Software demand analysis method and device, equipment and medium

Abstract

The embodiment of the invention discloses a software demand analysis method and device, equipment and a medium, and relates to the technical field of financial science and technology. The method comprises the steps of obtaining a to-be-processed software demand, and determining a corresponding candidate retrieval result from a preset domain knowledge base according to the to-be-processed software demand; determining a candidate demand distance between the to-be-processed software demand and each candidate retrieval result, and determining a target retrieval result from the candidate retrieval results according to the candidate demand distance; determining demand compensation data according to the target retrieval result and the to-be-processed software demand, and determining a target software demand according to the demand compensation data and the to-be-processed software demand; and inputting the target software demand into the trained demand analysis model to obtain a target demand result. And the richness of the data in the target software requirement is improved.

Description

Technical Field

[0001] Embodiments of the present invention relate to the field of financial technology, and in particular, to a software requirements analysis method, device, equipment and medium. Background Art

[0002] Requirements analysis is an important stage in the software development process. Generally, requirements acquisition and analysis are completed through multiple rounds of manual dialogue communication. Existing methods are difficult to capture the hidden knowledge in requirements, and important requirements are omitted. Model personalization also leads to unstable results. Requirements engineering is generally divided into requirements acquisition, requirements tracing, requirements classification, requirements analysis, and requirements generation. There is no mature solution for the challenging problems in each step. At present, the existing methods for requirements analysis mainly include requirements extraction based on expert systems, deep learning relationship extraction, and requirements analysis experts based on general large models. The existing requirements analysis methods have the problem that the data richness required for software requirements analysis is poor. Summary of the invention

[0003] The present invention provides a software requirement analysis method, device, equipment and medium to improve the richness of data required for software requirement analysis.

[0004] According to one aspect of the present invention, there is provided a software requirements analysis method, comprising:

[0005] Obtaining software requirements to be processed, and determining corresponding candidate search results from a preset domain knowledge base according to the software requirements to be processed;

[0006] Determine the candidate requirement distances between the to-be-processed software requirement and each of the candidate search results, and determine a target search result from the candidate search results according to the candidate requirement distances;

[0007] Determining demand compensation data according to the target search result and the software demand to be processed, and determining target software demand according to the demand compensation data and the software demand to be processed;

[0008] The target software requirements are input into the trained requirements analysis model to obtain the target requirements results.

[0009] According to another aspect of the present invention, there is provided a software requirement analysis device, comprising:

[0010] A candidate search result determination module is used to obtain the software requirements to be processed, and determine the corresponding candidate search results from a preset domain knowledge base according to the software requirements to be processed;

[0011] A target search result determination module, used to determine the candidate requirement distances between the to-be-processed software requirement and each of the candidate search results, and determine the target search result from the candidate search results according to the candidate requirement distances;

[0012] A target software requirement determination module, used to determine requirement compensation data according to the target search result and the software requirement to be processed, and determine the target software requirement according to the requirement compensation data and the software requirement to be processed;

[0013] The target requirement result determination module is used to input the target software requirement into the trained requirement analysis model to obtain the target requirement result.

[0014] According to another aspect of the present invention, there is provided an electronic device, comprising:

[0015] one or more processors;

[0016] A memory for storing one or more programs;

[0017] When one or more programs are executed by one or more processors, the one or more processors can execute any one of the software requirement analysis methods provided by the embodiments of the present invention.

[0018] According to another aspect of the present invention, a computer-readable storage medium is provided, which stores computer instructions, and the computer instructions are used to enable a processor to implement any software requirement analysis method provided by an embodiment of the present invention when executed.

[0019] The embodiment of the present invention provides a software requirement analysis scheme, which obtains the software requirement to be processed, and determines the corresponding candidate search results from a preset domain knowledge base according to the software requirement to be processed; determines the candidate requirement distance between the software requirement to be processed and each candidate search result, and determines the target search result from the candidate search result according to the candidate requirement distance; determines the requirement compensation data according to the target search result and the software requirement to be processed, and determines the target software requirement according to the requirement compensation data and the software requirement to be processed; inputs the target software requirement into a trained requirement analysis model to obtain the target requirement result. The above scheme determines the candidate search results corresponding to the software requirement to be processed from the domain knowledge base, determines the target search results according to the candidate requirement distance between the software requirement to be processed and the candidate search results, and then updates the software requirement to be processed according to the target search results and the requirement compensation data determined by the software requirement to be processed to obtain the target software requirement, thereby realizing the supplement of the upstream and downstream information of the software requirement to be processed, improving the richness of the data in the target software requirement, and thus improving the accuracy of the target requirement result obtained by subsequently inputting the target software requirement into the requirement analysis model.

[0020] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0022] Figure 1 is a flowchart of a software requirements analysis method provided by Embodiment 1 of the present invention;

[0023] Figure 2 is a flow chart of a software requirements analysis method provided by Embodiment 2 of the present invention;

[0024] Figure 3 This is a schematic diagram of a plug-in operation provided by Embodiment 3 of the present invention;

[0025] Figure 4 It is a structural diagram of a software requirement analysis device provided by Embodiment 4 of the present invention;

[0026] Figure 5 It is a structural diagram of an electronic device for implementing a software requirement analysis method provided in Embodiment 5 of the present invention. DETAILED DESCRIPTION

[0027] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the present invention, rather than to limit the present invention. It should also be noted that, for ease of description, only parts related to the present invention, rather than all structures, are shown in the accompanying drawings.

[0028] Embodiment 1

[0029] Figure 1 It is a flowchart of a software requirement analysis method provided in Example 1 of the present invention. This embodiment is applicable to the situation of analyzing software requirements. The method can be executed by a software requirement analysis device. The device can be implemented in software and / or hardware and can be configured in an electronic device that carries the software requirement analysis function.

[0030] See also Figure 1 The software requirements analysis method shown includes:

[0031] S110, obtaining the software requirements to be processed, and determining corresponding candidate search results from a preset domain knowledge base according to the software requirements to be processed.

[0032] The pending software requirement refers to the software requirement that needs to be analyzed. The domain knowledge base refers to a pre-built database for storing data related to software requirements. The candidate search results refer to the search results in the domain knowledge base that are associated with the pending software requirement.

[0033] S120, determining candidate requirement distances between the to-be-processed software requirement and each candidate search result, and determining a target search result from the candidate search results according to the candidate requirement distances.

[0034] The candidate requirement distance can be used to quantify the similarity between the software requirement to be processed and the candidate search result. The target search result refers to the candidate search result that can be used to provide upstream and downstream data for the software requirement to be processed.

[0035] Exemplarily, the candidate search results are vectorized, and the software requirements to be processed are vectorized; and the candidate requirement distance is determined based on the vectorized candidate search results and the vectorized software requirements to be processed.

[0036] Optionally, determine the candidate requirement distance between each candidate search result and the software requirement to be processed; sort the candidate search results from large to small according to the candidate requirement distance corresponding to each candidate search result, and use the first preset result quantity threshold candidate search results as target search results. The embodiment of the present invention does not impose any limitation on the size of the preset result quantity threshold, which can be set by technicians based on experience or needs, or determined repeatedly through a large number of experiments.

[0037] Alternatively, determine the candidate requirement distance between each candidate search result and the software requirement to be processed; for any candidate search result, if the candidate requirement distance of the candidate search result is greater than or equal to a preset requirement distance threshold, use the candidate search result as the target search result; if the candidate requirement distance of the candidate search result is less than the preset requirement distance threshold, use the candidate search result as the target search result. The embodiment of the present invention does not impose any limitation on the size of the preset requirement distance threshold, which can be set by a technician based on experience or determined repeatedly through a large number of experiments.

[0038] S130: Determine demand compensation data according to the target search result and the software demand to be processed, and determine the target software demand according to the demand compensation data and the software demand to be processed.

[0039] The demand compensation data refers to the upstream and downstream demand data of the pending software demand, that is, it is used to supplement the pending software demand. The target software demand refers to the software demand obtained after supplementing the pending software demand based on the demand compensation data.

[0040] In an optional embodiment, demand compensation data is determined based on the target retrieval results and the software requirements to be processed, including: determining target result data that matches the software requirements to be processed from the target retrieval results; updating the corresponding target retrieval results based on the target result data to obtain candidate compensation results; determining candidate compensation data in each candidate compensation result, and determining the demand compensation data based on the candidate compensation data, the software requirements to be processed and a preset character length.

[0041] Among them, the target result data refers to the data that needs to be deleted in the target search result. The candidate compensation result refers to the target search result after deleting the target result data. The candidate compensation data refers to the data that appears at least twice in the candidate compensation result. The preset character length can be used to limit the character length of the data in the target software requirements. The embodiment of the present invention does not impose any limitation on the size of the preset character length, which can be set by a technician based on experience or repeatedly determined through a large number of experiments.

[0042] Exemplarily, by performing grammatical analysis on the data in the target search results, candidate result data is obtained; the candidate result similarity between the software requirements to be processed and each candidate result data is determined; and the target result data is determined according to a preset upper similarity threshold, a preset lower similarity threshold and the candidate result similarity. Among them, the candidate result data refers to a data group obtained after dividing the data in the target search results. The candidate result similarity can be used to quantify the degree of similarity between the candidate result data and the software requirements to be processed. The embodiment of the present invention does not impose any restrictions on the size of the preset upper similarity threshold and the preset lower similarity threshold. It can be set by a technician based on experience, or determined repeatedly through a large number of experiments. It only needs to ensure that the preset upper similarity threshold is greater than the preset lower similarity threshold.

[0043] Exemplarily, for any candidate result data, if the candidate result similarity of the candidate result data is greater than or equal to a preset upper limit similarity threshold, the candidate result data is determined to be the target result data; if the candidate result similarity of the candidate result data is less than or equal to the preset lower limit similarity threshold, the candidate result data is determined to be the target result data.

[0044] The advantage of introducing a preset upper similarity threshold and a preset lower similarity threshold in the embodiment of the present invention is that data with higher or lower similarity between the target search results and the software requirements to be processed can be deleted, thereby reducing the amount of data and improving the efficiency of determining the requirement compensation data.

[0045] Exemplarily, the target result data in the target search result is deleted to obtain candidate compensation results; the candidate result data that appears at least twice in each candidate compensation result is used as candidate compensation data; and the required compensation data is determined based on the candidate compensation data, the software requirements to be processed and the preset character length.

[0046] It can be understood that by determining the target result data, the target retrieval result is updated to obtain the candidate compensation result, thereby filtering the data in the target retrieval result, avoiding the participation of irrelevant data, reducing the amount of data in the candidate compensation result, and improving the efficiency of subsequent determination of the required compensation data; at the same time, by introducing a preset character length, the character length of the required compensation data is restricted, avoiding the excessive length of characters in the subsequently generated target software requirements, and improving the rationality of the target software requirements.

[0047] In an optional embodiment, the required compensation data is determined based on the candidate compensation data, the software requirements to be processed and the preset character length, including: determining the amount of compensation data of the candidate compensation data, and determining the order of the compensation data based on the amount of compensation data; determining the target compensation data from the candidate compensation data based on the order of the compensation data; determining the required compensation data based on the target compensation data, the software requirements to be processed and the preset character length.

[0048] The number of compensation data refers to the number of times the candidate compensation data appears in each candidate compensation result. The order of compensation data refers to the result of sorting the candidate compensation data according to the number of compensation data. The target compensation data refers to the candidate compensation data that may be used to determine the required compensation data.

[0049] Exemplarily, for any candidate compensation data, determine the number of compensation data of the candidate compensation data in each candidate compensation result; sort the candidate compensation data from large to small according to the number of compensation data to obtain a compensation data sequence; according to the compensation data sequence, take the first preset compensation number threshold candidate compensation data as the target compensation data. The embodiment of the present invention does not impose any limitation on the size of the preset compensation number threshold, which can be set by a technician based on experience or determined repeatedly through a large number of experiments.

[0050] Exemplarily, the required character length of the software requirement to be processed is determined; the difference between the preset character length and the required character length is used as the target character length; and the required compensation data is determined according to the target character length and the target compensation data. The required character length refers to the character length of the data in the software requirement to be processed. The target character length refers to the character length of the required compensation data.

[0051] It can be understood that by determining the order of compensation data based on the number of compensation data of each candidate compensation data, and determining the target compensation data from the candidate compensation data based on the order of compensation data, the candidate compensation data is screened according to the number of compensation data, which further reduces the amount of data used to determine the required compensation data, and improves the efficiency of subsequently determining the required compensation data based on the target compensation data, the software requirements to be processed and the preset character length.

[0052] S140, input the target software requirements into the trained requirements analysis model to obtain the target requirements results.

[0053] The demand analysis model can be used to analyze software requirements and output corresponding demand results. The embodiment of the present invention does not impose any restrictions on the network structure of the demand analysis model, which can be set by technicians based on experience. The target demand result refers to the demand result corresponding to the target software requirement.

[0054] The embodiment of the present invention provides a software requirement analysis scheme, which obtains the software requirement to be processed, and determines the corresponding candidate search results from a preset domain knowledge base according to the software requirement to be processed; determines the candidate requirement distance between the software requirement to be processed and each candidate search result, and determines the target search result from the candidate search result according to the candidate requirement distance; determines the requirement compensation data according to the target search result and the software requirement to be processed, and determines the target software requirement according to the requirement compensation data and the software requirement to be processed; inputs the target software requirement into a trained requirement analysis model to obtain the target requirement result. The above scheme determines the candidate search results corresponding to the software requirement to be processed from the domain knowledge base, determines the target search results according to the candidate requirement distance between the software requirement to be processed and the candidate search results, and then updates the software requirement to be processed according to the target search results and the requirement compensation data determined by the software requirement to be processed to obtain the target software requirement, thereby realizing the supplement of the upstream and downstream information of the software requirement to be processed, improving the richness of the data in the target software requirement, and thus improving the accuracy of the target requirement result obtained by subsequently inputting the target software requirement into the requirement analysis model.

[0055] Embodiment 2

[0056] Figure 2It is a flowchart of a software requirement analysis method provided in the second embodiment of the present invention. Based on the above embodiments, this embodiment further refines the operation of "determining the candidate requirement distance between the software requirement to be processed and each candidate search result, and determining the target search result from the candidate search results based on the candidate requirement distance" into "determining the candidate file distance between the software requirement to be processed and each candidate requirement file, and determining the candidate dialogue distance between the software requirement to be processed and each candidate requirement dialogue; determining the target search result from the candidate requirement file and the candidate requirement dialogue based on the candidate file distance and the candidate dialogue distance; wherein the target search result includes the target requirement file and the target requirement dialogue" to improve the determination mechanism of the target search result. It should be noted that for the part not described in detail in the embodiment of the present invention, reference can be made to the description of other embodiments.

[0057] See also Figure 2 The software requirements analysis method shown includes:

[0058] S210, obtaining the software requirements to be processed, and determining corresponding candidate search results from a preset domain knowledge base according to the software requirements to be processed; wherein the candidate search results include candidate requirement files and candidate requirement dialogues.

[0059] The candidate requirement document refers to the requirement document associated with the pending software requirement stored in the domain knowledge base. The candidate requirement dialogue refers to the requirement dialogue associated with the pending software requirement stored in the domain knowledge base.

[0060] S220: Determine the candidate file distances between the pending software requirement and each candidate requirement file, and determine the candidate dialogue distances between the pending software requirement and each candidate requirement dialogue.

[0061] The candidate file distance refers to the similarity between the pending software requirement and the candidate requirement file. The candidate dialogue distance refers to the similarity between the pending software requirement and the candidate requirement dialogue.

[0062] S230: Determine a target search result from the candidate demand documents and the candidate demand dialogues according to the candidate document distance and the candidate dialogue distance.

[0063] Exemplarily, the target search results include target requirement files and / or target requirement dialogues. The target requirement files refer to candidate requirement files that can be used to provide upstream and downstream data for the software requirements to be processed. The target requirement dialogues refer to candidate dialogue files that can be used to provide upstream and downstream data for the software requirements to be processed.

[0064] In an optional embodiment, a target retrieval result is determined from candidate demand documents and candidate demand conversations based on a candidate file distance and a candidate conversation distance, including: determining a reference file distance of a corresponding candidate demand document based on a preset demand document weight and a candidate file distance; determining a reference conversation distance of a corresponding candidate demand conversation based on a preset demand conversation weight and a candidate conversation distance; and determining a target retrieval result from a candidate demand document and a candidate demand conversation based on a reference file distance and a reference conversation distance.

[0065] The preset requirement document weight can be used to characterize the importance of the candidate requirement document. The preset requirement conversation weight can be used to characterize the importance of the candidate requirement conversation. The embodiment of the present invention does not impose any restrictions on the size of the preset requirement document weight and the preset requirement conversation weight. The weight can be set by the technician according to experience or needs, or repeatedly determined through a large number of experiments. It only needs to ensure that the preset requirement document weight is less than the preset requirement conversation weight.

[0066] Exemplarily, the product of a preset demand file weight and a candidate file distance is used as a reference file distance; the product of a preset demand conversation weight and a candidate conversation distance is used as a reference conversation distance; based on the reference file distance and the reference conversation distance, the candidate demand files and candidate demand conversations are sorted from large to small, and the first preset result number threshold number of candidate demand files and / or candidate demand conversations are used as target retrieval results.

[0067] It can be understood that by updating the candidate file distance according to the preset demand file weight to obtain the reference file distance, and updating the candidate conversation distance according to the preset demand conversation weight to obtain the reference conversation distance, and then based on the reference file distance and the reference conversation distance, the target retrieval result is determined from the candidate demand files and the candidate demand conversations, thereby improving the accuracy of the determined target retrieval result.

[0068] In an optional embodiment, the reference file distance of the corresponding candidate demand file is determined according to the preset demand file weight and the candidate file distance, including: obtaining the file quality level of the candidate demand file, and determining the file weight coefficient corresponding to the file quality level according to the preset file weight-quality correspondence; determining the reference file distance of the corresponding candidate demand file according to the file weight coefficient, the preset demand file weight and the candidate file distance.

[0069] The file quality level can be used to quantify the quality of the candidate demand file. For example, the file quality level can be preset by a technician. The file weight quality correspondence refers to the correspondence between the file quality level and the file weight coefficient. The file weight coefficient refers to the coefficient of the preset demand file weight.

[0070] Exemplarily, the preset requirement file weight is updated according to the file weight coefficient of the candidate requirement file, and the reference file distance of the candidate requirement file is determined according to the updated preset requirement file weight and the candidate file distance.

[0071] It can be understood that by determining the file weight coefficient of the candidate demand file based on the correspondence between the file quality level and the file weight quality, and determining the reference file distance of the corresponding candidate demand file based on the file weight coefficient, the preset demand file weight and the candidate file distance, the accuracy of the determined reference file distance is improved.

[0072] S240: Determine demand compensation data according to the target search result and the software demand to be processed, and determine the target software demand according to the demand compensation data and the software demand to be processed.

[0073] S250, input the target software requirements into the trained requirements analysis model to obtain the target requirements results.

[0074] The embodiment of the present invention provides a software requirement analysis scheme, which refines the operation of determining the candidate requirement distance between the software requirement to be processed and each candidate search result respectively, and determining the target search result from the candidate search result according to the candidate requirement distance, into determining the candidate file distance between the software requirement to be processed and each candidate requirement file respectively, and determining the candidate dialogue distance between the software requirement to be processed and each candidate requirement dialogue respectively; determining the target search result from the candidate requirement file and the candidate requirement dialogue according to the candidate file distance and the candidate dialogue distance; wherein the target search result includes the target requirement file and the target requirement dialogue, thereby improving the determination mechanism of the target search result. The above scheme, by determining the candidate file distance between the software requirement to be processed and the candidate requirement file, determining the candidate dialogue distance between the software requirement to be processed and the candidate requirement dialogue, determining the target search result from the candidate requirement file and the candidate requirement dialogue according to the candidate file distance and the candidate dialogue distance, by dividing the candidate search result into the candidate requirement file and the candidate requirement dialogue, respectively determining the candidate file distance between the software requirement to be processed and the candidate requirement file, and the candidate dialogue distance between the software requirement to be processed and the candidate requirement dialogue, thereby improving the accuracy of the determined target search result.

[0075] Embodiment 3

[0076] The embodiment of the present invention provides an optional example of a software requirement analysis method. It should be noted that for the parts not described in detail in the embodiment of the present invention, reference may be made to the descriptions of other embodiments.

[0077] The existing steps of demand analysis based on deep learning models include: data collection and cleaning, key information extraction, feature extraction and model training. Specifically, data collection and processing refers to mining user needs and solutions from existing demand documents and massive corpus. Entity relationship extraction in demand documents, using open source entity relationship joint extraction model to identify domain triple information in demand documents, and complete demand function point analysis. Model training, based on demand document corpus and preprocessed functional demand points and solution steps. Generate demand analysis, input user demand description, and the deep learning model generates corresponding functional demand design solutions and verification points and other suggestions.

[0078] The expert system-based method is to customize the functional requirements design process template through prior classification, and perform demand analysis based on similarity matching according to the user's demand description, but the user's ability to understand demand is limited, and the similarity matching expert template cannot meet user needs to a limited extent. The deep learning-based method is to extract the demand points and corresponding demand solutions in the relevant materials of the demand document in sequence through deep learning technology, but there are problems such as large data annotation workload, lack of data, and weak logic of functional design extracted by the model; for the existing solutions for intelligent demand analysis through large models, there are problems such as lack of data and general quality of intelligent demand.

[0079] The embodiment of the present invention performs intelligent demand analysis based on a large domain model and core algorithm modules. After the service is deployed, it can be used by developers in the form of a Web (World Wide Web) or an integrated development environment plug-in. The main steps are to first collect and organize various demand documents and obtain data sets to make up for the problem of data scarcity; through model fine-tuning and instruction set training, the ability to understand user needs and design needs according to needs is guaranteed; the large domain model and knowledge base retrieval enhancement solution are adopted to automatically obtain user needs from multiple rounds of dialogues, and suggestions such as demand analysis design content and workload assessment are given respectively. At the same time, similar demand documents in the field are given for users to assist in reference, which helps the intelligent process of each task in the R&D process. The present invention realizes intelligent demand analysis and function design generation, and improves R&D efficiency.

[0080] The software requirement analysis method provided by the embodiment of the present invention includes: data collection and processing, model fine-tuning and instruction set training, service development and deployment, and automatic generation of requirement analysis. Exemplarily, data collection and processing: collect requirement documents, build a knowledge base and a model training set respectively, and use a framework based on a large language model to develop applications for retrieval enhancement. Model fine-tuning and instruction set training: clarify fine-tuning tasks and loss functions, etc., set appropriate hyperparameters including learning rate, batch size, number of iterations, etc., to ensure that the requirement analysis model is evaluated during the fine-tuning process. Service development and deployment: implement service development and deployment based on commonly used programming language technologies, and develop plug-ins based on plug-in interface documents of software such as integrated development environments. Automatic generation of requirement analysis. The requirement analysis service can be called and displayed through the Web or plug-in.

[0081] For example, data collection can be achieved through a Web site to achieve data set co-construction, and various demand documents can be uploaded on the web page and stored after review. The data set co-construction interactive interface can include functions such as data upload, approval progress, storage status, document description information, quality evaluation, and editability.

[0082] Exemplarily, in data preprocessing and knowledge base construction, the batch uploaded documents are first desensitized and then reviewed to decide whether to use them as training sets or domain knowledge bases, and the demand analysis model training set and knowledge base are completed respectively.

[0083] Exemplarily, fine-tuning and instruction set training are performed based on the preprocessed data set and the demand analysis model, and a lightweight fine-tuning method is adopted, that is, an additional low-rank matrix is ​​added to the preset specified parameters on the demand analysis model to achieve controllable overhead and achieve better results. The preset specified parameters refer to the parameters that are predetermined and need to be fine-tuned in the demand analysis model. The embodiment of the present invention does not impose any limitation on the preset specified parameters, which can be set by technicians based on experience or needs.

[0084] Exemplary, service development and deployment, service development and deployment are implemented based on commonly used programming language technologies, specifically designing gateway modules and microservice development, and performing retrieval enhancement based on the knowledge base and the framework for developing applications based on a large language model.

[0085] For example, in plug-in development, determine the plug-in extension points and event monitoring: the extended functions are realized through extension points, which mainly include menu items, toolbar buttons, shortcut keys, etc. Event monitoring can capture the defined actions in time and trigger the execution logic to realize related requirements.

[0086] For example, the operation mechanism of the plug-in is mainly based on the ability of the plug-in operation framework to execute related extension plug-ins, where the development of the plug-in is based on the framework communication contract interface, and the specific plug-in project mainly implements business entities, logic and related interface operations, such as Figure 3 The plug-in operation diagram shown.

[0087] Exemplarily, the demand analysis is automatically generated, and the demand analysis service call and user interaction can be performed through the Web terminal or plug-in.

[0088] The embodiment of the present invention relies on development tools such as Web or plug-ins to realize the generation of demand analysis content based on the fine-tuned training demand analysis model. Exemplarily, the intelligent demand interaction interface design input includes user demand description and acceptance criteria in the development document. After clicking the submit button, the backend demand analysis model service is called to return the demand design content and the workload evaluation and specific plan of the demand development task.

[0089] A software requirement analysis method provided by an embodiment of the present invention is provided to users in the form of a Web or a plug-in, which can automatically obtain user requirements (i.e., target software requirements) from multiple rounds of dialogues, and provide suggestions such as requirement analysis design content and workload assessment (i.e., target requirement results), and provide similar requirement documents in the field for users' auxiliary reference, thereby facilitating the intelligent process of each task in the R&D process.

[0090] It should be noted that the relevant information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for display, data for analysis, etc.) involved in the embodiments of the present invention are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data comply with the relevant laws, regulations and standards of the relevant regions.

[0091] Embodiment 4

[0092] Figure 4 This is a schematic diagram of the structure of a software requirement analysis device provided in Embodiment 4 of the present invention. This embodiment is applicable to the situation of analyzing software requirements, and the method can be executed by a software requirement analysis device, which can be implemented in software and / or hardware, and can be configured in an electronic device that carries the software requirement analysis function.

[0093] like Figure 4 As shown, the device includes: a candidate search result determination module 310, a target search result determination module 320, a target software requirement determination module 330 and a target requirement result determination module 340. Among them,

[0094] The candidate search result determination module 310 is used to obtain the software requirements to be processed, and determine the corresponding candidate search results from the preset domain knowledge base according to the software requirements to be processed;

[0095] A target search result determination module 320 is used to determine the candidate requirement distances between the to-be-processed software requirement and each of the candidate search results, and determine a target search result from the candidate search results according to the candidate requirement distances;

[0096] A target software requirement determination module 330 is used to determine requirement compensation data according to the target search result and the software requirement to be processed, and to determine the target software requirement according to the requirement compensation data and the software requirement to be processed;

[0097] The target requirement result determination module 340 is used to input the target software requirement into the trained requirement analysis model to obtain the target requirement result.

[0098] The embodiment of the present invention provides a software requirement analysis scheme, which obtains the software requirement to be processed, and determines the corresponding candidate search results from a preset domain knowledge base according to the software requirement to be processed; determines the candidate requirement distance between the software requirement to be processed and each candidate search result, and determines the target search result from the candidate search result according to the candidate requirement distance; determines the requirement compensation data according to the target search result and the software requirement to be processed, and determines the target software requirement according to the requirement compensation data and the software requirement to be processed; inputs the target software requirement into a trained requirement analysis model to obtain the target requirement result. The above scheme determines the candidate search results corresponding to the software requirement to be processed from the domain knowledge base, determines the target search results according to the candidate requirement distance between the software requirement to be processed and the candidate search results, and then updates the software requirement to be processed according to the target search results and the requirement compensation data determined by the software requirement to be processed to obtain the target software requirement, thereby realizing the supplement of the upstream and downstream information of the software requirement to be processed, improving the richness of the data in the target software requirement, and thus improving the accuracy of the target requirement result obtained by subsequently inputting the target software requirement into the requirement analysis model.

[0099] Optionally, if the candidate search results include candidate requirement documents and candidate requirement dialogues, the target search result determination module 320 includes:

[0100] A candidate distance determination unit, used to determine the candidate file distance between the software requirement to be processed and each of the candidate requirement files, and determine the candidate dialogue distance between the software requirement to be processed and each of the candidate requirement dialogues;

[0101] The target search result determination unit is used to determine the target search result from the candidate demand documents and the candidate demand dialogues according to the candidate document distance and the candidate dialogue distance; wherein the target search result includes the target demand document and the target demand dialogue.

[0102] Optionally, the target search result determination unit includes:

[0103] A reference file distance determination subunit, used to determine the reference file distance of the corresponding candidate demand file according to the preset demand file weight and the candidate file distance;

[0104] A reference dialogue distance determination subunit, used to determine the reference dialogue distance of the corresponding candidate demand dialogue according to the preset demand dialogue weight and the candidate dialogue distance;

[0105] The target retrieval result determination subunit is used to determine the target retrieval result from the candidate demand documents and the candidate demand dialogues according to the reference document distance and the reference dialogue distance.

[0106] Optionally, the reference file distance determination subunit is specifically used to:

[0107] Obtaining the file quality level of the candidate demand file, and determining the file weight coefficient corresponding to the file quality level according to a preset file weight quality correspondence relationship;

[0108] The reference file distance of the corresponding candidate requirement file is determined according to the file weight coefficient, the preset requirement file weight and the candidate file distance.

[0109] Optionally, the target software requirement determination module 330 includes:

[0110] A target result data determination unit, used to determine target result data matching the software requirement to be processed from the target search result;

[0111] A candidate compensation result determination unit, configured to update the corresponding target search result according to the target result data to obtain a candidate compensation result;

[0112] The required compensation data determining unit is used to determine the candidate compensation data in each of the candidate compensation results, and determine the required compensation data according to the candidate compensation data, the software requirement to be processed and the preset character length.

[0113] Optionally, the demand compensation data determining unit is specifically configured to:

[0114] Determining the number of compensation data of the candidate compensation data, and determining the compensation data order according to the number of compensation data;

[0115] determining target compensation data from the candidate compensation data according to the compensation data sequence;

[0116] Determine the required compensation data according to the target compensation data, the software requirement to be processed and the preset character length.

[0117] The software requirement analysis device provided in the embodiment of the present invention can execute the software requirement analysis method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing each software requirement analysis method.

[0118] In the technical solution of the present invention, the collection, storage, use, processing, transmission, provision and disclosure of the pending software requirements, preset requirement file weights and preset requirement dialogue weights are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0119] According to an embodiment of the present invention, the present invention also provides an electronic device, a readable storage medium and a computer program product.

[0120] Embodiment 5

[0121] Figure 5 It is a structural diagram of an electronic device for implementing a software requirements analysis method provided by Embodiment 5 of the present invention. Electronic device 410 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0122] like Figure 5As shown, the electronic device 410 includes at least one processor 411, and a memory connected to the at least one processor 411 in communication, such as a read-only memory (ROM) 412, a random access memory (RAM) 413, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 411 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 412 or the computer program loaded from the storage unit 418 to the random access memory (RAM) 413. In the RAM 413, various programs and data required for the operation of the electronic device 410 can also be stored. The processor 411, the ROM 412, and the RAM 413 are connected to each other via a bus 414. An input / output (I / O) interface 415 is also connected to the bus 414.

[0123] Multiple components in the electronic device 410 are connected to the I / O interface 415, including: an input unit 416, such as a keyboard, a mouse, etc.; an output unit 417, such as various types of displays, speakers, etc.; a storage unit 418, such as a disk, an optical disk, etc.; and a communication unit 419, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 419 allows the electronic device 410 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0124] The processor 411 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 411 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 411 executes the various methods and processes described above, such as a software requirements analysis method.

[0125] In some embodiments, the software requirement analysis method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 418. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 410 via the ROM 412 and / or the communication unit 419. When the computer program is loaded into the RAM 413 and executed by the processor 411, one or more steps of the software requirement analysis method described above may be performed. Alternatively, in other embodiments, the processor 411 may be configured to perform the software requirement analysis method in any other appropriate manner (e.g., by means of firmware).

[0126] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0127] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0128] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0129] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0130] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may 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 (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0131] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0132] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0133] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A software requirements analysis method, characterized in that: include: Obtaining software requirements to be processed, and determining corresponding candidate search results from a preset domain knowledge base according to the software requirements to be processed; Determine the candidate requirement distances between the to-be-processed software requirement and each of the candidate search results, and determine a target search result from the candidate search results according to the candidate requirement distances; Determining demand compensation data according to the target search result and the software demand to be processed, and determining target software demand according to the demand compensation data and the software demand to be processed; The target software requirements are input into the trained requirements analysis model to obtain the target requirements results.

2. The method according to claim 1, characterized in that If the candidate search results include candidate requirement files and candidate requirement dialogues, determining the candidate requirement distances between the to-be-processed software requirement and each of the candidate search results, and determining a target search result from the candidate search results according to the candidate requirement distances, includes: Determine the candidate file distances between the software requirement to be processed and each of the candidate requirement files, and determine the candidate dialogue distances between the software requirement to be processed and each of the candidate requirement dialogues; According to the candidate document distance and the candidate dialogue distance, a target search result is determined from the candidate demand document and the candidate demand dialogue; wherein the target search result includes a target demand document and a target demand dialogue.

3. The method according to claim 2, characterized in that The step of determining a target search result from the candidate demand document and the candidate demand dialogue according to the candidate document distance and the candidate dialogue distance includes: Determining the reference file distance of the corresponding candidate demand file according to the preset demand file weight and the candidate file distance; Determining a reference dialogue distance of a corresponding candidate demand dialogue according to a preset demand dialogue weight and the candidate dialogue distance; According to the reference document distance and the reference dialogue distance, a target retrieval result is determined from the candidate demand documents and the candidate demand dialogues.

4. The method according to claim 3, characterized in that The step of determining the reference file distance of the corresponding candidate demand file according to the preset demand file weight and the candidate file distance includes: Obtaining the file quality level of the candidate demand file, and determining the file weight coefficient corresponding to the file quality level according to a preset file weight quality correspondence relationship; The reference file distance of the corresponding candidate requirement file is determined according to the file weight coefficient, the preset requirement file weight and the candidate file distance.

5. The method according to claim 1, characterized in that The step of determining demand compensation data according to the target search result and the software demand to be processed includes: Determining target result data matching the software requirement to be processed from the target search results; According to the target result data, the corresponding target search result is updated to obtain a candidate compensation result; Determine candidate compensation data in each of the candidate compensation results, and determine required compensation data according to the candidate compensation data, the software requirement to be processed and the preset character length.

6. The method according to claim 5, characterized in that The step of determining the required compensation data according to the candidate compensation data, the software requirement to be processed and the preset character length includes: Determining the number of compensation data of the candidate compensation data, and determining the compensation data order according to the number of compensation data; determining target compensation data from the candidate compensation data according to the compensation data sequence; Determine the required compensation data according to the target compensation data, the software requirement to be processed and the preset character length.

7. A software requirement analysis device, characterized in that: include: A candidate search result determination module is used to obtain the software requirements to be processed, and determine the corresponding candidate search results from a preset domain knowledge base according to the software requirements to be processed; A target search result determination module, used to determine the candidate requirement distances between the to-be-processed software requirement and each of the candidate search results, and determine the target search result from the candidate search results according to the candidate requirement distances; A target software requirement determination module, used to determine requirement compensation data according to the target search result and the software requirement to be processed, and determine the target software requirement according to the requirement compensation data and the software requirement to be processed; The target requirement result determination module is used to input the target software requirement into the trained requirement analysis model to obtain the target requirement result.

8. An electronic device, characterized in that: include: one or more processors; A memory for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement a software requirement analysis method as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, a software requirement analysis method as described in any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program / instruction, wherein when the computer program / instruction is executed by a processor, the steps of the software requirement analysis method according to any one of claims 1 to 6 are implemented.

Images