Information software project cost evaluation system and method

Abstract

The invention discloses an informatization software project cost evaluation system and method, and the system comprises a data collection module, a data processing module, a cost evaluation module and a result output module, and relates to the technical field of software project management. According to the informatization software project cost evaluation system and method, through cooperative work of the cost evaluation unit and the value evaluation unit of the cost evaluation module, a quantitative evaluation model is constructed based on historical data and market dynamics, accurate measurement and calculation of the whole life cycle cost of a software project are realized, and when the deviation analysis unit finds that the cost is abnormal, the evaluation result is accurate. And the data can be immediately fed back to the data processing module and the cost evaluation module for dynamic adjustment, so that a closed-loop management mechanism of estimation-evaluation-deviation correction is formed, the problem of disordered cost management is fundamentally solved, the cost is reduced, and meanwhile, reasonable allocation of resources and stability of software quality are ensured through matching analysis of the cost and the value.

Description

Technical Field

[0001] This invention relates to the field of software project management technology, specifically to an information-based software project cost assessment system and method. Background Technology

[0002] The reference patent title is: "A Method and System for Engineering Cost Assessment" (Authorization Announcement No.: CN109325665A, Authorization Announcement Date: 2019.02.12). This method includes the following steps: establishing a software engineering cost assessment index table with dimensions, types, and hierarchical relationships; performing automated cost assessment analysis on the development projects participating in the assessment using the software engineering cost assessment index table; and automatically generating a cost assessment doubt table for the corresponding development project based on the assessment analysis results. It can flexibly design a software engineering cost index table, fully considering the unit cost indicators of building installation engineering under different factors using multiple index dimensions, making engineering cost assessment more transparent and standardized. It provides tax bureau personnel with an intuitive table display, providing a basis for engineering cost auditing, saving tax bureau auditors' calculation time, and significantly reducing the time required for engineering cost assessment of specific real estate development projects.

[0003] Based on the aforementioned documents, with the development of the computer software industry and the continuous expansion and increasing complexity of software, many problems exist in software project management. Firstly, cost management is relatively weak; many software projects suffer from increased software costs due to poor cost management, while software quality is also difficult to guarantee. Secondly, current software cost estimation relies heavily on manual statistical calculations by a large number of personnel, which not only leads to low efficiency in cost estimation but also results in low accuracy in cost calculation. Thirdly, in information technology engineering projects, the cost of some software deviates significantly from its actual value, posing a huge challenge to project budgeting and cost control. Therefore, this invention provides an information technology software project cost assessment system and method. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides an information-based software project cost assessment system and method, which solves the problems of weak cost management, low efficiency, and cost deviation from value in existing software project management.

[0005] To achieve the above objectives, the present invention provides the following technical solution: an information-based software project cost assessment system, comprising: a data acquisition module, a data processing module, a cost assessment module, and a result output module.

[0006] The data acquisition module is used to collect various basic data related to the software project.

[0007] The data processing module cleans, classifies, and integrates the collected data to form a standard data format.

[0008] The cost assessment module performs cost estimation, value assessment, and deviation analysis based on the processed data.

[0009] The result output module outputs the cost assessment results and enhances early warning prompts.

[0010] Preferably, the data acquisition module includes:

[0011] Requirements Information Collection Unit: Accurately collects detailed requirements information on the functional requirements, performance indicators, and technical specifications of the software project;

[0012] Historical data acquisition unit: The system collects historical data on the cost amount, cost structure, and project schedule of similar software projects in the past;

[0013] Market data acquisition unit: Real-time acquisition of dynamic market data on current human resource compensation standards, hardware equipment prices, and software licensing fees.

[0014] Preferably, the data processing module includes:

[0015] Data cleaning unit: Improves data accuracy through deduplication, outlier detection, and logical error correction.

[0016] Data classification unit: Data is classified and archived according to dimensions such as demand type, cost item, and market category;

[0017] Data integration unit: Transforms heterogeneous data from multiple sources into a unified data structure and establishes a standardized data model.

[0018] Preferably, the cost assessment module includes:

[0019] Cost estimation unit: Cost calculation is performed by using analogy estimation models, parametric estimation models, and bottom-up estimation models, combined with project characteristics.

[0020] Value assessment unit: Evaluate the actual value by comprehensively considering factors such as software functional complexity, technological innovation, and market demand.

[0021] Deviation Analysis Unit: Compares the estimated cost with the assessed value, calculates the deviation magnitude, and determines the degree of deviation.

[0022] Preferably, the cost estimation unit includes a built-in analogy estimation model that can perform rapid cost comparisons based on data from similar historical projects;

[0023] The parameter estimation model built into the cost estimation unit calculates costs by quantifying parameters such as function points and lines of code.

[0024] The cost estimation unit has a built-in bottom-up estimation model that forms an overall estimate by accumulating module-level costs.

[0025] Preferably, the result output module includes:

[0026] Report generation unit: Automatically generates multi-dimensional evaluation reports that include cost details, value assessment, and deviation analysis;

[0027] Early warning unit: When the cost deviates from the value by more than a preset threshold, an early warning will be issued through a visual interface or message push.

[0028] This invention also discloses a method for cost assessment of information technology software projects, specifically including the following steps:

[0029] S1. Multi-dimensional data collection: Collect project requirements, historical cases, and market data from multiple dimensions through the data collection module;

[0030] S2. Data Governance and Standardization: Use the data processing module to clean, classify and integrate the collected multi-dimensional data to build a standard dataset;

[0031] S3. Intelligent Assessment and Deviation Analysis: The cost assessment module is used to complete cost estimation, value assessment and deviation analysis.

[0032] S4. Results Visualization and Decision Support: The results output module outputs the cost assessment results, including generating reports and issuing early warnings.

[0033] Preferably, when the deviation analysis unit detects a cost deviation, it feeds the deviation data back to the data processing module for data verification, and simultaneously transmits it to the cost assessment module to adjust the estimation model parameters, forming an assessment optimization closed loop.

[0034] Beneficial effects

[0035] This invention provides a system and method for cost assessment of information technology software projects. Compared with existing technologies, it has the following advantages:

[0036] 1. This information technology software project cost assessment system and method, through the collaborative work of the cost estimation unit and value assessment unit of the cost assessment module, constructs a quantitative assessment model based on historical data and market dynamics, realizing accurate calculation of the entire life cycle cost of the software project. When the deviation analysis unit detects cost anomalies, it can immediately feed back to the data processing module and cost assessment module for dynamic adjustment, forming a closed-loop management mechanism of "estimation-assessment-correction". This fundamentally solves the problem of disordered cost management, reduces cost, and at the same time, through cost and value matching analysis, ensures the rational allocation of resources and guarantees the stability of software quality.

[0037] 2. This information-based software project cost assessment system and method automatically acquires multi-source data through a data acquisition module. Combined with the cleaning, classification, and integration units of the data processing module, it uses algorithm models to eliminate data noise and form a standardized dataset, reducing manual data processing by more than 80%. In the cost assessment stage, the cost estimation unit's built-in analogy estimation, parameter estimation, and other models can automatically match the optimal algorithm according to project characteristics. With the help of machine learning, the estimation parameters are dynamically optimized, improving cost estimation efficiency by 3-5 times and controlling the accuracy error within ±10%, which is significantly better than the ±20%-±10% error range of existing technologies, providing a more reliable decision-making basis for project budgeting.

[0038] 3. This information technology software project cost assessment system and method, through a value assessment unit, integrates multi-dimensional indicators such as functional complexity, technological innovation, and market demand. It uses the analytic hierarchy process (AHP) and a fuzzy comprehensive evaluation model to quantify the actual value of the software. The deviation analysis unit compares the estimated cost with the value assessment results in real time. When the cost deviates from the value by more than a preset threshold, the early warning unit immediately triggers a response mechanism. At the same time, combined with a closed-loop feedback system, the data processing rules and evaluation model parameters are adjusted, which greatly improves the matching degree between the cost and the actual value, effectively solves the problem of inflated or underestimated costs, and provides a scientific value anchor for project cost control. Attached Figure Description

[0039] Figure 1 This is a flowchart of the workflow of the information software project cost assessment system of the present invention;

[0040] Figure 2 This is a schematic diagram of the result output module of the present invention;

[0041] Figure 3 This is a schematic diagram of the data acquisition module of the present invention;

[0042] Figure 4 This is a schematic diagram of the data processing module of the present invention;

[0043] Figure 5 This is a schematic diagram of the cost assessment module of the present invention.

[0044] Figure 6 This is a flowchart for comparing and analyzing competing products in the industry according to the present invention.

[0045] Figure 7 This is a flowchart comparing the accuracy of the intelligent function point analysis results of the present invention.

[0046] In the diagram: 1-Data Acquisition Module, 11-Demand Information Acquisition Unit, 12-Historical Data Acquisition Unit, 13-Market Data Acquisition Unit, 2-Data Processing Module, 21-Data Cleaning Unit, 22-Data Classification Unit, 23-Data Integration Unit, 3-Cost Assessment Module, 31-Cost Estimation Unit, 32-Value Assessment Unit, 33-Variation Analysis Unit, 4-Result Output Module, 41-Report Generation Unit, 42-Early Warning Unit. Detailed Implementation

[0047] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0048] Please see Figure 1-7 The present invention provides a technical solution:

[0049] An information-based software project cost assessment system includes: a data acquisition module 1, a data processing module 2, a cost assessment module 3, and a result output module 4.

[0050] Data Acquisition Module 1: Used to collect various basic data related to the software project;

[0051] Data processing module 2: Cleans, classifies, and integrates the collected data to form a standard data format;

[0052] Cost assessment module 3: Based on the processed data, complete cost estimation, value assessment and deviation analysis;

[0053] Output module 4: Outputs cost assessment results and enhances early warning prompts.

[0054] In this embodiment of the invention, the data acquisition module 1 includes:

[0055] Requirements Information Collection Unit 11: Accurately collect detailed requirements information on the functional requirements, performance indicators, and technical specifications of the software project;

[0056] Historical data acquisition unit 12: The system collects historical data on the cost amount, cost composition, and project schedule of similar software projects in the past;

[0057] Market data acquisition unit 13: Real-time acquisition of dynamic market data on current market labor compensation standards, hardware equipment prices, and software licensing fees.

[0058] In this embodiment of the invention, the data processing module 2 includes:

[0059] Data cleaning unit 21: Improves data accuracy through deduplication, outlier detection, and logical error correction.

[0060] Data classification unit 22: Classify and archive data according to dimensions such as demand type, cost item, and market category;

[0061] Data integration unit 23: Convert multi-source heterogeneous data into a unified data structure and establish a standardized data model.

[0062] Data cleaning: The Isolation Forest algorithm is used to detect outliers in cost data, entity parsing technology is used to eliminate semantic ambiguity in the requirement description, and time series interpolation is used to fill in missing values ​​in historical data;

[0063] Data Classification: Construct a cost item classification system based on the national standard "Software Engineering Software Development Cost Measurement Specification" GB / T36964-2018, use topic modeling to perform functional domain clustering on requirement texts, and establish a regional / industry dimension classification index for market data;

[0064] Data integration: Design a star schema data warehouse to store multi-source data, develop ETL tools to achieve automatic mapping and transformation of data models, and build a data quality monitoring dashboard to track governance effects in real time.

[0065] Requirements information collection: Natural language processing technology is used to parse project documents and extract structured information such as functional requirements list, performance indicators, and technical architecture requirements;

[0066] Historical data matching: Based on project feature tags, retrieve similar project cases from the knowledge base in the past three years, and extract cost breakdown structure (CBS) data and key performance indicators (KPIs);

[0067] Market data aggregation: Connecting with human resources platforms to obtain developer salary curves, capturing real-time quotes from hardware suppliers, and collecting data on open-source component licensing fees.

[0068] In this embodiment of the invention, the cost assessment module 3 includes:

[0069] Cost estimation unit 31: Use analogy estimation model, parametric estimation model, and bottom-up estimation model to calculate costs in combination with project characteristics;

[0070] Value Assessment Unit 32: Evaluate the actual value by comprehensively considering factors such as software functional complexity, technological innovation, and market demand.

[0071] Deviation Analysis Unit 33: Compare the estimated cost value with the assessed value, calculate the deviation range, and determine the degree of deviation.

[0072] Cost estimation: Monte Carlo simulation is used to generate cost probability distribution curves, machine learning algorithms are applied to dynamically adjust the weights of the estimation model, and a cost sensitivity analysis model is constructed to identify key influencing factors;

[0073] Value assessment: Based on the analytic hierarchy process (AHP), a value assessment index system is constructed, a fuzzy comprehensive evaluation model is developed to quantify the functional value of the software, and a real options pricing model is introduced to assess the value of technological innovation;

[0074] Deviation analysis: Establish a dynamic deviation threshold algorithm (automatically adjusted based on project size), apply attribution analysis techniques to locate the root causes of deviations, and develop a deviation propagation path prediction model to assess potential impacts.

[0075] The steps of the Isolation Forest algorithm are as follows:

[0076] S1. Input data: For cost data (such as numerical data like labor costs and hardware quotes);

[0077] S2. Algorithm Configuration: Set the number of trees in the forest (e.g., 100 trees) and the sampling size for each tree (e.g., 256 sample points);

[0078] S3. Random sampling: Randomly selecting a subset of samples from the cost data;

[0079] S4. Recursive Segmentation: Random Feature Selection: In each recursive step, a feature is randomly selected (e.g., "developer compensation");

[0080] Random split point: Randomly select a split value within the range of values ​​of this feature to divide the data point into left and right child nodes;

[0081] Termination condition: The node contains only one data point or the preset tree depth is reached;

[0082] S5. Path length: For each data point, calculate its path length in all isolated trees (i.e., the number of splits from the root node to the leaf node);

[0083] Anomaly scoring formula:

[0084]

[0085] in:

[0086] E(h(x)): The average path length of data point x across all trees.

[0087] c(n): A normalization factor related to the sample size h (approximately...) ).

[0088] Scoring Explanation: A score close to 1 indicates an anomaly (such as an outlier in cost data), while a score close to 0 indicates normality.

[0089] S6. Threshold determination: Set a threshold (e.g., S > 0.7) and mark data points that exceed the threshold as abnormal.

[0090] S7. Cleaning operations: Correct or remove outliers, for example: replace them with interpolations within a reasonable range; mark data points that require manual verification.

[0091] The steps for Monte Carlo simulation are as follows:

[0092] S1. Parametric Modeling: Identify key variables affecting costs (such as labor costs, project duration, and hardware procurement prices), and establish a probability distribution model (such as normal distribution or triangular distribution) for each variable;

[0093] S2. Random sampling: Using computer algorithms, a large number of random samples (e.g., 10,000 times) are taken from each variable, and each sample generates a set of variable values;

[0094] S3. Model Calculation: Substitute each sample value into the cost estimation formula, such as:

[0095] A simulated cost result is obtained;

[0096] Where: i generally represents the i-th simulation iteration currently being executed; n represents the total number of Monte Carlo simulation iterations, which is a manually set parameter;

[0097] S4. Statistical Analysis: Summarize all simulation results and generate the cost probability distribution curve.

[0098] The steps of the Analytic Hierarchy Process (AHP):

[0099] S1. Constructing a hierarchical structure:

[0100] Target layer: Overall value assessment of the software project;

[0101] Criteria layer: Decomposed into dimensions such as function, performance, market demand, and technological innovation;

[0102] Metrics layer: Further refined into specific metrics (such as feature completeness, response time, user growth rate);

[0103] S2. Constructing the judgment matrix: For elements at the same level, compare them pairwise using expert scores, employing a 1-9 scale (e.g., 1 indicates equal importance, 9 indicates extreme importance); compare the importance of "functional integrity" and "performance indicators" to obtain the judgment matrix.

[0104] S3. Calculate the weight vector: Solve for the maximum eigenvalue λmax of the judgment matrix and its corresponding eigenvector. After normalization, the weight of each element is obtained. That is, the weight of "functional integrity" is 0.75 and the weight of "performance index" is 0.25.

[0105] S4. Calculate the consistency index And compare it with the random consistency index RI (obtainable from a table); consistency ratio When CI < 0.1, the judgment matrix is ​​considered to have satisfactory consistency.

[0106] S5. Overall Score Calculation:

[0107] The final value score is obtained by multiplying the actual scores of each indicator by their corresponding weights and summing the results. For example, if the function score is 80 points (weight 0.75) and the performance score is 90 points (weight 0.25), the overall value is 80*0.75+90*0.25=82.5 points.

[0108] In this embodiment of the invention, the cost estimation unit 31 includes a built-in analogy estimation model, which can perform rapid cost comparison based on data from similar historical projects;

[0109] The built-in parameter estimation model of the cost estimation unit 31 calculates costs by quantifying parameters such as function points and lines of code.

[0110] The bottom-up estimation model built into cost estimation unit 31 forms an overall estimate by accumulating module-level costs.

[0111] In this embodiment of the invention, the result output module 4 includes:

[0112] Report generation unit 41: Automatically generates multi-dimensional evaluation reports that include cost details, value assessment, and deviation analysis;

[0113] Warning unit 42: When the cost deviates from the value by more than the preset threshold, a warning will be issued through a visual interface or message push.

[0114] Report generation: Automatically generate multi-dimensional evaluation reports (including Gantt charts, S-curves, and radar charts), develop interactive cost decomposition visualization components, and generate a comparison matrix of alternative solutions based on scenario analysis;

[0115] Early warning response: Design a three-level early warning mechanism (yellow warning: ±10% deviation; orange warning: ±20% deviation; red warning: >20% deviation), develop an intelligent decision suggestion engine to provide corrective measures, and construct a change impact analysis model to evaluate adjustment schemes.

[0116] This invention also discloses a method for cost assessment of information technology software projects, specifically including the following steps:

[0117] S1. Multi-dimensional data collection: Collect project requirements, historical cases, and market data from multiple dimensions through data collection module 1;

[0118] S2. Data Governance and Standardization: Using data processing module 2, the collected multi-dimensional data is cleaned, classified, and integrated to construct a standard dataset;

[0119] S3, Intelligent Assessment and Deviation Analysis: Complete cost estimation, value assessment and deviation analysis with the help of cost assessment module 3;

[0120] S4. Results Visualization and Decision Support: The results output module 4 outputs the cost assessment results, including generating reports and issuing early warning prompts.

[0121] In this embodiment of the invention, when the deviation analysis unit 33 detects a deviation in cost, it feeds back the deviation data to the data processing module 2 for data verification, and simultaneously transmits it to the cost evaluation module 3 to adjust the estimation model parameters, forming an evaluation optimization closed loop.

[0122] In summary, this information technology software project cost assessment system and method are as follows:

[0123] By having the cost estimation unit 31 and value assessment unit 32 of the cost assessment module 3 work together to build a quantitative assessment model based on historical data and market dynamics, the accurate calculation of the cost of the entire life cycle of the software project can be achieved. When the deviation analysis unit 33 detects cost anomalies, it can immediately feed back to the data processing module 2 and the cost assessment module 3 for dynamic adjustment, forming a closed-loop management mechanism of "estimation-assessment-correction". This fundamentally solves the problem of disordered cost management, reduces cost, and ensures reasonable resource allocation and software quality stability through cost and value matching analysis.

[0124] The data acquisition module 1 automatically acquires multi-source data, and the data processing module 2 cleans, classifies and integrates the data. The algorithm model is used to eliminate data noise and form a standardized dataset, reducing the amount of manual data processing by more than 80%. In the cost assessment stage, the cost estimation unit 31 has built-in analog estimation, parameter estimation and other models that can automatically match the optimal algorithm according to the project characteristics. With the help of machine learning, the estimation parameters are dynamically optimized, which improves the cost estimation efficiency by 3-5 times and controls the accuracy error within ±10%, which is significantly better than the error range of ±20%-±10% of the existing technology, and provides a more reliable decision basis for project budget.

[0125] "Machine learning to dynamically optimize estimation parameters" refers to the process of using machine learning algorithms to dynamically adjust and optimize the parameters of the estimation model used in the cost assessment process based on historical project data and actual feedback from the current project, in order to improve the accuracy and efficiency of cost estimation. The specific details are as follows:

[0126] Data foundation: The actual cost data, cost composition data, and project characteristic data (such as functional requirements, performance requirements, and technical architecture) of historical software projects, as well as real-time data of projects currently being evaluated, are used as training data for machine learning. This data covers the entire lifecycle information of the project from requirements analysis and development process to final delivery, providing a rich information foundation for parameter optimization.

[0127] Algorithm selection: Machine learning algorithms suitable for parameter optimization are usually adopted, such as gradient descent and its variants (stochastic gradient descent, batch gradient descent, etc.). These algorithms can find the combination of parameters that minimizes the objective function (such as cost estimation error) in the data through iteration.

[0128] Optimization process:

[0129] A1. Constructing the Objective Function: Define the cost estimation error as the objective function, such as using the mean squared error (MSE), which is the average of the squares of the differences between the actual cost of historical or current projects and the estimated cost calculated using the estimation model and current parameters. The goal is to minimize this error by adjusting the parameters.

[0130] A2. Calculate the gradient: Using the selected machine learning algorithm, calculate the gradient of the objective function with respect to the estimated model parameters. The direction of the gradient indicates the direction of parameter adjustment so that the objective function value changes in the direction of decreasing.

[0131] A3. Parameter Update: Update the parameters of the estimated model based on the calculated gradient and the preset learning rate (which controls the step size of each parameter update). For example, for a certain parameter θ, the update formula may be θ = θ - learning rate × gradient value.

[0132] A4. Iterative optimization: The process of repeatedly calculating gradients and updating parameters, through multiple iterations, gradually converges the objective function value, that is, the error between the estimated cost and the actual cost becomes smaller and smaller, and finally a set of optimal estimation model parameters is determined.

[0133] Dynamic Applications: In actual software project cost assessments, as new project data is continuously generated, or new situations arise during the project's progress (such as changes in requirements or fluctuations in market costs), the machine learning model will re-optimize its parameters in real time or periodically to adapt to the characteristics of different projects and changing environments. This ensures that accurate cost estimates can be provided under various circumstances. For example, when hardware prices fluctuate significantly in the market, the system can quickly adjust the estimation parameters related to hardware costs through machine learning, so that the cost estimates reflect the actual situation in a timely manner.

[0134] By using the value assessment unit 32 to comprehensively evaluate multi-dimensional indicators such as functional complexity, technological innovation, and market demand, the actual value of the software is quantified using the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation model. The deviation analysis unit 33 compares the estimated cost with the value assessment results in real time. When the cost deviates from the value by more than a preset threshold, the early warning unit 42 immediately triggers a response mechanism. At the same time, the closed-loop feedback system adjusts the data processing rules and evaluation model parameters, which greatly improves the matching degree between the cost and the actual value, effectively solves the problem of inflated or understated costs, and provides a scientific value anchor for project cost control.

[0135] System architecture logic: data acquisition → processing → evaluation → output, with clear data flow between modules.

[0136] Closed-loop management mechanism: estimation → evaluation → correction, with a reasonable feedback path for deviation analysis.

[0137] In practice, we use "user verification + expert verification" for error correction offline;

[0138] Online, corrections are performed through (1) identifying logical transactions, (2) identifying logical files, and (3) a cyclical verification mechanism. The verification process mainly consists of three parts:

[0139] A. Duplicate Checking and Labeling: This involves checking for duplicates in the analysis results. It requires considering the context to identify duplicate count items. If a feature has already been counted in previous content, it should be marked as "[Suspected Duplicate]" upon encountering a duplicate. The handling principle is: For the first occurrence, retain the complete count item; subsequent occurrences should be marked as "[Suspected Duplicate]" with a description of the initial occurrence.

[0140] B. Verification and correction: In conjunction with Nesma's general statistical rules, such as the Nesma Function Point Analysis and Application Guide (Version 2.3), check and correct the output content to improve the accuracy of the output results.

[0141] C. Circular verification, checked according to general FPA guidelines, for example: checking from the perspective of logical transactions and logical file dependencies (similar to circular verification mechanisms).

[0142] Furthermore, any content not described in detail in this specification is existing technology known to those skilled in the art.

[0143] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0144] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An information technology software project cost assessment system, characterized in that, include: The system comprises a data acquisition module (1), a data processing module (2), a cost assessment module (3), and a result output module (4). The data acquisition module (1) is used to collect various basic data related to the software project; The data processing module (2) cleans, classifies, and integrates the collected data to form a standard data format; The cost assessment module (3) completes cost estimation, value assessment and deviation analysis based on the processed data; The result output module (4) outputs the cost assessment results and improves the early warning prompts.

2. The information technology software project cost assessment system according to claim 1, characterized in that: The data acquisition module (1) includes: Requirements information collection unit (11): Accurately collect detailed requirements information on the functional requirements, performance indicators, and technical specifications of the software project; Historical data acquisition unit (12): The system collects historical data on the cost amount, cost composition, and schedule of similar software projects in the past; Market data acquisition unit (13): Real-time acquisition of market dynamic data such as current market salary standards, hardware equipment prices, and software licensing fees.

3. The information technology software project cost assessment system according to claim 1, characterized in that: The data processing module (2) includes: Data cleaning unit (21): Improves data accuracy through deduplication, outlier detection, and logical error correction. Data classification unit (22): Data is classified and archived according to dimensions such as demand type, cost item, and market category; Data integration unit (23): Converts multi-source heterogeneous data into a unified data structure and establishes a standardized data model.

4. The information technology software project cost assessment system according to claim 1, characterized in that: The cost assessment module (3) includes: Cost estimation unit (31): Cost calculation is carried out by using analogy estimation model, parametric estimation model and bottom-up estimation model in combination with project characteristics; Value assessment unit (32): Evaluate the actual value by taking into account factors such as the complexity of software functions, technological innovation, and market demand. Deviation Analysis Unit (33): Compare the estimated cost value with the value assessment value, calculate the deviation range and determine the degree of deviation.

5. The information technology software project cost assessment system according to claim 4, characterized in that: The cost estimation unit (31) includes a built-in analogy estimation model that can perform rapid cost comparisons based on data from similar historical projects. The parameter estimation model built into the cost estimation unit (31) performs cost calculation by quantifying parameters such as function points and lines of code. The cost estimation unit (31) has a built-in bottom-up estimation model that accumulates costs at the module level to form an overall estimate.

6. The information technology software project cost assessment system according to claim 1, characterized in that: The result output module (4) includes: Report generation unit (41): Automatically generates multi-dimensional evaluation reports including cost details, value assessment, and deviation analysis; Early warning unit (42): When the cost deviates from the value by more than the preset threshold, an early warning is issued through a visual interface or message push.

7. A method for cost assessment of information technology software projects, implementing the software scale, workload, and cost measurement as described in claims 1-6, specifically including the following steps: S1. Multi-dimensional data collection: Collect project requirements, historical cases and market data from multiple dimensions through the data collection module (1); S2. Data Governance and Standardization: The data processing module (2) is used to clean, classify and integrate the collected multi-dimensional data to build a standard dataset; S3, Intelligent Assessment and Deviation Analysis: Cost estimation, value assessment and deviation analysis are completed with the help of the cost assessment module (3); S4. Results visualization and decision support: The cost assessment results are output by the results output module (4), including generating reports and issuing early warning prompts.

8. The method for cost assessment of information technology software projects according to claim 7, characterized in that: When the deviation analysis unit (33) detects a cost deviation, it feeds the deviation data back to the data processing module (2) for data verification, and at the same time transmits it to the cost evaluation module (3) to adjust the estimation model parameters, forming an evaluation optimization closed loop.

Images