CN117035515A - Workload evaluation method, workload evaluation device, computer equipment and storage medium - Google Patents
Workload evaluation method, workload evaluation device, computer equipment and storage medium Download PDFInfo
- Publication number
- CN117035515A CN117035515A CN202310994689.3A CN202310994689A CN117035515A CN 117035515 A CN117035515 A CN 117035515A CN 202310994689 A CN202310994689 A CN 202310994689A CN 117035515 A CN117035515 A CN 117035515A
- Authority
- CN
- China
- Prior art keywords
- workload
- project
- index
- indexes
- evaluation
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000011156 evaluation Methods 0.000 title claims abstract description 105
- 238000000034 method Methods 0.000 claims abstract description 40
- 238000012360 testing method Methods 0.000 claims description 48
- 238000011161 development Methods 0.000 claims description 31
- 230000018109 developmental process Effects 0.000 claims description 31
- 238000004422 calculation algorithm Methods 0.000 claims description 24
- 230000002068 genetic effect Effects 0.000 claims description 15
- 238000012549 training Methods 0.000 claims description 15
- 238000004590 computer program Methods 0.000 claims description 9
- 230000035772 mutation Effects 0.000 claims description 9
- 230000000694 effects Effects 0.000 claims description 8
- 238000012423 maintenance Methods 0.000 claims description 6
- 238000012216 screening Methods 0.000 claims description 6
- 210000000349 chromosome Anatomy 0.000 claims description 3
- 238000009933 burial Methods 0.000 claims description 2
- 238000005516 engineering process Methods 0.000 abstract description 7
- 238000013473 artificial intelligence Methods 0.000 abstract description 2
- 238000004364 calculation method Methods 0.000 description 7
- 238000010586 diagram Methods 0.000 description 6
- 238000013461 design Methods 0.000 description 4
- 238000010276 construction Methods 0.000 description 3
- 238000012545 processing Methods 0.000 description 3
- 238000011160 research Methods 0.000 description 3
- 238000012356 Product development Methods 0.000 description 2
- 230000001419 dependent effect Effects 0.000 description 2
- 238000000605 extraction Methods 0.000 description 2
- 238000003062 neural network model Methods 0.000 description 2
- 230000001360 synchronised effect Effects 0.000 description 2
- 238000012935 Averaging Methods 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000003066 decision tree Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 238000002372 labelling Methods 0.000 description 1
- 238000007477 logistic regression Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000007637 random forest analysis Methods 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
- 230000003245 working effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Strategic Management (AREA)
- Development Economics (AREA)
- Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- Educational Administration (AREA)
- Operations Research (AREA)
- Marketing (AREA)
- Game Theory and Decision Science (AREA)
- Quality & Reliability (AREA)
- Tourism & Hospitality (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The application relates to the technical field of artificial intelligence and financial science and technology, and discloses a workload assessment method, a workload assessment device, a workload assessment computer device and a workload assessment storage medium, wherein the workload assessment method comprises the following steps: selecting key indexes affecting the workload from all indexes contained in all evaluation dimensions; acquiring all key indexes contained in each evaluation dimension of the project requirement to be evaluated; and obtaining a workload evaluation result of the project requirement to be evaluated according to the key index of the project requirement to be evaluated. According to the method, the key indexes related to the project demand workload are extracted in advance, and then the workload of the project demand to be evaluated is evaluated according to the key indexes, so that interference of irrelevant indexes can be reduced, the influence of the key indexes on the workload is excavated, and the accuracy and timeliness of evaluating the project demand workload are improved.
Description
Technical Field
The present application relates to the field of artificial intelligence and financial technology, and in particular, to a workload assessment method, a workload assessment device, a workload assessment computer device, and a workload assessment storage medium.
Background
Assessment workload is a common and important part of enterprise work and is a troublesome matter. Whether the workload estimation is accurate can affect the working quality and efficiency.
The workload estimation in the prior art may deviate due to various reasons such as inaccurate selected evaluation dimension, lack of evaluation of working details, and the like, so that an evaluation result is inaccurate. Especially in the field of financial science and technology, such as online business rapid development of banks, insurance and the like, the new products/new systems have huge demand, and developers are required to follow project demands to meet the business development demands. Therefore, the workload estimation of project requirements has great reference significance to development and development work.
Disclosure of Invention
The application mainly aims to provide a workload assessment method, a workload assessment device, computer equipment and a storage medium, which can solve the technical problem of inaccurate workload assessment in the prior art.
To achieve the above object, a first aspect of the present application provides a workload assessment method, including:
selecting key indexes affecting the workload from all indexes contained in all evaluation dimensions;
acquiring all key indexes contained in each evaluation dimension of the project requirement to be evaluated;
and obtaining a workload evaluation result of the project requirement to be evaluated according to the key index of the project requirement to be evaluated.
To achieve the above object, a second aspect of the present application provides a workload assessment apparatus, comprising:
The screening module is used for selecting key indexes affecting the workload from all indexes contained in all evaluation dimensions;
the first data acquisition module is used for acquiring all key indexes contained in each evaluation dimension of the project requirement to be evaluated;
and the evaluation module is used for obtaining a workload evaluation result of the project requirement to be evaluated according to the key index of the project requirement to be evaluated.
To achieve the above object, a third aspect of the present application provides a computer-readable storage medium storing a computer program which, when executed by a processor, causes the processor to perform the steps of:
selecting key indexes affecting the workload from all indexes contained in all evaluation dimensions;
acquiring all key indexes contained in each evaluation dimension of the project requirement to be evaluated;
and obtaining a workload evaluation result of the project requirement to be evaluated according to the key index of the project requirement to be evaluated.
To achieve the above object, a fourth aspect of the present application provides a computer apparatus including a memory and a processor, the memory storing a computer program which, when executed by the processor, causes the processor to perform the steps of:
Selecting key indexes affecting the workload from all indexes contained in all evaluation dimensions;
acquiring all key indexes contained in each evaluation dimension of the project requirement to be evaluated;
and obtaining a workload evaluation result of the project requirement to be evaluated according to the key index of the project requirement to be evaluated.
The embodiment of the application has the following beneficial effects:
according to the method, the key indexes related to the project demand workload are extracted in advance, and then the workload of the project demand to be evaluated is evaluated according to the key indexes, so that the interference of irrelevant indexes can be reduced, the influence of the key indexes on the workload is excavated, the accuracy and timeliness of the project demand workload evaluation are improved, and the method has guiding significance for improving the working quality. Especially in the field of finance and technology, such as online business rapid development of banks, insurance and the like, the new product/new system demand is huge, developers are required to follow project demands to meet the business development demands, and accurate estimation of the workload of the project demands has great reference significance for research, development and development work.
Drawings
In order to more clearly illustrate the embodiments of the application or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Wherein:
FIG. 1 is a diagram of an application environment of a method for evaluating a workload in an embodiment of the present application;
FIG. 2 is a flow chart of a method for evaluating a workload in an embodiment of the present application;
FIG. 3 is a block diagram showing the construction of an apparatus for evaluating a work amount in an embodiment of the present application;
fig. 4 is a block diagram of a computer device in an embodiment of the application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
FIG. 1 is an application environment diagram of a method of evaluating a work volume in one embodiment. Referring to fig. 1, the workload assessment method is applied to a workload assessment system. The workload assessment system includes a terminal 110 and a server 120. The terminal 110 and the server 120 are connected through a network, and the terminal 110 may be a desktop terminal or a mobile terminal, and the mobile terminal may be at least one of a mobile phone, a tablet computer, a notebook computer, and the like. The server 120 may be implemented as a stand-alone server or as a server cluster composed of a plurality of servers. The terminal 110 is configured to send a workload evaluation instruction according to the user instruction to the server 120, and the server 120 is configured to select, according to the workload evaluation instruction, a key index affecting workload from all indexes included in all evaluation dimensions; acquiring all key indexes contained in each evaluation dimension of the project requirement to be evaluated; and obtaining a workload evaluation result of the project requirement to be evaluated according to the key index of the project requirement to be evaluated.
As shown in FIG. 2, in one embodiment, a workload assessment method is provided. The method can be applied to the terminal and the server. The workload assessment method specifically comprises the following steps:
s100: and selecting key indexes influencing the workload from all indexes contained in all evaluation dimensions.
Specifically, the evaluation dimensions are dimensions of the workload required to evaluate project requirements, each evaluation dimension including at least one index.
Some of the various indexes contained in the evaluation dimension are related to the workload, and some indexes are unrelated to the workload. Therefore, in order to improve the accuracy of workload assessment, key indexes affecting the workload may be screened in advance.
The screening of the key indexes can be selected by using genetic algorithm, demand analysis model and other methods.
S200: and acquiring all key indexes contained in the requirements of the item to be evaluated in each evaluation dimension.
Specifically, the project requirements to be evaluated include the work to be completed for completing one project, and may be the back-end project requirements in the development of the internet technology, the front-end project requirements, and the like.
The key indicators may also be different depending on the type of item requirements to be evaluated.
S300: and obtaining a workload evaluation result of the project requirement to be evaluated according to the key index of the project requirement to be evaluated.
Specifically, the workload can be estimated through a neural network model, or can be estimated through scoring key indexes.
According to the method, the device and the system, the key indexes related to project demand workload are extracted in advance, then the workload of the project demand to be evaluated is evaluated according to the key indexes, interference of irrelevant indexes can be reduced, influence of the key indexes on the workload is mined, accuracy and timeliness of evaluating the project demand workload are improved, and guiding significance is provided for improving the working quality. Especially in the field of finance and technology, such as online business rapid development of banks, insurance and the like, the new product/new system demand is huge, developers are required to follow project demands to meet the business development demands, and accurate estimation of the workload of the project demands has great reference significance for research, development and development work.
In one embodiment, step S300 specifically includes:
determining an option score of a target index based on a preset scoring rule, wherein the target index is any key index of the requirement of the item to be evaluated;
obtaining dimension scores of target evaluation dimensions according to option scores of all target indexes contained in the target evaluation dimensions, wherein the target evaluation dimensions are any evaluation dimension;
And carrying out weighted summation on the dimension scores of all the evaluation dimensions to obtain a workload score of the requirements of the item to be evaluated, and taking the workload score as a workload evaluation result.
Specifically, the preset scoring rule specifies the option scores corresponding to each index within a certain value or a certain value range, so that the option scores of the indexes can be obtained according to the actual values of the indexes.
In addition, the option scores of all target indexes contained in the target evaluation dimension are summed to obtain a dimension score of the target evaluation dimension; or, weighting and summing the option scores of all the target indexes contained in the target evaluation dimension to obtain the dimension score of the target evaluation dimension.
Furthermore, the weight of the evaluation dimension is configured according to the actual situation, which is not limited by the present application.
In the embodiment, the key index related to the workload is determined, then the option score is determined according to the value of the key index in a scoring mode, the dimension score of each evaluation dimension is calculated, and the weighted summation is carried out on the dimension scores to obtain the workload score. The selected key indexes are related to the workload, so that the interference of irrelevant indexes is removed, the related calculation of the irrelevant indexes is reduced, and the efficiency of calculating the workload score is also improved by a scoring mode, thereby being capable of rapidly and accurately acquiring the workload evaluation result of the requirements of the project to be evaluated.
In one embodiment, step S300 specifically includes:
constructing a data set according to the selected key indexes, wherein the data set comprises a plurality of samples, each sample comprises the key indexes of the known project requirements, and the labels of the samples are the known workload scores of the known project requirements;
training the workload scoring model to be trained by utilizing the data set to obtain a trained workload scoring model;
inputting the key indexes of the project requirements to be evaluated into a trained workload scoring model to obtain workload scores of the project requirements to be evaluated, and taking the workload scores as workload evaluation results.
Specifically, the known project requirements are historical project requirements that have been developed. By analyzing various historical project requirements, candidate evaluation dimensions related to workload and candidate indexes of the candidate evaluation dimensions can be obtained.
The candidate evaluation dimension and the candidate index of the known project requirement are analyzed, and the evaluation dimension and the key index related to the project requirement workload can be determined.
And according to the selected evaluation dimension and key index, retaining the key index and the corresponding evaluation dimension of the known project requirement, and removing other non-key indexes and the corresponding candidate evaluation dimension to obtain a sample corresponding to each known project requirement. In addition, each known project requirement is completed, the workload of which is known.
After labeling the samples, a dataset is obtained.
The data set may be divided into a training set and a test set. And training the workload scoring model to be trained by using the training set, and verifying the workload estimation effect of the trained workload scoring model by using the testing set. And after the verification is passed, obtaining a trained workload scoring model.
Inputting key indexes of the project requirements to be evaluated into a trained workload scoring model, and estimating the workload scores of the project requirements to be evaluated by using the trained workload scoring model.
In addition, the workload scoring model to be trained can be constructed by other algorithms such as an algorithm based on logistic regression, an algorithm based on decision trees, an algorithm based on random forests, and the like, and the workload scoring model to be trained is not limited in this regard.
According to the method, the device and the system, the key indexes related to the workload are determined, the data set is constructed according to the key indexes, the workload scoring model is trained by utilizing the data set, the selected key indexes are related to the workload, interference of irrelevant indexes is removed, model training cost is reduced, model training speed and model evaluation accuracy are improved, hidden relations and rules between the key indexes and the workload can be captured through the neural network model, and therefore workload evaluation results of requirements of projects to be evaluated can be rapidly and accurately obtained.
In one embodiment, step S100 specifically includes:
acquiring known indexes contained in the known project requirements in each evaluation dimension;
randomly extracting any one known item requirement, acquiring known indexes of the extracted known item requirement on each evaluation dimension, and determining a known index combination with highest fitness according to the maximum iteration times, the crossover probability and the variation probability of a genetic algorithm, wherein the known index combination with highest fitness comprises part of known indexes in the known indexes of the extracted known item requirement;
and determining key indexes according to all known index combinations with highest fitness, wherein each extracted known item requirement corresponds to one known index combination with highest fitness.
Specifically, in this embodiment, the key index selection is performed by a genetic algorithm.
The extracted known item requirements comprise a plurality of known indexes, and a plurality of different known index combinations can be obtained by randomly combining the known indexes. And combining each known index as an individual to form an initial population, wherein the size of the initial population is set according to actual conditions. And calculating the fitness of the individual, and carrying out selection operation, crossover operation and mutation operation on the individual according to the fitness, crossover probability and mutation probability of the individual so as to evolve the individual and generate a next generation population. If the number of evolutions does not reach the maximum number of evolutions, continuing to execute the calculation of the individual fitness for the population of the next generation, and carrying out the selection operation, the crossover operation and the mutation operation on the individual according to the individual fitness, the crossover probability and the mutation probability so as to evolve the individual and generate the population of the next generation until the number of evolutions reaches the maximum number of evolutions, and stopping the genetic algorithm. And acquiring the known index combination with the highest adaptability corresponding to the requirement of the known item selected at the time.
According to the method, different known project requirements are extracted for a plurality of times without replacement, and one known project requirement is extracted at a time. The genetic algorithm obtains the known index combination with highest fitness through the steps according to the known index of the known project requirement extracted each time.
The number of extraction may be configured according to practical situations, which is not limited by the present application.
Through multiple extraction, multiple calculation of known index combinations with highest fitness is performed through a genetic algorithm, key indexes are determined according to all known index combinations with highest fitness, accidental factors can be eliminated as much as possible, the selected key indexes are more convincing, and further the accuracy of workload assessment is improved.
In one embodiment, determining the key indicator from all known indicator combinations with highest fitness comprises:
counting the occurrence times of the same known index in all the known index combinations with the highest fitness;
and determining a key index according to the occurrence times.
Specifically, a known index whose number of occurrences exceeds a number threshold is selected as a key index.
Or selecting the first preset number of known indexes with highest frequency ranking as key indexes. For example, the top 10 known indicators with the highest ranking of occurrence numbers are selected as key indicators.
Of course, the above-mentioned frequency threshold and preset number are configured according to practical situations, and the present application is not limited thereto.
In another embodiment, in order to ensure that each evaluation dimension includes at least one key indicator, a known indicator that the number of occurrences exceeds the threshold number of occurrences may be selected as the key indicator, or a top preset number of known indicators that the number of occurrences is highest in rank may be selected as the key indicator.
If the key indexes are only indexes of partial evaluation dimensions and not cover all the evaluation dimensions, at least one index of the uncovered evaluation dimensions is selected according to the occurrence number and added to the key indexes. Wherein the selected index is one or more of the highest occurrence frequency.
In addition, the weight proportion between the key indexes and/or between the evaluation dimensions corresponding to the key indexes can be determined according to the occurrence times of the key indexes, and the higher the occurrence times, the larger the weight. I.e. the weight is proportional to the number of occurrences.
According to the embodiment, the key indexes are selected according to the occurrence times of the indexes, so that the interferences of accidental factors are eliminated as much as possible, the selected key indexes are more convincing, and the accuracy of workload assessment is further improved.
In one embodiment, determining the combination of known indicators with highest fitness based on the maximum number of iterations, crossover probability, and mutation probability of the genetic algorithm comprises:
randomly combining all known indexes of the selected known project requirements to obtain a plurality of known index combinations, and taking each known index combination as an individual;
binary coding is carried out on each individual, an initial population is constructed according to the obtained chromosome, and the initial population is used as a current population;
calculating individual fitness of each individual in the current population;
selecting, intersecting and mutating the individuals of the current population according to the individual fitness, intersecting probability and mutating probability to obtain a next generation population;
judging whether the current iteration number reaches the maximum iteration number, if not, taking the next generation population as the current population, and executing the steps to calculate the individual fitness of each individual in the current population and the subsequent steps until the current iteration number reaches the maximum iteration number;
and determining the known index combination with the highest fitness according to the individual corresponding to the maximum individual fitness.
Specifically, a known workload score for a known project demand is obtained.
A known index is combined into an individual. The population includes a plurality of known index combinations.
The genetic algorithm determines the calculation mode of the fitness according to the optimized target.
In one embodiment, a combination of known metrics is input to a workload scoring model to be trained, which derives a predictive score based on the input combination of known metrics.
And obtaining a scoring error according to the prediction scoring and the known workload scoring of the selected known project requirement, wherein the scoring error is a non-negative number, and the reciprocal of the scoring error is taken as the individual fitness, or the scoring error takes a negative value and is taken as the individual fitness.
Wherein all individuals use the same workload scoring model to be trained.
In one embodiment, an initial training set may be constructed based on all indicators of known project requirements and known workload scores, the initial training set comprising a plurality of initial samples, each initial sample comprising all indicators of known project requirements, the labels of the initial samples being the known workload scores of the known project requirements. The workload scoring model to be trained is initially trained by utilizing the initial training set, so that the initial evaluation performance of the workload scoring model to be trained is more excellent, and the individual fitness of the obtained genetic algorithm is more accurate. The stopping condition of the initial training may be that the number of iterations reaches a first threshold or the loss function value is smaller than a second threshold, and of course, the requirement of the initial training of the model is lower than the requirement of the formal training, and the first threshold and the second threshold need to be set reasonably and cannot be too large or too small.
In another embodiment, the option scores for each known indicator in the combination of known indicators are determined based on a preset scoring rule; and averaging the option scores of the known indexes in the known index combination to obtain an average option score. The average option score is taken as the individual fitness of the known index combination. Wherein the number of known indicators included in each known indicator combination is not necessarily the same.
According to the embodiment, the known index combination with the highest fitness can be accurately determined according to the genetic algorithm, and then indexes related to workload are optimized, so that key indexes are accurately determined.
In one embodiment, the evaluation dimensions include a complexity dimension, a risk level dimension, and a testability dimension;
if the back-end item requirement is met, the first index included in the complexity dimension includes: at least one of business scene complexity, technical complexity, code workload, number of applications involved in a group, number of interfaces involved outside a group, and number of parties involved outside a group;
wherein the code workload includes: code workload related to the number of pages, code workload related to the number of interface reconstructions, code workload related to the number of updated interfaces, code workload related to the number of database tables;
The second index included in the risk level dimension includes: a dependency risk;
the third index included in the testability dimension includes: developing at least one of the number of combined test scenes, the number of system test scenes and the number of regression test scenes;
if the first index is the front-end project requirement, the first index included in the complexity dimension includes: at least one of technical complexity, code workload, number of new component developments involved, number of existing component maintenance involved, number of applications involved in a group, number of interfaces involved outside a group, number of parties involved outside a group;
wherein the code workload includes: code workload related to number of dynamic effects, code workload related to number of buried points, code workload related to number of pages, code workload related to number of interfaces;
the second index included in the risk level dimension includes: a dependency risk;
the third index included in the testability dimension includes: developing at least one of the number of combined test scenes, the number of system test scenes and the number of regression test scenes.
Specifically, if the requirements are back-end projects, the technical complexity is code design complexity, and is determined according to algorithm design complexity related to the codes. For example, classification of technical complexity includes no reference to an algorithm, low complexity of the algorithm involved, medium complexity of the algorithm involved, high complexity of the algorithm involved, etc. The higher the complexity, the greater the workload corresponding to the project requirements.
The complexity of the service scene is determined according to the type of the service scene and the number of the service scenes related to the project requirements. For example, the business scenario types include general business scenarios, related funds processing scenarios, related complex data processing (e.g., multi-library multi-table, database processing, etc.) scenarios, related administrative reporting scenarios, and the like.
The code workload includes a code workload related to the number of pages, a code workload related to the number of interface reconstructions, a code workload related to the number of interface updates, a code workload related to the number of tables.
The greater the number of pages involved, the higher the corresponding code workload.
The greater the number of interface reconstructions involved, the higher the corresponding code workload.
The greater the number of interface updates involved, the higher the corresponding code workload. The number of the updated interfaces comprises the number of newly added interfaces, the number of modified interfaces and the like.
The larger the number of tables involved, the higher the corresponding code workload. Wherein the number of tables is the number of tables involved in the database.
The number of applications involved in the group is the number of applications involved in the same industry product development group. The more applications are involved in a group, the greater the amount of work required for the corresponding project.
The number of the interfaces involved outside the group is the number of the interfaces involved outside the development group of the same-industry products. The more interfaces the group is outside, the greater the workload corresponding to the project requirements.
The number of related parties outside the group is the number of related parties outside the group for developing the same-industry products. The more the number of parties involved outside the group, the greater the workload corresponding to the project requirements.
The second index included in the risk level dimension includes: the risk of dependency. The dependent risk includes, for example, a level of approval related to the process (e.g., wall opening, etc.). The higher the level of the process requiring approval, the higher the dependency risk and the higher the workload.
The third index included in the testability dimension includes: developing at least one of the number of combined test scenes, the number of system test scenes and the number of regression test scenes;
code testing is required for each item of code to be encoded. The development joint testing scene number is the development stage joint debugging and joint testing scene number. The more the number of development co-survey scenarios, the greater the workload corresponding to project requirements.
The number of the system test scenes is the number of the test scenes after the system test is entered. The more the number of system test scenes, the greater the workload corresponding to project requirements.
The regression testing scene number is the number of the testing scenes after entering the regression test. The more the number of regression testing scenarios, the greater the workload corresponding to the project requirements.
If the front-end project requirement is met, the technical complexity is code design complexity, and the technical complexity is determined according to the number of parameters related to the code and the like.
The code work tool specifically includes: code workload related to number of dynamic effects, code workload related to number of buried points, code workload related to number of pages, code workload related to number of interfaces.
The dynamic effect number refers to the number of dynamic effect designs. The greater the number of dynamic effects, the greater the corresponding code workload.
The greater the number of pages involved, the higher the corresponding code workload.
The greater the number of interfaces involved, the higher the corresponding code workload.
The number of new component developments refers to components that are specifically designed, not existing components. The components are page elements in front-end development including, but not limited to, various buttons, selection boxes, input boxes, and the like. The greater the number of new component developments involved, the greater the workload of the corresponding project requirements.
The maintenance number related to the existing component refers to the number of components that need maintenance in the existing component. The greater the number of maintenance related to existing components, the greater the workload corresponding to project requirements.
The number of applications involved in the group is the number of applications involved in the same industry product development group. The more applications are involved in a group, the greater the amount of work required for the corresponding project.
The number of the interfaces involved outside the group is the number of the interfaces involved outside the development group of the same-industry products. The more interfaces the group is outside, the greater the workload corresponding to the project requirements.
The number of related parties outside the group is the number of related parties outside the group for developing the same-industry products. The more the number of parties involved outside the group, the greater the workload corresponding to the project requirements.
The second index included in the risk level dimension includes: the risk of dependency. The dependent risk includes, for example, a level of approval related to the process (e.g., wall opening, etc.). The higher the level of the process requiring approval, the higher the dependency risk and the higher the workload.
The third index included in the testability dimension includes: developing at least one of the number of combined test scenes, the number of system test scenes and the number of regression test scenes;
code testing is required for each item of code to be encoded. The development joint testing scene number is the development stage joint debugging and joint testing scene number. The more the number of development co-survey scenarios, the greater the workload corresponding to project requirements.
The number of the system test scenes is the number of the test scenes after the system test is entered. The more the number of system test scenes, the greater the workload corresponding to project requirements.
The regression testing scene number is the number of the testing scenes after entering the regression test. The more the number of regression testing scenarios, the greater the workload corresponding to the project requirements.
The first, second, and third indices may be partially and finally used as key indices for evaluating the project demand workload, or may be all used as key indices for evaluating the project demand workload. And the first index, the second index and the third index are only partial indexes of the project requirement, and the application does not limit the indexes for evaluating the project requirement workload.
In one particular embodiment, the option scores for each key indicator may be determined according to the following rules:
the option scores of the partial indexes can be configured according to intervals corresponding to actual values of the indexes. For example, interval 1 corresponds to option score 1, interval 2 corresponds to option score 2, interval 3 corresponds to option score 3, interval 4 corresponds to option score 4, and so on.
For example, different types of code workload correspond to one sub-rule, and the option scores of the code workload are configured according to the interval corresponding to the actual code workload.
The option scores of the other indexes are configured according to one option score corresponding to each actual value of the indexes.
According to the embodiment, the front-end project requirements and the back-end project requirements are distinguished, different indexes are set according to respective actual working properties, and the workload of the project requirements can be evaluated in a targeted and more accurate manner.
According to the embodiment, the project work condition is comprehensively known, the work load of the project requirement is estimated from each index, the work load of the project requirement can be estimated more accurately by covering the work load to specific details.
Aiming at the problems in the prior art, the workload model is estimated based on various indexes, so that the problems of large workload estimation deviation, accurate work detail estimation, risk prediction and the like can be effectively solved, and the problems of work quality and risk are effectively solved by further applying the method. Especially in the field of finance and technology, such as online business rapid development of banks, insurance and the like, the new product/new system demand is huge, developers are required to follow project demands to meet the business development demands, and accurate estimation of the workload of the project demands has great reference significance for research, development and development work.
Referring to fig. 3, the present application provides a workload assessment apparatus, the apparatus comprising:
the screening module 100 is configured to select key indexes affecting workload from all indexes included in all evaluation dimensions;
the first data obtaining module 200 is configured to obtain all key indexes contained in each evaluation dimension of the requirement of the item to be evaluated;
the evaluation module 300 is configured to obtain a workload evaluation result of the to-be-evaluated project requirement according to the key index of the to-be-evaluated project requirement.
In one embodiment, the assessment module 300 specifically includes:
the scoring module is used for determining the option scores of target indexes based on preset scoring rules, wherein the target indexes are any key indexes of the requirements of the item to be evaluated;
the first calculation module is used for obtaining dimension scores of target evaluation dimensions according to option scores of all target indexes contained in the target evaluation dimensions, wherein the target evaluation dimensions are any evaluation dimension;
and the second calculation module is used for carrying out weighted summation on the dimension scores of all the evaluation dimensions to obtain a workload score of the requirements of the item to be evaluated, and taking the workload score as a workload evaluation result.
In one embodiment, the assessment module 300 includes:
the data set construction module is used for constructing a data set according to the selected key indexes, wherein the data set comprises a plurality of samples, each sample comprises the key indexes of the known project requirements, and the labels of the samples are the known workload scores of the known project requirements;
the model training module is used for training the workload scoring model to be trained by utilizing the data set to obtain a trained workload scoring model;
the prediction module is used for inputting the key indexes of the project requirements to be evaluated into the trained workload scoring model to obtain workload scores of the project requirements to be evaluated, and the workload scores are used as workload evaluation results.
In one embodiment, the screening module 100 includes:
the second data acquisition module is used for acquiring known indexes contained in each evaluation dimension of the known project requirements;
the genetic algorithm module is used for randomly extracting any one of the known item demands, acquiring the known indexes of the extracted known item demands on each evaluation dimension, and determining a known index combination with the highest fitness according to the maximum iteration times, the crossover probability and the mutation probability of the genetic algorithm, wherein the known index combination with the highest fitness comprises part of the known indexes of the extracted known item demands;
The key index determining module is used for determining key indexes according to all known index combinations with highest fitness, wherein each extracted known item requirement corresponds to one known index combination with highest fitness.
In one embodiment, the key indicator determination module includes:
the statistics unit is used for counting the occurrence times of the same known index in all the known index combinations with the highest fitness;
and the screening unit is used for determining the key index according to the occurrence number.
In one embodiment, the genetic algorithm module specifically includes:
the combination module is used for randomly combining all the known indexes of the selected known project requirements to obtain a plurality of known index combinations, and each known index combination is used as an individual;
the initial population construction module is used for binary coding each individual, constructing an initial population according to the obtained chromosome, and taking the initial population as a current population;
the fitness calculation module is used for calculating the individual fitness of each individual in the current population;
the evolution module is used for carrying out selection operation, crossover operation and mutation operation on individuals of the current population according to the individual fitness, crossover probability and mutation probability to obtain a next generation population;
The loop judging module is used for judging whether the current iteration number reaches the maximum iteration number, if not, taking the next generation population as the current population, and executing the steps to calculate the individual fitness of each individual in the current population and the subsequent steps until the current iteration number reaches the maximum iteration number;
and the target determining module is used for determining the known index combination with the highest fitness according to the individual corresponding to the largest individual fitness.
In one embodiment, the evaluation dimensions include a complexity dimension, a risk level dimension, and a testability dimension;
if the back-end item requirement is met, the first index included in the complexity dimension includes: at least one of business scene complexity, technical complexity, code workload, number of applications involved in a group, number of interfaces involved outside a group, and number of parties involved outside a group;
wherein the code workload includes: the number of pages involved, the number of interface reconstructions involved, the number of updated interfaces involved, the number of database tables involved;
the second index included in the risk level dimension includes: a dependency risk;
the third index included in the testability dimension includes: developing at least one of the number of combined test scenes, the number of system test scenes and the number of regression test scenes;
If the first index is the front-end project requirement, the first index included in the complexity dimension includes: at least one of technical complexity, code workload, number of new component developments involved, number of existing component maintenance involved, number of applications involved in a group, number of interfaces involved outside a group, number of parties involved outside a group;
wherein the code workload includes: the number of dynamic effects, the number of burial points, the number of pages and the number of interfaces are related;
the second index included in the risk level dimension includes: a dependency risk;
the third index included in the testability dimension includes: developing at least one of the number of combined test scenes, the number of system test scenes and the number of regression test scenes.
FIG. 4 illustrates an internal block diagram of a computer device in one embodiment. The computer device may specifically be a terminal or a server. As shown in fig. 4, the computer device includes a processor, a memory, and a network interface connected by a system bus. The memory includes a nonvolatile storage medium and an internal memory. The non-volatile storage medium of the computer device stores an operating system, and may also store a computer program which, when executed by a processor, causes the processor to implement the steps of the method embodiments described above. The internal memory may also have stored therein a computer program which, when executed by a processor, causes the processor to perform the steps of the method embodiments described above. It will be appreciated by persons skilled in the art that the architecture shown in fig. 4 is merely a block diagram of some of the architecture relevant to the present inventive arrangements and is not limiting as to the computer device to which the present inventive arrangements are applicable, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
In one embodiment, a computer device is provided comprising a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the steps of:
selecting key indexes affecting the workload from all indexes contained in all evaluation dimensions;
acquiring all key indexes contained in each evaluation dimension of the project requirement to be evaluated;
and obtaining a workload evaluation result of the project requirement to be evaluated according to the key index of the project requirement to be evaluated.
In one embodiment, a computer readable storage medium is provided, storing a computer program which, when executed by a processor, causes the processor to perform the steps of:
selecting key indexes affecting the workload from all indexes contained in all evaluation dimensions;
acquiring all key indexes contained in each evaluation dimension of the project requirement to be evaluated;
and obtaining a workload evaluation result of the project requirement to be evaluated according to the key index of the project requirement to be evaluated.
Those skilled in the art will appreciate that the processes implementing all or part of the methods of the above embodiments may be implemented by a computer program for instructing relevant hardware, and the program may be stored in a non-volatile computer readable storage medium, and the program may include the processes of the embodiments of the methods as above when executed. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The foregoing examples illustrate only a few embodiments of the application, which are described in detail and are not to be construed as limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of protection of the present application is to be determined by the appended claims.
Claims (10)
1. A method of workload assessment, the method comprising:
selecting key indexes affecting the workload from all indexes contained in all evaluation dimensions;
acquiring all key indexes contained in each evaluation dimension of the project requirement to be evaluated;
and obtaining a workload evaluation result of the project requirement to be evaluated according to the key index of the project requirement to be evaluated.
2. The method according to claim 1, wherein the obtaining the workload assessment result of the project to be assessed demand according to the key index of the project to be assessed demand comprises:
determining an option score of a target index based on a preset scoring rule, wherein the target index is any one key index of the to-be-evaluated item requirement;
obtaining dimension scores of target evaluation dimensions according to option scores of all target indexes contained in the target evaluation dimensions, wherein the target evaluation dimensions are any evaluation dimension;
and carrying out weighted summation on the dimension scores of all the evaluation dimensions to obtain the workload score of the to-be-evaluated project requirement, and taking the workload score as a workload evaluation result.
3. The method according to claim 1, wherein the obtaining the workload assessment result of the project to be assessed demand according to the key index of the project to be assessed demand comprises:
constructing a data set according to the selected key indexes, wherein the data set comprises a plurality of samples, each sample comprises the key indexes of the known project requirements, and the labels of the samples are the known workload scores of the known project requirements;
Training the workload scoring model to be trained by utilizing the data set to obtain a trained workload scoring model;
inputting the key indexes of the project requirements to be evaluated into a trained workload scoring model to obtain workload scores of the project requirements to be evaluated, and taking the workload scores as workload evaluation results.
4. A method according to claim 3, wherein the selecting key indicators affecting the workload from all indicators included in all evaluation dimensions comprises:
acquiring known indexes contained in the known project requirements in each evaluation dimension;
randomly extracting any one known item requirement, acquiring known indexes of the extracted known item requirement on each evaluation dimension, and determining a known index combination with highest fitness according to the maximum iteration times, the crossover probability and the variation probability of a genetic algorithm, wherein the known index combination with highest fitness comprises part of the known indexes of the extracted known item requirement;
and determining key indexes according to all known index combinations with highest fitness, wherein each extracted known item requirement corresponds to one known index combination with highest fitness.
5. The method of claim 4, wherein determining the key indicator based on all known indicator combinations having highest fitness comprises:
counting the occurrence times of the same known index in all the known index combinations with the highest fitness;
and determining a key index according to the occurrence times.
6. The method of claim 4, wherein determining the combination of known indicators having the highest fitness based on the maximum number of iterations, crossover probability, and mutation probability of the genetic algorithm comprises:
randomly combining all known indexes of the selected known project requirements to obtain a plurality of known index combinations, and taking each known index combination as an individual;
binary coding is carried out on each individual, an initial population is constructed according to the obtained chromosome, and the initial population is used as a current population;
calculating individual fitness of each individual in the current population;
selecting, intersecting and mutating the individuals of the current population according to the individual fitness, intersecting probability and mutating probability to obtain a next generation population;
judging whether the current iteration number reaches the maximum iteration number, if not, taking the next generation population as the current population, and executing the steps to calculate the individual fitness of each individual in the current population and the subsequent steps until the current iteration number reaches the maximum iteration number;
And determining the known index combination with the highest fitness according to the individual corresponding to the maximum individual fitness.
7. The method of claim 1, wherein the evaluation dimensions include a complexity dimension, a risk level dimension, and a testability dimension;
if the complexity dimension is the back-end item requirement, the first index included in the complexity dimension includes: at least one of business scene complexity, technical complexity, code workload, number of applications involved in a group, number of interfaces involved outside a group, and number of parties involved outside a group;
wherein the code workload comprises: the number of pages involved, the number of interface reconstructions involved, the number of updated interfaces involved, the number of database tables involved;
the second index included in the risk degree dimension includes: a dependency risk;
the third index included in the testability dimension includes: developing at least one of the number of combined test scenes, the number of system test scenes and the number of regression test scenes;
if the complexity dimension is the front-end project requirement, the first index included in the complexity dimension includes: at least one of technical complexity, code workload, number of new component developments involved, number of existing component maintenance involved, number of applications involved in a group, number of interfaces involved outside a group, number of parties involved outside a group;
Wherein the code workload comprises: the number of dynamic effects, the number of burial points, the number of pages and the number of interfaces are related;
the second index included in the risk degree dimension includes: a dependency risk;
the third index included in the testability dimension includes: developing at least one of the number of combined test scenes, the number of system test scenes and the number of regression test scenes.
8. A workload assessment device, the device comprising:
the screening module is used for selecting key indexes affecting the workload from all indexes contained in all evaluation dimensions;
the first data acquisition module is used for acquiring all key indexes contained in each evaluation dimension of the project requirement to be evaluated;
and the evaluation module is used for obtaining a workload evaluation result of the project requirement to be evaluated according to the key index of the project requirement to be evaluated.
9. A computer readable storage medium storing a computer program, which when executed by a processor causes the processor to perform the steps of the method according to any one of claims 1 to 7.
10. A computer device comprising a memory and a processor, wherein the memory stores a computer program which, when executed by the processor, causes the processor to perform the steps of the method of any of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310994689.3A CN117035515A (en) | 2023-08-08 | 2023-08-08 | Workload evaluation method, workload evaluation device, computer equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310994689.3A CN117035515A (en) | 2023-08-08 | 2023-08-08 | Workload evaluation method, workload evaluation device, computer equipment and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117035515A true CN117035515A (en) | 2023-11-10 |
Family
ID=88644184
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310994689.3A Pending CN117035515A (en) | 2023-08-08 | 2023-08-08 | Workload evaluation method, workload evaluation device, computer equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117035515A (en) |
-
2023
- 2023-08-08 CN CN202310994689.3A patent/CN117035515A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107025596B (en) | Risk assessment method and system | |
| US11250368B1 (en) | Business prediction method and apparatus | |
| CN111625516A (en) | Method and device for detecting data state, computer equipment and storage medium | |
| CN111639798A (en) | Intelligent prediction model selection method and device | |
| CN115357764A (en) | Abnormal data detection method and device | |
| US10803403B2 (en) | Method for adaptive tuning via automated simulation and optimization | |
| CN112990989B (en) | Value prediction model input data generation method, device, equipment and medium | |
| CN111340102B (en) | Method and apparatus for evaluating model interpretation tools | |
| CN116911994A (en) | External trade risk early warning system | |
| Jiarpakdee et al. | Autospearman: Automatically mitigating correlated metrics for interpreting defect models | |
| CN117035515A (en) | Workload evaluation method, workload evaluation device, computer equipment and storage medium | |
| CN113468477B (en) | Sensitive data investigation analysis method, storage medium and equipment | |
| CN114861989A (en) | Method and device for predicting insurance added service | |
| CN115130887A (en) | Reservoir dam environmental impact evaluation method and device, electronic equipment and storage medium | |
| CN111026661B (en) | Comprehensive testing method and system for software usability | |
| Bourdache et al. | Active preference elicitation by bayesian updating on optimality polyhedra | |
| Han et al. | Empirical investigation of code and process metrics for defect prediction | |
| CN111340356A (en) | Method and apparatus for evaluating model interpretation tools | |
| Lattanzi et al. | A change-point approach for the identification of financial extreme regimes | |
| Kraus et al. | Credit scoring optimization using the area under the curve | |
| US20230134042A1 (en) | System and Method for Modular Building of Statistical Models | |
| Medina et al. | Modeling credit risk: An application of the rough set methodology | |
| Asthana et al. | ML Model Change Detection and Versioning Service | |
| CN118071174A (en) | Evaluation method, system, equipment and medium based on joint drive modeling | |
| CN115795470A (en) | Method and device for identifying security level of optimization model hyper-parameter |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |