CN114493001A - Demand scheduling method and device, electronic equipment and storage medium - Google Patents
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Abstract
The invention provides a demand scheduling method, a demand scheduling device, electronic equipment and a storage medium, wherein the method comprises the following steps: determining a demand to be scheduled; and scheduling the to-be-scheduled demands based on a Functional Point Analysis (FPA) method. According to the invention, the scheduling of the demand to be scheduled is carried out based on the FPA method, so that not only can scientific basis be provided for the scheduling of the full life cycle of the demand to be scheduled, but also the time consumed by production to be put into operation can be effectively shortened, and the efficient and intelligent reasonable scheduling of the demand to be scheduled is realized.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a demand scheduling method and device, electronic equipment and a storage medium.
Background
In response to the increasing demand of customers for software product services, it is important to implement flexible and fast iterations of software systems. However, the small-scale demand cycle of the software system is unstable and the number of the demand cycle is large, so that great challenges are brought to demand development scheduling work.
For small-sized demands with various requirements, the phenomena of inconsistent development period, complex dependence relationship, inconsistent online versions and the like exist, while the traditional scheduling mode mainly depends on experience, resource allocation cannot be effectively carried out, and the situation of poor work coordination is easy to occur, for example, the phenomenon that the small-sized demand developed first is on line for a long time due to the fact that the small-sized demand waits for the development of another matching demand is caused, so that the time from the demand to the production and the research and development cost are increased.
Therefore, how to perform efficient and intelligent rationalization scheduling for small-sized demands with large quantity and different requirements is a problem to be solved urgently in the industry.
Disclosure of Invention
The invention provides a demand scheduling method, a demand scheduling device, electronic equipment and a storage medium, which are used for solving the problem that a demand scheduling mode in the prior art mainly depends on experience and effectively realizing efficient and intelligent reasonable scheduling on small demands with large quantity and different requirements.
In a first aspect, the present invention provides a demand scheduling method, including:
determining a demand to be scheduled;
and scheduling the to-be-scheduled demands based on a Functional Point Analysis (FPA) method.
Optionally, according to the demand scheduling method provided by the present invention, the scheduling the demand to be scheduled based on the FPA analysis method includes:
determining the limited production time of the demand to be scheduled;
determining the demand priority of the demand to be scheduled based on the deadline commissioning time;
scheduling the demand to be scheduled by using a target prediction strategy corresponding to the demand priority;
wherein the target prediction strategy comprises: a forward prediction strategy and a backward prediction strategy.
Optionally, according to the demand scheduling method provided by the present invention, scheduling the demand to be scheduled by using a target prediction policy corresponding to the demand priority includes:
when the demand priority is a first priority, scheduling the demand to be scheduled based on the forward prediction strategy;
when the demand priority is a second priority, scheduling the demand to be scheduled based on the reverse prediction strategy;
the limited production time of the demand to be scheduled corresponding to the first priority is later than the limited production time of the demand to be scheduled corresponding to the second priority.
Optionally, according to the demand scheduling method provided by the present invention, scheduling the demand to be scheduled based on the forward prediction strategy includes:
determining a development estimation function point of the demand to be scheduled based on the FPA method;
determining an estimated workload of a development phase based on the development estimated function point and a reference productivity, wherein the reference productivity is obtained based on an actual productivity of a historical scheduling demand;
determining estimated workloads corresponding to different stages respectively based on the estimated workloads of the development stages and the workload weights corresponding to the different stages of the demand to be scheduled respectively, wherein the different stages comprise the development stages, the test stages and the acceptance stages;
and determining the starting time of the development stage, and determining the planned production time based on the sum of the starting time of the development stage and the estimated workload corresponding to the different stages respectively.
Alternatively, according to a demand scheduling method provided by the present invention, the determining an estimated workload at a development stage based on the development estimation function point and a reference productivity includes:
determining an estimated workload of the development phase based on the development estimated function point, the reference productivity, and an adjustment factor;
wherein the adjustment factor comprises at least one of:
an application type adjustment factor, a quality characteristic adjustment factor, a development language adjustment factor, and a development team background adjustment factor.
Alternatively, according to a demand scheduling method provided by the present invention, the determining the estimated workload of the development phase based on the development estimation function point, the reference productivity, and the adjustment factor includes:
determining an estimated workload of the development phase based on a development estimated workload calculation formula; wherein the development estimation workload calculation formula is as follows:
the development estimation workload is the development estimation function point × the reference productivity × the application type adjustment factor × the quality characteristic adjustment factor × the development language adjustment factor × the development team background adjustment factor ÷ the preset value.
Optionally, according to a demand scheduling method provided by the present invention, scheduling the demand to be scheduled based on the backward prediction policy includes:
scheduling the demand to be scheduled based on the forward prediction strategy to obtain scheduling arrangement;
performing risk prediction analysis on the scheduling arrangement, and determining a risk identifier corresponding to the scheduling arrangement;
adjusting the scheduling based on the risk identification.
In a second aspect, the present invention further provides a demand scheduling apparatus, including:
the determining module is used for determining the demand to be scheduled;
and the scheduling module is used for scheduling the to-be-scheduled demands based on the functional point analysis FPA method.
In a third aspect, the present invention further provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the demand scheduling method according to the first aspect.
In a fourth aspect, the present invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the demand scheduling method according to the first aspect.
In a fifth aspect, the present invention also provides a computer program product comprising a computer program which, when executed by a processor, performs the steps of the demand scheduling method according to the first aspect.
According to the demand scheduling method, the demand scheduling device, the electronic equipment and the storage medium, the scheduling is carried out on the demand to be scheduled based on the FPA method, so that not only can a scientific basis be provided for the scheduling of the full life cycle of the demand to be scheduled, but also the time spent on production can be effectively shortened, and the efficient and intelligent reasonable scheduling of the demand to be scheduled can be realized.
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In order to more clearly illustrate the technical solutions of the present invention or the prior art, the drawings needed for the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
FIG. 1 is a schematic flow chart of a demand scheduling method according to the present invention;
FIG. 2 is a schematic illustration of demand prioritization provided by the present invention;
FIG. 3 is a schematic flow chart of the forward prediction strategy provided by the present invention;
FIG. 4 is a schematic flow chart of a reverse prediction strategy provided by the present invention;
FIG. 5 is a second flowchart of the demand scheduling method according to the present invention;
FIG. 6 is a flow diagram of large version management provided by the present invention;
FIG. 7 is a flow diagram of minor version management provided by the present invention;
FIG. 8 is a flow diagram of a generic version management provided by the present invention;
FIG. 9 is a schematic diagram of a demand scheduling apparatus according to the present invention;
fig. 10 is a schematic physical structure diagram of an electronic device provided in the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
To facilitate a clearer understanding of embodiments of the present invention, some relevant background information is first presented below.
The Functional Point Analysis (FPA) is a method for estimating the size of a software development project. The method is based on the logic design of the system, and is based on the principle that the scale of software is measured by quantifying the system function from the user perspective, namely the service value.
The FPA method is a decomposition-type scale measurement method, namely, a complex system is decomposed into smaller subsystems to be evaluated, and a software system is decomposed according to components, so that the number of functional points of the system is determined. The FPA method is a measure based on the functional requirements of a software document, the result of which is to represent the scale of the software in terms of functional points.
The small-scale demand cycle of some software systems is unstable and numerous, and great challenges are brought to demand development scheduling work. For small demands with various requirements, the phenomena of inconsistent development period, complex dependency relationship, inconsistent online versions and the like exist, for some small demands with lower processing complexity, the traditional scheduling mode mainly depends on experience, resource allocation cannot be effectively carried out, and the situation of poor work coordination is easy to occur, for example, the phenomenon that the small demand developed at first is on line for a long time due to the fact that the small demand waits for the development of another matching demand, and the time from demand to production and the research and development cost are increased. In addition, the existing scheduling mode mainly depends on experience and lacks objective basis.
In order to overcome the above-mentioned drawbacks, the present invention provides a demand scheduling method, apparatus, electronic device and storage medium. The following provides an exemplary description of a demand scheduling method, apparatus, electronic device, and storage medium provided by the present invention.
The demand scheduling method and apparatus provided by the present invention will be described with reference to fig. 1 to 9.
Fig. 1 is a schematic flow chart of a demand scheduling method provided by the present invention, and as shown in fig. 1, the method includes the following steps:
and step 110, scheduling the demand to be scheduled based on the FPA method of function point analysis.
In order to overcome the defects that the conventional demand scheduling mode mainly depends on experience, resource allocation cannot be effectively carried out, and the condition of poor work coordination is easy to occur, so that the time cost from demand to production is increased.
Optionally, the demand scheduling method provided by the invention can be suitable for scheduling demands of a bank software system and similar scheduling demands of a software system with a large number and unstable periods.
Specifically, the demand to be scheduled may be determined first, and then the demand to be scheduled may be scheduled based on the FPA method for function point analysis.
Alternatively, the development function point of the to-be-scheduled demand may be calculated based on the FPA method.
Alternatively, the amount of work required for the pending issue may be determined based on the calculated development function point.
Alternatively, the planned commissioning time may be determined based on the determined workload of the pending demand and based on the development start time.
According to the demand scheduling method provided by the invention, the scheduling of the demand to be scheduled is carried out based on the FPA method, so that not only can a scientific basis be provided for the scheduling of the full life cycle of the demand to be scheduled, but also the time consumed by production can be effectively shortened, and the efficient and intelligent reasonable scheduling of the demand to be scheduled is realized.
Optionally, the FPA analyzing method based on function point performs scheduling on the demand to be scheduled, including:
determining the limited production time of the demand to be scheduled;
determining the demand priority of the demand to be scheduled based on the deadline commissioning time;
scheduling the demand to be scheduled by using a target prediction strategy corresponding to the demand priority;
wherein the target prediction strategy comprises: a forward prediction strategy and a backward prediction strategy.
Specifically, the time limit on-production time of the demand to be scheduled can be determined firstly, and the demand priority of the demand to be scheduled is determined based on the time limit on-production time of the demand to be scheduled; and then scheduling the demand to be scheduled by using a target prediction strategy corresponding to the priority of the demand.
Optionally, the demand priority may be determined based on the deadline time, wherein a partitioning policy of the demand priority may be preset.
For example, fig. 2 is a schematic diagram of the requirement prioritization provided by the present invention, as shown in fig. 2, the requirement prioritization can be divided into four levels, i.e., a first level, a second level, a third level and a fourth level, and the requirements of each priority are illustrated in fig. 2; wherein, the first level corresponds to production within a week, the second level corresponds to production within a month, the third level corresponds to production within two months, and the fourth level corresponds to no clear production time requirement.
For example, the to-be-scheduled demands corresponding to the third and fourth levels with more production time limitation may be used as the scheduling demands of the first priority.
For example, the to-be-scheduled demands corresponding to the first level and the second level with relatively urgent production time can be used as the scheduling demands of the second priority.
Optionally, the demand to be scheduled may be scheduled by using a target prediction policy corresponding to the priority of the demand.
The target prediction strategy may include a forward prediction strategy and a backward prediction strategy.
Optionally, the forward prediction strategy may include determining, based on the function point of the demand to be scheduled, the workload of each stage corresponding to the demand to be scheduled, and then determining the scheduling.
Optionally, the backward prediction strategy may include obtaining scheduling of the to-be-scheduled demand based on experience, or obtaining scheduling of the to-be-scheduled demand based on the forward prediction strategy, and performing backward evaluation on the obtained scheduling to further perform rationalization adjustment on the scheduling.
For example, where the pending demand corresponds to a first priority, the pending demand may be scheduled based on a forward prediction strategy.
For example, where the pending demand corresponds to a second priority, the pending demand may be scheduled based on a reverse prediction strategy.
According to the invention, based on the FPA method, the scheduling is carried out on the to-be-scheduled demands with different priorities based on the software function points and the forward prediction strategy and the backward prediction strategy, so that the rationality of scheduling can be further improved, and an intelligent scheduling optimization strategy is realized.
Optionally, the scheduling the demand to be scheduled by using a target prediction policy corresponding to the demand priority includes:
when the demand priority is a first priority, scheduling the demand to be scheduled based on the forward prediction strategy;
when the demand priority is a second priority, scheduling the demand to be scheduled based on the reverse prediction strategy;
the limited production time of the demand to be scheduled corresponding to the first priority is later than the limited production time of the demand to be scheduled corresponding to the second priority.
Optionally, in a case that the priority of the demand is the first priority, the demand to be scheduled may be scheduled based on a forward prediction policy.
Optionally, in the case that the priority of the demand is the second priority, the demand to be scheduled may be scheduled based on a reverse prediction strategy.
Optionally, the limited-term production time of the to-be-scheduled demand corresponding to the first priority may be later than the limited-term production time of the to-be-scheduled demand corresponding to the second priority.
For example, if the current time is 08-month-1 day, the limited on-time of the first target demand to be scheduled is 11-month-1 day, and the limited on-time of the second target demand to be scheduled is 09-month-1 day, the first target demand to be scheduled may be scheduled based on the forward prediction strategy, and the second target demand to be scheduled may be scheduled based on the backward prediction strategy.
According to the invention, based on the FPA method, the scheduling is carried out on the to-be-scheduled demands with different priorities based on the software function points and the forward prediction strategy and the backward prediction strategy, so that the rationality of scheduling can be further improved, and an intelligent scheduling optimization strategy is realized.
Optionally, the scheduling the demand to be scheduled based on the forward prediction strategy includes:
determining a development estimation function point of the demand to be scheduled based on the FPA method;
determining an estimated workload of a development phase based on the development estimated function point and a reference productivity, wherein the reference productivity is obtained based on an actual productivity of a historical scheduling demand;
determining estimated workloads corresponding to different stages respectively based on the estimated workloads of the development stages and the workload weights corresponding to the different stages of the demand to be scheduled respectively, wherein the different stages comprise the development stages, the test stages and the acceptance stages;
and determining the starting time of the development stage, and determining the planned production time based on the sum of the starting time of the development stage and the estimated workload corresponding to the different stages respectively.
Specifically, scheduling the to-be-scheduled demand based on the forward prediction strategy may include the following steps (1) to (4):
(1) determining a development estimation function point of a demand to be scheduled based on an FPA method;
(2) determining an estimated workload of the development phase based on the determined development estimated function point and a reference productivity, wherein the reference productivity may be obtained based on an actual productivity of the historical scheduling demand;
(3) determining estimated workloads corresponding to different stages respectively based on the estimated workload of the development stage and the workload weights corresponding to different stages of the to-be-scheduled demand respectively; wherein, the different stages can comprise a development stage, a test stage and an acceptance stage;
(4) and determining the starting time of the development stage based on the actual situation, and determining the planned production time based on the sum of the starting time of the development stage and the estimated workload corresponding to different stages respectively.
Optionally, before determining the estimation workload of the development phase based on the development estimation function point and the reference productivity, the method may further include:
determining a development estimation function point of the historical scheduling requirement based on the FPA method and the historical scheduling requirement;
the benchmark production rate is determined based on the historical scheduling demand development estimation function points and the historical scheduling demand actual production rate.
Alternatively, it is possible to re-evaluate the required function points from the existing history data based on the FPA method, and calculate the reference productivity (unit: man-hour/function point) in conjunction with the actual productivity, which is currently often used at 7.35 man-hours/function point, i.e., 7.35 hours are required for 1 person to complete 1 function point.
Optionally, a prediction model can be established by combining various types of influence factors, objective characteristics, external requirements, team capacity and other factors are respectively combined into various factors to construct a demand workload estimation model, and the factors for determining workload are shown in table 1; and then analyzing the characteristics of various numerical values and setting weights, directly calculating the workload by using the model, and finally decomposing the work task and formulating a work schedule, namely a demand schedule, according to the workload estimation result and the actual resource condition.
TABLE 1 determination of the constituent factors of the workload
For example, fig. 3 is a schematic flow chart of the forward prediction strategy provided by the present invention, and as shown in fig. 3, a development estimation function point of a demand to be scheduled is determined based on the FPA method; then, determining the estimated workload of the development stage based on the development estimation function points, the reference productivity and various adjustment factors; further determining the workload of the testing stage and the acceptance stage based on the estimated workload of the development stage and the workload of each stage; and finally, decomposing the work tasks and formulating a work schedule, namely a demand scheduling, according to the total workload estimation result and the actual resource condition.
Optionally, the determining an estimation workload of a development phase based on the development estimation function point and a reference productivity includes:
determining an estimated workload of the development phase based on the development estimated function point, the reference productivity, and an adjustment factor;
wherein the adjustment factor comprises at least one of:
and applying a type adjustment factor, a quality characteristic adjustment factor, a development language adjustment factor and a development team background adjustment factor, which is specifically referred to in table 2.
TABLE 2 adjustment factor Classification and value description
Alternatively, the estimation workload of the development stage may be determined based on the development estimation function point, the reference productivity, and the adjustment factor.
Optionally, the adjustment factor may comprise at least one of:
an application type adjustment factor, a quality characteristic adjustment factor, a development language adjustment factor, and a development team background adjustment factor.
Specifically, the development estimation function point may be calculated based on the FPA method, and then the estimation workload at the development stage may be determined based on the development estimation function point, the reference productivity and application type adjustment factor, the quality characteristic adjustment factor, the development language adjustment factor, and the development team background adjustment factor.
The invention determines the estimated workload in the development stage by developing and estimating the function points, the reference productivity and various adjustment factors, can improve the accuracy of workload estimation and further rationalize the schedule.
Optionally, the determining the estimated workload of the development phase based on the development estimation function point, the reference productivity and the adjustment factor comprises:
determining an estimated workload of the development phase based on a development estimated workload calculation formula; wherein the development estimation workload calculation formula is as follows:
the development estimation workload is the development estimation function point × the reference productivity × the application type adjustment factor × the quality characteristic adjustment factor × the development language adjustment factor × the development team background adjustment factor ÷ the preset value.
Alternatively, the estimated workload of the development stage may be determined based on a development estimated workload calculation formula.
Wherein, the development estimation workload calculation formula can be expressed as:
the development estimation workload is the development estimation function point × the reference productivity × the application type adjustment factor × the quality characteristic adjustment factor × the development language adjustment factor × the development team background adjustment factor ÷ the preset value.
Wherein, the preset value can be 8, which means that the working time per day is 8 hours.
The invention determines the estimated workload in the development stage by developing and estimating the function points, the reference productivity and various adjustment factors, can improve the accuracy of workload estimation and further rationalize the schedule.
Optionally, the scheduling the demand to be scheduled based on the backward prediction strategy includes:
scheduling the demand to be scheduled based on the forward prediction strategy to obtain scheduling arrangement;
performing risk prediction analysis on the scheduling arrangement, and determining a risk identifier corresponding to the scheduling arrangement;
adjusting the scheduling based on the risk identification.
Optionally, the scheduling needs may be scheduled based on a forward prediction strategy, so as to obtain a scheduling arrangement.
Specifically, a development estimation function point of the demand to be scheduled may be determined based on the FPA method; then determining the estimated workload of the development stage based on the development estimation function points, the reference productivity and various adjustment factors; further determining the workload of the testing stage and the acceptance stage based on the estimated workload of the development stage and the workload of each stage; and finally, determining the total workload based on the workloads in different stages, decomposing the work tasks and making a work schedule according to the total workload estimation result and the actual resource condition, and acquiring scheduling.
Optionally, risk prediction analysis may be performed on the obtained scheduling, and a risk identifier corresponding to the obtained scheduling is determined.
Alternatively, risk predictive analysis may be performed on scheduling by delphi's method, i.e., predictive analysis may be performed by consistency of panel opinions, identifying a risk and determining a risk identifier for that risk, as shown in table 3 for a risk analysis table; and reversely evaluating whether the current scheduling can be realized or not, and properly scheduling the scheduling after balancing the risks at all levels.
TABLE 3 Risk analysis Table
Optionally, scheduling may be adjusted based on the determined risk identification, for example, to increase the number of people to increase work rate or to tailor demand to reduce workload.
Fig. 4 is a schematic flow chart of the backward prediction strategy provided by the present invention, and as shown in fig. 4, a scheduling arrangement is obtained based on the forward prediction strategy, then the risk of the scheduling arrangement is reversely evaluated, and then the scheduling arrangement is adjusted based on the empirical value and the risk of the scheduling arrangement to determine the final possible demand scheduling.
According to the invention, the scheduling of the demand to be scheduled is carried out based on the FPA method, so that not only can scientific basis be provided for the scheduling of the full life cycle of the demand to be scheduled, but also the time consumed by production to be put into operation can be effectively shortened, and the efficient and intelligent reasonable scheduling of the demand to be scheduled is realized.
FIG. 5 is a second schematic flow chart of the demand scheduling method provided by the present invention, and the demand scheduling flow is illustrated by taking FIG. 5 as an example; as shown in fig. 5, the method may include the steps of:
step 510, judging the demand priority of the demand to be scheduled, and entering a corresponding processing flow according to the demand priority;
step 520, for the third-level requirement with loose limited period and the fourth-level requirement without definite time requirement, because the period is long, sufficient conditions are provided for forward prediction to release the requirement scheduling;
step 521, based on the FPA method, estimating a development function point of the demand to be scheduled, referring to the historical benchmark productivity of the system (the benchmark productivity refers to the median of the benchmark productivity and is 7.35 man-hours/function points), and referring to the calculation formula in the workload estimation model in table 4, calculating the development estimation workload (unit: man-days, the development estimation workload refers to the median of the development estimation workload) and the most likely total implementation workload (unit: man-months), and further calculating the most likely total implementation cost (unit: yuan);
TABLE 4 workload estimation model
Step 522, according to the industry standard data, combining the historical accumulated workload of each working stage of demand, development, test, acceptance and production, determining the workload weight distribution of each stage through analysis, as shown in table 5. Based on the estimated development workload (unit: man day) obtained in step 521, the estimated total workload of the small-sized demand is calculated by combining the work proportion of each stage, namely the estimated development workload/the workload proportion of the development stage, so as to obtain the estimated workload of each stage of demand, development, test and acceptance before production, and the specific calculation method is shown in table 5;
TABLE 5 formula for distributing and calculating workload weight of each link
In step 523, since the workload of the associated system is generally not more than half of the workload of the main system (the main system refers to a system to which the small-sized demand is directly applied, and the associated system refers to a system related to the modification of the small-sized demand, for example, when the application software a adds a new financial purchasing function, the main system is a financial platform, and the application software a is used as a financial purchasing channel, needs to be modified together, and belongs to the associated system), and the related systems do not necessarily start at the same time. In order to simplify the flow and leave a portion of the maneuvering time for development scheduling, the sum of development, testing and acceptance and estimation workloads is used for scheduling without considering parallelism. Calculating the planned production date according to the development starting time and the sum of the development, test and acceptance evaluation workloads;
in step 524, the system displays the selectable production versions according to the planned production dates calculated by the schedule, and the demand personnel selects one of the selectable production versions, thereby determining the production version and the planned production date. The version window time arrangement and the version-receiving rule are shown in table 6, and the version management process is shown in fig. 6-8, where fig. 6 is a schematic flow diagram of large version management provided by the present invention, fig. 7 is a schematic flow diagram of small version management provided by the present invention, and fig. 8 is a schematic flow diagram of general version management provided by the present invention;
TABLE 6 version Window scheduling and Name rules
In step 530, for secondary demands with more stringent deadlines, the demand scheduling may follow a reverse prediction strategy. And estimating a demand development function point based on the FPA method, and combining the weight distribution backstepping workload of each stage to provide personnel at each stage for adjusting resources and carrying out reasonable scheduling distribution. In the process, the matching condition of each stage and the plan can be monitored, the unreasonable resource allocation and deficiency condition can be fed back and improved in time, and the rationality degree of the resource allocation is evaluated in a measuring way after the production period of the demand is finished;
step 531, estimating development requirement function points by using an FPA method, and distributing the workload of each stage in a backward recursion manner according to the weight of each stage;
step 532, the construction condition of the project is analyzed and discussed by the expert group, the risk existing in the project is identified, and the risk brought by the workload is reversely evaluated;
step 533, performing predictive analysis through consistency of the expert group opinions, and determining a risk identifier corresponding to the risk under the workload;
step 534, determine scheduling at risk, such as increasing personnel count or tailoring requirements;
step 535, determine a plan;
in step 540, the demand schedule can be directly evaluated in the reverse direction for the first-class demand with the very urgent deadline. Under the condition of time limit tension, each department does not have the function of orderly measuring and constructing an index system so as to gradually feed back and adjust the implementation conditions of the plan, so that the similar type of demands need to be allocated through a reverse evaluation path and historical data, and the risk values of the demands are determined as reference data. In the process, whether the starting time of each stage meets the plan or not can be monitored, and if delay exists, a solution needs to be communicated with a development department and a testing department;
step 541, determining a possible construction period according to the empirical value;
step 542, the expert group analyzes and discusses the construction condition of the project, identifies the risk in the project and reversely evaluates the risk brought by the workload;
543, performing predictive analysis through consistency of the expert group opinions, and determining a risk identifier corresponding to the risk under the workload;
step 544, determining scheduling at risk, such as increasing personnel count or tailoring requirements;
step 545, determine a plan;
step 550, after scheduling implementation, comparing the plan with the actual situation, and evaluating the plan implementation effect by combining the index content of the basic management data of the whole demand flow, wherein the main reference indexes are shown in table 7.
TABLE 7 requirement full flow basic management data index
The present invention is described below by way of specific examples, which are described herein for the purpose of illustration only and are not intended to be limiting.
For the requirements of three levels and four levels with loose time limit, the scheduling follows a forward prediction strategy, and the specific implementation steps comprise:
step 610, estimating the required development function points by the FPA method, as shown in table 8, and calculating the development estimation workload (unit: day), the most likely total implementation workload (month and month), and the most likely total implementation cost (yuan) by using the calculation formula in table 3, as shown in table 9;
table 8 case demand function point estimation results
Table 9 case development workload estimation results
Step 611, calculating the small demand estimated total workload, i.e., the ratio of the development estimated workload to the development stage workload is 5.88/(10.68+42.74), i.e., 11.007 (man day), and then referring to the calculation formulas of the respective stage workloads in table 5, calculating the estimated workload of each stage, wherein the calculation results are shown in table 10;
table 10 case results of workload estimation in each link
Step 612, summarizing the workload input from the demand to each stage of production, and calculating the planned production date according to the development starting time and the estimated workload from the development starting to the pre-production. Referring to the version scheduling model in table 11, assuming that the development start time is 11 months and 1 day, the estimated workload from the development start to the pre-commissioning is the estimated development workload + the estimated test workload + the estimated acceptance workload 9.06 (man day), and the calculation is performed on a working day basis, so that 11 months and 12 days are completed, and the summary of the required scheduling of each stage is shown in table 12. According to the flow of the small version management of fig. 7, the small version of 11-month 19-day is fished at 11-month 5-day, released at 11-month 10-day, frozen at 11-month 15-day, and the small version application needs to be added before 11-month 15-day, so the planned commissioning time is 11-month 19-day, and the required full life cycle is 17 working days. If a more loose plan is selected, the small edition is put into production in 12 months and 3 days, the edition is fished in 11 months and 19 days, and the development and test time of 10 working days is increased in the whole life cycle of 27 days. As shown in table 13, the median of the service demands of 5-10 function points is 58.27 working days, which is the median of the demand time lengths of the respective types calculated by the conventional method. Therefore, the full life cycle can be effectively shortened by utilizing the FPA method to estimate and schedule, and the iterative updating of small-size requirements is accelerated.
TABLE 11 case schedule model of XX year, 11 month version
Table 12 summary of requirements of each stage
TABLE 13 mean time duration for each type of demand calculated using conventional allocation
For secondary demands with more stringent deadlines, the demand scheduling follows a reverse prediction strategy. Assuming the case described above is a more urgent orange demand, the planned on-stream time is 11 months and 19 days. If the small version is to be put into production in 11 months and 19 days, the small version management flow shown in fig. 7 is used, the small version in 11 months and 19 days is fished in 11 months and 5 days, the small version is released in 11 months and 10 days, and the small version is frozen in 11 months and 15 days, the small version application needs to be proposed before 11 months and 15 days, and the small demand plan is finished before 11 months and 15 days. The specific implementation steps comprise:
step 620, estimating the required development function points by the FPA method, as shown in Table 8, and calculating the development estimation workload (unit: day), the most possible total implementation workload (month and month) and the most possible total implementation cost (Yuan) by using the calculation formula in Table 4, as shown in Table 9;
step 621, calculating the BR/IR estimated total workload, i.e., the ratio of the estimated workload to the phase workload in development is 5.88/(10.68+42.74), i.e., 11.007 (human days), and then calculating the estimated workload in each phase by referring to the calculation formula of the workload in each phase in table 5, where the calculation result is shown in table 10;
and 622, summarizing the workload input from the requirement to each stage of production, and calculating the planned production date according to the development starting time and the estimated workload from the development starting to the pre-production. Referring to the version scheduling model in table 11, assuming that the development start time is 11 months and 1 day, the estimated workload from the development start to the pre-commissioning is 9.06 (man days) of the estimated workload of development, the estimated workload of testing and the estimated workload of acceptance and acceptance, and the calculation is performed on working days, so that 11 months and 12 days can be completed;
step 623, analyzing and discussing the construction condition of the project by the expert group, identifying risks existing in the project, and reversely evaluating the risks brought by the workload;
at step 624, a predictive analysis is performed based on the consistency of the panel opinions to determine the risk indicators corresponding to the risks at the workload, as shown in table 14. Assuming that the risk of the expert evaluating the framework construction unsatisfied with the requirement is high and the framework construction needs to be completed in 4 days, the estimated workload from the beginning of development to the pre-production is 13.06 (human days), so that the small requirement is completed in 11 and 18 days, which is 3 days later than the planning completion time (11 and 15 days);
table 14 risk analysis table of cases
In step 625, to complete the production in 11 months and 19 days, i.e., to complete the mini-demand in 11 months and 15 days, the number of people is increased to increase the work rate or to tailor the demand to reduce the workload, ensuring that the mini-demand can be completed in 11 months and 15 days.
For the first-level requirement with a very urgent time limit, under the condition of time limit tension, each department does not have the function of orderly measuring and constructing an index system, and further gradually feeds back and adjusts the implementation conditions of the plan. Therefore, through a reverse evaluation path, similar types of demands need to be accommodated through historical data, and at the same time, risk values of the demands are determined as reference data. In this process, it is possible to monitor whether the start time of each phase meets the plan, and if there is a delay, to communicate the solution to the development and testing department. Assuming the case described above is a more urgent orange demand, the planned on-stream time is 11 months and 19 days. If the small version is to be put into production in 11 months and 19 days, the small version management flow shown in fig. 7 is used, the small version in 11 months and 19 days is fished in 11 months and 5 days, the small version is released in 11 months and 10 days, and the small version is frozen in 11 months and 15 days, the small version application needs to be proposed before 11 months and 15 days, and the small demand plan is finished before 11 months and 15 days. The method comprises the following specific steps:
step 630, determining the estimated workload from the beginning of development to pre-production according to the empirical value;
step 631, referring to step 623, step 624 and step 625, the requirement scheduling is completed, which is not described herein again.
The invention can estimate the more accurate planned production time by carrying out the small-scale demand scheduling based on the FPA method. A large number of practices prove that the coincidence degree of most of the required actual production time and the planned production time is higher. The model has remarkable effect in the aspects of optimizing resource allocation, saving manpower and material resources and accelerating the on-line of the demand. The following beneficial effects can be achieved:
(1) and the full life cycle management level is improved. The FPA method is used for small-scale demand scheduling, scientific basis can be provided for scheduling of a full life cycle of demand, and demand managers can perform demand management duties.
(2) The project cost is reduced. The FPA method is used for scheduling small-sized demands, versions are reasonably arranged, each stage has continuity, time consumed by production to be put into operation is effectively shortened, and remarkable effects are achieved in the aspects of optimizing resource allocation, saving manpower and material resources and accelerating the on-line of the demands. As shown in Table 15, comparing the small requirement scheduling data of the software function point method with the historical data, the whole life cycle of the requirements of all the scale sections is shorter than that of the historical data, wherein the effect of the requirements of 10-20 function points is larger, the requirement is shortened by 31.70%, and the requirement of 5-10 function points is shortened by 19.63%.
TABLE 15 comparison of trial data with historical data
(3) And reasonably distributing resources. According to the priority and workload estimation, the requirement starting time can be more reasonably arranged, and the development testing resources are adjusted.
The demand scheduling apparatus provided by the present invention is described below, and the demand scheduling apparatus described below and the demand scheduling method described above may be referred to in correspondence with each other.
Fig. 9 is a schematic structural diagram of a demand scheduling apparatus provided in the present invention, and as shown in fig. 9, the apparatus includes: a determination module 910 and a scheduling module 920; wherein:
the determining module 910 is configured to determine a demand to be scheduled;
the scheduling module 920 is configured to schedule the to-be-scheduled demand based on the FPA method for function point analysis.
According to the demand scheduling device provided by the invention, the scheduling of the demand to be scheduled is carried out based on the FPA method, so that not only can a scientific basis be provided for the scheduling of the full life cycle of the demand to be scheduled, but also the time consumed by production can be effectively shortened, and the efficient and intelligent reasonable scheduling of the demand to be scheduled is realized.
Fig. 10 is a schematic physical structure diagram provided in the present invention, and as shown in fig. 10, the electronic device may include: a processor (processor)1010, a communication Interface (Communications Interface)1020, a memory (memory)1030, and a communication bus 1040, wherein the processor 1010, the communication Interface 1020, and the memory 1030 communicate with each other via the communication bus 1040. Processor 1010 may invoke logic instructions in memory 1030 to perform the demand scheduling method provided by the methods described above, including:
determining a demand to be scheduled;
and scheduling the to-be-scheduled demands based on a Functional Point Analysis (FPA) method.
Furthermore, the logic instructions in the memory 1030 can be implemented in software functional units and stored in a computer readable storage medium when the logic instructions are sold or used as independent products. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In another aspect, the present invention also provides a computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, enable the computer to perform a demand scheduling method provided by the above methods, the method comprising:
determining a demand to be scheduled;
and scheduling the to-be-scheduled demands based on a Functional Point Analysis (FPA) method.
In yet another aspect, the present invention also provides a non-transitory computer readable storage medium having stored thereon a computer program that when executed by a processor is implemented to perform the method for demand scheduling provided above, the method comprising:
determining a demand to be scheduled;
and scheduling the to-be-scheduled demands based on a Functional Point Analysis (FPA) method.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (11)
1. A method of demand scheduling, comprising:
determining a demand to be scheduled;
and scheduling the to-be-scheduled demands based on a Functional Point Analysis (FPA) method.
2. The demand scheduling method of claim 1, wherein the FPA based on function point analysis method schedules the demand to be scheduled, comprising:
determining the limited production time of the demand to be scheduled;
determining the demand priority of the demand to be scheduled based on the deadline commissioning time;
scheduling the demand to be scheduled by using a target prediction strategy corresponding to the demand priority;
wherein the target prediction strategy comprises: a forward prediction strategy and a backward prediction strategy.
3. The demand scheduling method of claim 2, wherein scheduling the demand to be scheduled by using the target prediction strategy corresponding to the demand priority comprises:
when the demand priority is a first priority, scheduling the demand to be scheduled based on the forward prediction strategy;
when the demand priority is a second priority, scheduling the demand to be scheduled based on the reverse prediction strategy;
the limited production time of the demand to be scheduled corresponding to the first priority is later than the limited production time of the demand to be scheduled corresponding to the second priority.
4. The demand scheduling method of claim 3, wherein said scheduling the demand to be scheduled based on the forward prediction strategy comprises:
determining a development estimation function point of the to-be-scheduled demand based on the FPA method;
determining an estimated workload of a development phase based on the development estimated function point and a reference productivity, wherein the reference productivity is obtained based on an actual productivity of a historical scheduling demand;
determining estimated workloads corresponding to different stages respectively based on the estimated workloads of the development stages and the workload weights corresponding to the different stages of the demand to be scheduled respectively, wherein the different stages comprise the development stages, the test stages and the acceptance stages;
and determining the starting time of the development stage, and determining the planned production time based on the sum of the starting time of the development stage and the estimated workload corresponding to the different stages respectively.
5. The demand scheduling method of claim 4, wherein determining the estimated workload of the development phase based on the development estimated function points and the benchmark production rates comprises:
determining an estimated workload of the development phase based on the development estimated function point, the reference productivity, and an adjustment factor;
wherein the adjustment factor comprises at least one of:
an application type adjustment factor, a quality characteristic adjustment factor, a development language adjustment factor, and a development team background adjustment factor.
6. The demand scheduling method of claim 5, wherein the determining the estimated workload of the development phase based on the development estimated function point, the benchmark productivity, and an adjustment factor comprises:
determining an estimated workload of the development phase based on a development estimated workload calculation formula; wherein the development estimation workload calculation formula is as follows:
the development estimation workload is the development estimation function point × the reference productivity × the application type adjustment factor × the quality characteristic adjustment factor × the development language adjustment factor × the development team background adjustment factor ÷ the preset value.
7. The demand scheduling method of claim 3, wherein scheduling the demand to be scheduled based on the backward prediction strategy comprises:
scheduling the demand to be scheduled based on the forward prediction strategy to obtain scheduling arrangement;
performing risk prediction analysis on the scheduling arrangement, and determining a risk identifier corresponding to the scheduling arrangement;
adjusting the scheduling based on the risk identification.
8. A demand scheduling apparatus, comprising:
the determining module is used for determining the demand to be scheduled;
and the scheduling module is used for scheduling the to-be-scheduled demands based on the functional point analysis FPA method.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements the steps of the demand scheduling method according to any one of claims 1 to 7.
10. A non-transitory computer readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the demand scheduling method of any one of claims 1 to 7.
11. A computer program product comprising a computer program, wherein the computer program when executed by a processor implements the steps of the demand scheduling method according to any one of claims 1 to 7.
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| Title |
|---|
| 李海涛 等: "广东农信基于软件功能点方法的小型需求排期优化策略及模型", 《HTTPS://WWW.BSCEA.ORG/HTML/JSWZ/2021/0720/1226.HTML》, 20 July 2021 (2021-07-20), pages 1 - 8 * |
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