforementioned functional requirement information and each functional module in the functional module library is calculated, including: Perform semantic analysis on the current functional requirement information to obtain the first semantic representation vector, and obtain the second semantic representation vector corresponding to the current functional module; Calculate the initial similarity scores for each preset dimension between the first semantic representation vector and the second semantic representation vector, and perform a weighted summation of each initial similarity score to obtain the sum value as the target similarity score between the current functional requirement information and the current functional module.

6. The method according to claim 5, characterized in that, Perform semantic analysis on the current functional requirements information to obtain the first semantic representation vector, including: Semantic analysis is performed on the current functional requirement information to obtain word vectors and sentence vectors, and the first semantic representation vector is generated based on the word vectors and sentence vectors.

7. The method according to any one of claims 1-6, characterized in that, Functional requirements information includes function name, function description and / or technical requirements.

8. An electronic device, characterized in that, The electronic device includes: At least one processor, and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the matching detection method for the functional requirements of any one of claims 1-7.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the matching detection method according to any one of claims 1-7.

10. A computer program product, characterized in that, It includes a computer program that, when executed by a processor, implements the matching detection method for the functional requirements of any one of claims 1-7.

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