A software development and functional testing system and method based on artificial intelligence.

Abstract

The application provides a software development and function detection system and method based on artificial intelligence, wherein the method comprises the following steps: obtaining the work history of a user when the user develops software or detects functions; determining work assistance content based on an artificial intelligence model according to the work history; and assisting the user in work based on the work assistance content. When the user develops software or detects functions, the application determines work assistance content based on an artificial intelligence model according to the work history of the user, assists the user in work based on the work assistance content, provides targeted assistance to the user, greatly improves the efficiency of the user in developing software or detecting functions, reduces the probability of human errors, and avoids wasting time.

Description

Technical Field

[0001] This invention relates to the field of artificial intelligence technology, and in particular to a software development and functional testing system and method based on artificial intelligence. Background Technology

[0002] Currently, with the rapid development of information technology, software development and functional testing have become indispensable and crucial aspects of the modern technology industry. In this process, developers and testers need to handle a large amount of technical documents, code, functional requirements, test cases, and other information. This work is complex and easily affected by various factors, potentially leading to low efficiency, frequent errors, and even wasting valuable development and functional testing time. Therefore, improving the efficiency of software development and functional testing processes and reducing human error has become a critical issue that urgently needs to be addressed. Summary of the Invention

[0003] One of the objectives of this invention is to provide an artificial intelligence-based software development and functional testing system. When a user is performing software development or functional testing, the system uses an artificial intelligence model to determine the auxiliary tasks based on the user's work history. Based on these auxiliary tasks, the system provides targeted assistance to the user, which greatly improves the efficiency of the user's software development or functional testing work, reduces the probability of human error, and avoids wasting time.

[0004] This invention provides an artificial intelligence-based software development and functional testing system, comprising:

[0005] The acquisition module is used to acquire the user's work history when the user is engaged in software development or functional testing.

[0006] The determination module is used to determine the auxiliary content of the work based on the work history and artificial intelligence model;

[0007] The first auxiliary module is used to provide work assistance to users based on work assistance content.

[0008] Optionally, the first assistance module provides work assistance to the user based on work assistance content, including:

[0009] Predict the user's future job content;

[0010] When the most recent generation time of the work content is no more than the current time threshold and the prediction confidence of the work content exceeds the confidence threshold, obtain the occurrence area sequence of the work content on the user's work interface.

[0011] Identify the first target region related to the work support content from the sequence of occurrence regions;

[0012] Determine a second target region from the occurrence region sequence where the first target region exists within both the preceding and following preset sequence ranges;

[0013] Feature extraction is performed on the auxiliary work content, the first target region, and the second target region to obtain the first feature value set;

[0014] Based on the first feature set, query the display layout strategy library to determine the display layout strategy;

[0015] Based on the display layout strategy, the work support content is displayed in the first target area and the second target area.

[0016] Optionally, the first assistance module provides work assistance to the user based on work assistance content, including:

[0017] Whenever a user continues software development or functional testing, obtain the user's current work operation and the corresponding operation area;

[0018] When a work operation has a corresponding auxiliary item in the work auxiliary content and the deviation of the work operation from the auxiliary item exceeds the deviation threshold, the first target content before the logic of the auxiliary item and the auxiliary item in the work auxiliary content is set in the operation area; wherein, the deviation threshold decreases as the user continues to carry out software development or functional testing work for a longer period of time.

[0019] When a work operation has a corresponding auxiliary item in the work auxiliary content and the deviation of the work operation from the auxiliary item does not exceed the deviation threshold, the logical range library is queried based on the difference between the deviation and the deviation threshold to determine the logical range.

[0020] Set the second target content within the logical scope following the auxiliary items and the auxiliary items in the work auxiliary content in the operation area.

[0021] Optionally, after the first assistance module provides work assistance to the user based on work assistance content, it further includes:

[0022] The representation module is used to represent the content of the work assistance based on the feature map representation template when the user refuses the work assistance, and obtain the content feature map.

[0023] The fine-tuning module is used to assist users in quickly fine-tuning the work-related auxiliary content based on the content feature map;

[0024] The second auxiliary module is used to re-assist users with their work based on the fine-tuned work assistance content.

[0025] Optionally, the fine-tuning module assists users in quickly fine-tuning the work-related auxiliary content based on the content feature map, including:

[0026] The target content is determined from the content feature map; where the target content is a local part of the work assistance content where the expected value expected by the artificial intelligence model does not exceed the expected value threshold or the actual effect value of assisting the user in their work does not exceed the effect value threshold.

[0027] Feature extraction is performed on the graph region to obtain the second feature value set;

[0028] Based on the second feature value set, query the sort value database to determine the sort value;

[0029] Users are guided to view the corresponding graph areas in the content feature map in descending order of their sorting values.

[0030] Whenever a user stays in a guided viewing area for more than a certain duration threshold, the corresponding image area is designated as the first target image area, and the viewing trajectory of the user's content feature image is recorded.

[0031] Based on the viewed trajectory, plan the content slices and the standard positional relationship between the content slices and the first target map area;

[0032] When the user views the first target image area again, the content slice will be continuously displayed to the user while maintaining a standard positional relationship with the first target image area.

[0033] The quick selection table of replacement assistance requirements for the target content corresponding to the first target map area will be randomly output and displayed to the user;

[0034] When a user selects a replacement auxiliary requirement from the quick selection table, the replacement content is determined based on the artificial intelligence model, according to the target content corresponding to the first target map area and the replacement auxiliary requirement.

[0035] Based on the replacement content, replace the target content corresponding to the first target map area in the auxiliary work content.

[0036] Optionally, the step of planning content slices and the standard positional relationship between content slices and the first target map region based on the viewing trajectory includes:

[0037] The observed trajectory shows a continuous local trajectory that first exits the first target map region and then re-enters the first target map region; however, the local trajectory does not pass through the first target map region.

[0038] Determine the trajectory point furthest from the first target map region from the local trajectory;

[0039] The local trajectory is divided into two segments by using the trajectory points as dividing points;

[0040] When the similarity between two trajectory segments does not exceed the similarity threshold, the second target map region traversed by the local trajectory is obtained;

[0041] Feature extraction is performed on the first target map region and the second target map region to obtain a third feature set;

[0042] Based on the third feature set, query the slice load content search rule base to determine the slice load content search rules;

[0043] Based on the slice load content search rules, search for slice load content in the content feature map;

[0044] Based on the slice template, content slices are generated according to the content loaded by the slice;

[0045] Feature extraction is performed on the last segment of the viewed trajectory with a preset length to obtain the fourth feature set;

[0046] Based on the fourth feature set, the standard positional relationship is determined by querying the standard positional relationship database.

[0047] This invention provides a software development and functional testing method based on artificial intelligence, comprising:

[0048] When a user is performing software development or functional testing, obtain the user's work history;

[0049] Based on an artificial intelligence model and work history, determine the content of work assistance.

[0050] Based on work assistance content, provide work assistance to users.

[0051] Optionally, the provision of work assistance to users based on work assistance content includes:

[0052] Predict the user's future job content;

[0053] When the most recent generation time of the work content is no more than the current time threshold and the prediction confidence of the work content exceeds the confidence threshold, obtain the occurrence area sequence of the work content on the user's work interface.

[0054] Identify the first target region related to the work support content from the sequence of occurrence regions;

[0055] Determine a second target region from the occurrence region sequence where the first target region exists within both the preceding and following preset sequence ranges;

[0056] Feature extraction is performed on the auxiliary work content, the first target region, and the second target region to obtain the first feature value set;

[0057] Based on the first feature set, query the display layout strategy library to determine the display layout strategy;

[0058] Based on the display layout strategy, the work support content is displayed in the first target area and the second target area.

[0059] Optionally, the provision of work assistance to users based on work assistance content includes:

[0060] Whenever a user continues software development or functional testing, obtain the user's current work operation and the corresponding operation area;

[0061] When a work operation has a corresponding auxiliary item in the work auxiliary content and the deviation of the work operation from the auxiliary item exceeds the deviation threshold, the first target content before the logic of the auxiliary item and the auxiliary item in the work auxiliary content is set in the operation area; wherein, the deviation threshold decreases as the user continues to carry out software development or functional testing work for a longer period of time.

[0062] When a work operation has a corresponding auxiliary item in the work auxiliary content and the deviation of the work operation from the auxiliary item does not exceed the deviation threshold, the logical range library is queried based on the difference between the deviation and the deviation threshold to determine the logical range.

[0063] Set the second target content within the logical scope following the auxiliary items and the auxiliary items in the work auxiliary content in the operation area.

[0064] Optionally, after providing work assistance to the user based on work assistance content, the method further includes:

[0065] When a user refuses work assistance, the work assistance content is represented by a feature map based on the feature map representation template to obtain a content feature map;

[0066] It assists users in quickly fine-tuning work-related support content based on content feature maps;

[0067] Based on the slightly adjusted work assistance content, work assistance will be re-provided to users.

[0068] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings.

[0069] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0070] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0071] Figure 1 This is a schematic diagram of an artificial intelligence-based software development and functional testing system according to an embodiment of the present invention;

[0072] Figure 2 This is a schematic diagram of a software development and function testing method based on artificial intelligence in an embodiment of the present invention. Detailed Implementation

[0073] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0074] This invention provides an artificial intelligence-based software development and functional testing system, such as... Figure 1 As shown, it includes:

[0075] Module 1 is used to obtain the user's work history when the user is performing software development or functional testing.

[0076] Module 2 is used to determine the auxiliary content of the work based on the work history and an artificial intelligence model;

[0077] The first auxiliary module 3 is used to provide work assistance to users based on work assistance content.

[0078] In the above technical solution, the user is a software developer or functional tester; the work history includes at least: the user's historical work records of software development or functional testing, such as: processed technical documents, designed code, software design functional requirements, and test cases used for functional testing of the software; the artificial intelligence model is a model obtained by training a neural network using a large amount of software development and functional testing experience. It can determine the work assistance content that will help the user continue to carry out software development or functional testing work based on the work history. For example, if the work content reflects that the user needs to test a certain function of the software, then the work assistance content is the test cases used to complete the test; finally, work assistance is provided to the user based on the work assistance content.

[0079] This application, when users are engaged in software development or functional testing, uses an artificial intelligence model to determine the content of their work assistance based on their work history. Based on this content, it provides targeted assistance to users, which greatly improves their efficiency in software development or functional testing, reduces the probability of human error, and avoids wasting time.

[0080] In one embodiment, the first assistance module provides work assistance to the user based on work assistance content, including:

[0081] Predict the user's future job content;

[0082] When the most recent generation time of the work content is no more than the current time threshold and the prediction confidence of the work content exceeds the confidence threshold, obtain the occurrence area sequence of the work content on the user's work interface.

[0083] Identify the first target region related to the work support content from the sequence of occurrence regions;

[0084] Determine a second target region from the occurrence region sequence where the first target region exists within both the preceding and following preset sequence ranges;

[0085] Feature extraction is performed on the auxiliary work content, the first target region, and the second target region to obtain the first feature value set;

[0086] Based on the first feature set, query the display layout strategy library to determine the display layout strategy;

[0087] Based on the display layout strategy, the work support content is displayed in the first target area and the second target area.

[0088] In the above technical solution, the work content includes at least: work records that the user may generate when continuing software development or functional testing in the future; prediction can be made by combining the user's historical work history of extensive software development or functional testing with the user's current work history of software development or functional testing, which reflects their work habits, and therefore can be combined for prediction; the most recent generation time of the work content refers to the time when the user most recently continues software development or functional testing; the time threshold can be, for example, 10 minutes; the prediction confidence level represents the degree of confidence that the user will generate work content in the future, which can be the number of times the user's historical work history of simultaneously generating the current work history of software development or functional testing and the predicted work content; the confidence level threshold can be 2; when the most recent generation time is no more than the time threshold and the prediction confidence level of the work content exceeds the confidence level threshold, it indicates that the work content can be displayed in advance; the user's work interface refers to the computer interface on which the user performs software development or functional testing; the occurrence area sequence includes the operation areas on the work interface in sequence when the user generates work content; and work auxiliary content. "Relevance" refers to the fact that if the work assistance content is adopted by the user, its execution area is within the occurrence area, etc.; if it is relevant to the work assistance content, the corresponding occurrence area can be used for the display layout of the work assistance content as the first target area; the preset sequence range can be, for example, three occurrence areas; if the first target area exists both before and after a certain occurrence area in the occurrence area sequence, it means that to ensure the continuity of the display layout, it can also be used for the display layout of the work assistance content as the second target area; the features in the first feature set include at least: the type of work assistance content, the area type and location of the first target area, the area type and location of the second target area, etc.; the display layout strategy library contains display layout strategies corresponding to different first feature sets, the first feature value set reflects the situation where work assistance content needs to be displayed, and the display layout strategy indicates how to display the work assistance content in this situation; finally, based on the display layout strategy, the work assistance content is displayed in the first target area and the second target area; when the user continues to carry out software development or functional testing work, they can see the pre-displayed layout of the work assistance content, and can quickly obtain work assistance by combining the displayed layout area.

[0089] This invention utilizes time thresholds and confidence thresholds to accurately determine the timing for advance display layout of work content, improving the accuracy of advance display layout and reducing system workload. It determines a first target region and a second target region from the occurrence region sequence, using these two as the basis for display layout, further improving the accuracy and suitability of advance display layout. By introducing a first feature value set and a real-world layout strategy library, it quickly determines the display layout strategy, improving the efficiency of advance display layout.

[0090] In one embodiment, the first assistance module provides work assistance to the user based on work assistance content, including:

[0091] Whenever a user continues software development or functional testing, obtain the user's current work operation and the corresponding operation area;

[0092] When a work operation has a corresponding auxiliary item in the work auxiliary content and the deviation of the work operation from the auxiliary item exceeds the deviation threshold, the first target content before the logic of the auxiliary item and the auxiliary item in the work auxiliary content is set in the operation area; wherein, the deviation threshold decreases as the user continues to carry out software development or functional testing work for a longer period of time.

[0093] When a work operation has a corresponding auxiliary item in the work auxiliary content and the deviation of the work operation from the auxiliary item does not exceed the deviation threshold, the logical range library is queried based on the difference between the deviation and the deviation threshold to determine the logical range.

[0094] Set the second target content within the logical scope following the auxiliary items and the auxiliary items in the work auxiliary content in the operation area.

[0095] In the above technical solution, the user's current work operation refers to the operation performed by the user while continuing software development or functional testing. The corresponding operation area is the area where the user performs the work operation, located on the work interface. The corresponding auxiliary items in the work assistance content refer to content items that can assist the user in performing work operations. The deviation degree of the work operation from the auxiliary item refers to the degree of difference between the work operation and the operation indicated by the auxiliary item. The deviation degree threshold can be as shown in Figure 4. When a work operation has a corresponding auxiliary item in the work assistance content and the deviation degree of the work operation from the auxiliary item exceeds the deviation degree threshold, it indicates that the user needs to restart the work. Therefore, the logic of the auxiliary item and the auxiliary items in the work assistance content is placed before the first target. The content is set in the operation area. Each item in the work assistance content has a logical execution order, and items before the logical execution order are arranged before the assistance items. However, when a work operation has a corresponding assistance item in the work assistance content and the deviation of the work operation from the assistance item does not exceed the deviation threshold, it indicates that the user's future work can be guided and assisted. The larger the difference between the deviation and the deviation threshold, the greater the degree to which the work operation fits the assistance item. The greater the degree of assistance, the larger the logical range. The logical range can be the number of items in the work assistance content, such as 3. The assistance item and the second target content within the logical range following the logic of the assistance item in the work assistance content are set in the operation area.

[0096] The technical solution of this embodiment differs from that of the previous embodiment. Whenever the user continues to carry out software development or function testing, the auxiliary content set in the operation area is accurately determined by combining the deviation degree, deviation degree threshold, etc., and set accordingly in the operation area, so as to realize accurate and real-time assistance to the user, which greatly improves the user assistance effect.

[0097] In one embodiment, after the first assistance module provides work assistance to the user based on work assistance content, it further includes:

[0098] The representation module is used to represent the content of the work assistance based on the feature map representation template when the user refuses the work assistance, and obtain the content feature map.

[0099] The fine-tuning module is used to assist users in quickly fine-tuning the work-related auxiliary content based on the content feature map;

[0100] The second auxiliary module is used to re-assist users with their work based on the fine-tuned work assistance content.

[0101] In the above technical solution, the feature map representation template is a feature map template pre-set by technicians, which can be a data structure of different data types, etc. Based on the feature map representation template, the work assistance content can be represented by feature maps to obtain content feature maps. The content feature maps reflect the characteristics of the work assistance content, thus assisting users in quickly fine-tuning the work assistance content to meet their actual requirements. Based on the fine-tuned work assistance content, work assistance is re-provided to the user. Generally, if the work assistance content does not meet the user's requirements, the user will refuse assistance. At this time, the artificial intelligence model needs to determine new work assistance content based on the user's newly generated work history, which is cumbersome and results in the user not receiving effective assistance at the current stage. The embodiments of the present invention can solve this problem by representing the work assistance content by feature maps to obtain content feature maps, assisting users in quickly fine-tuning the work assistance content based on the content feature maps, and re-providing work assistance to the user based on the fine-tuned work assistance content. This greatly improves the assistance efficiency and effect, enhances the applicability of the system, and makes it more humanized and intelligent.

[0102] In one embodiment, the fine-tuning module assists the user in quickly fine-tuning the work-related auxiliary content based on the content feature map, including:

[0103] The target content is determined from the content feature map; where the target content is a local part of the work assistance content where the expected value expected by the artificial intelligence model does not exceed the expected value threshold or the actual effect value of assisting the user in their work does not exceed the effect value threshold.

[0104] Feature extraction is performed on the graph region to obtain the second feature value set;

[0105] Based on the second feature value set, query the sort value database to determine the sort value;

[0106] Users are guided to view the corresponding graph areas in the content feature map in descending order of their sorting values.

[0107] Whenever a user stays in a guided viewing area for more than a certain duration threshold, the corresponding image area is designated as the first target image area, and the viewing trajectory of the user's content feature image is recorded.

[0108] Based on the viewed trajectory, plan the content slices and the standard positional relationship between the content slices and the first target map area;

[0109] When the user views the first target image area again, the content slice will be continuously displayed to the user while maintaining a standard positional relationship with the first target image area.

[0110] The quick selection table of replacement assistance requirements for the target content corresponding to the first target map area will be randomly output and displayed to the user;

[0111] When a user selects a replacement auxiliary requirement from the quick selection table, the replacement content is determined based on the artificial intelligence model, according to the target content corresponding to the first target map area and the replacement auxiliary requirement.

[0112] Based on the replacement content, replace the target content corresponding to the first target map area in the auxiliary work content.

[0113] In the above technical solution, the expected value expected by the artificial intelligence model refers to the degree to which the work assistance content is expected to have an auxiliary effect on the user; the expected value threshold can be, for example, 4. The actual effect value of assisting the user refers to the degree of effect of using the work assistance content to assist the user; the effect value threshold can be, for example, 2. The target content is the local content of the work assistance content where the expected value expected by the artificial intelligence model does not exceed the expected value threshold or the actual effect value of assisting the user does not exceed the effect value threshold. Therefore, the corresponding graph region is the content area that the user needs to pay attention to and decide how to fine-tune the work assistance content. The features in the second feature value set include the number of other graph regions within a circular area with a radius of 8 cm around the graph region, etc. The ranking value library contains ranking values ​​corresponding to different second feature value sets. The second feature value set reflects the degree to which the user needs to pay attention first, and the larger the corresponding ranking value. For example, the larger the number of other graph regions within a circular area with a radius of 8 cm around the graph region, the easier it is for the user to view other graph regions when viewing this graph region. Therefore, the larger the corresponding ranking value, the more users are guided to view the corresponding areas in the content feature map in descending order of ranking value; the duration threshold can be 30 seconds; the viewing trajectory is the trajectory formed by the user's line of sight moving on the content feature map when viewing it; the viewing trajectory reflects the user's viewing situation; based on the planned content slices and the standard positional relationship between the content slices and the first target map area; when the user views the first target map area again, the content slices are continuously displayed to the user while maintaining a standard positional relationship with the first target map area, allowing the user to receive assistance from the best angle; the quick selection table for replacement assistance requirements of the target content corresponding to the first target map area has multiple preset replacement assistance requirements generated by the target content corresponding to the first target map area, which are randomly output and displayed to the user for easy selection of replacement assistance requirements; then, based on the artificial intelligence model, the replacement content is determined according to the target content corresponding to the first target map area and the replacement assistance requirements, and the target content corresponding to the first target map area in the work assistance content is replaced based on the replacement content to achieve fine-tuning.

[0114] This invention, through its embodiments, identifies target content regions in a graph, performs targeted feature extraction and sorting, helping users quickly identify content areas requiring attention, thereby efficiently optimizing the effectiveness of work assistance content. Based on the user's viewing trajectory and dwell time, it guides the user to gradually focus on important areas on the content feature graph, ensuring the fine-tuning process is orderly and meets the user's actual needs. By randomly displaying a quick selection table of replacement assistance needs and combining it with artificial intelligence model analysis, it intelligently recommends the most suitable replacement content, improving the adaptability and user experience of new work assistance content. Through customized guidance and dynamic feedback, users can obtain more accurate and personalized work assistance in a shorter time, enhancing work efficiency and user experience.

[0115] In one embodiment, the step of planning content slices and the standard positional relationship between content slices and the first target map region based on the viewing trajectory includes:

[0116] The observed trajectory shows a continuous local trajectory that first exits the first target map region and then re-enters the first target map region; however, the local trajectory does not pass through the first target map region.

[0117] Determine the trajectory point furthest from the first target map region from the local trajectory;

[0118] The local trajectory is divided into two segments by using the trajectory points as dividing points;

[0119] When the similarity between two trajectory segments does not exceed the similarity threshold, the second target map region traversed by the local trajectory is obtained;

[0120] Feature extraction is performed on the first target map region and the second target map region to obtain a third feature set;

[0121] Based on the third feature set, query the slice load content search rule base to determine the slice load content search rules;

[0122] Based on the slice load content search rules, search for slice load content in the content feature map;

[0123] Based on the slice template, content slices are generated according to the content loaded by the slice;

[0124] Feature extraction is performed on the last segment of the viewed trajectory with a preset length to obtain the fourth feature set;

[0125] Based on the fourth feature set, the standard positional relationship is determined by querying the standard positional relationship database.

[0126] In the above technical solution, the similarity threshold can be 80%. When the similarity between two trajectory segments does not exceed the similarity threshold, it indicates that the user did not immediately return to the first target image area after viewing the second target image area, which means that the second target image area is the user's newly focused area. Feature extraction is performed on the first and second target image areas. The features obtained in the third feature set include the region type of the first target image area and the region type of the second target image area. The slice load content search rule library contains slice load content search rules corresponding to different third feature sets. The slice load content search rules indicate how to search for slice load content in the content feature map. Slice load content is the content that needs to be displayed on the content slice and is loaded. This content plays a targeted auxiliary role after the user has viewed the second target image area. Based on the slice load content search rules, slice load content is searched in the content feature map, and content slices are generated accordingly. The final preset length can be 20 centimeters. The last segment reflects the user's final viewing situation and can be used to determine the best angle for presenting the content slice. Since the user needs to compare the content slice with the first target image area, the standard positional relationship between the content slice and the first target image area is determined. The standard positional relationship library contains standard positional relationships corresponding to different fourth feature sets.

[0127] This invention, through analysis of viewing patterns, can identify changes in user interest in newly viewed areas and provide targeted content to enhance user experience. Utilizing feature extraction and a search rule base, it can intelligently generate content slices that match user needs, ensuring the displayed content is targeted and practical. By planning standard positional relationships, it ensures the accurate relative relationship between the content slice display position and the user's current viewing area, optimizing the visual presentation of the content and improving interactive smoothness. Furthermore, it can automatically adjust the slice display method based on the user's viewing pattern, adapting to different user behavior patterns and improving the accuracy of content recommendations.

[0128] This invention provides a software development and function testing method based on artificial intelligence, such as... Figure 2 As shown, it includes:

[0129] S1. Obtain the user's work history when the user is performing software development or functional testing.

[0130] S2. Based on an artificial intelligence model and work history, determine the auxiliary work content;

[0131] S3. Provide work assistance to users based on work-related content.

[0132] The work assistance provided to users based on work assistance content includes:

[0133] Predict the user's future job content;

[0134] When the most recent generation time of the work content is no more than the current time threshold and the prediction confidence of the work content exceeds the confidence threshold, obtain the occurrence area sequence of the work content on the user's work interface.

[0135] Identify the first target region related to the work support content from the sequence of occurrence regions;

[0136] Determine a second target region from the occurrence region sequence where the first target region exists within both the preceding and following preset sequence ranges;

[0137] Feature extraction is performed on the auxiliary work content, the first target region, and the second target region to obtain the first feature value set;

[0138] Based on the first feature set, query the display layout strategy library to determine the display layout strategy;

[0139] Based on the display layout strategy, the work support content is displayed in the first target area and the second target area.

[0140] The work assistance provided to users based on work assistance content includes:

[0141] Whenever a user continues software development or functional testing, obtain the user's current work operation and the corresponding operation area;

[0142] When a work operation has a corresponding auxiliary item in the work auxiliary content and the deviation of the work operation from the auxiliary item exceeds the deviation threshold, the first target content before the logic of the auxiliary item and the auxiliary item in the work auxiliary content is set in the operation area; wherein, the deviation threshold decreases as the user continues to carry out software development or functional testing work for a longer period of time.

[0143] When a work operation has a corresponding auxiliary item in the work auxiliary content and the deviation of the work operation from the auxiliary item does not exceed the deviation threshold, the logical range library is queried based on the difference between the deviation and the deviation threshold to determine the logical range.

[0144] Set the second target content within the logical scope following the auxiliary items and the auxiliary items in the work auxiliary content in the operation area.

[0145] Following the provision of work assistance to users based on work assistance content, the process also includes:

[0146] When a user refuses work assistance, the work assistance content is represented by a feature map based on the feature map representation template to obtain a content feature map;

[0147] It assists users in quickly fine-tuning work-related support content based on content feature maps;

[0148] Based on the slightly adjusted work assistance content, work assistance will be re-provided to users.

[0149] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. An artificial intelligence based software development and functionality detection system, characterized by, The method comprises the following steps: acquiring a work history of a user when the user is developing software or performing function detection work; determining work assistance content based on an artificial intelligence model and the work history; assisting the user in the work based on the work assistance content. The assisting the user in the work based on the work assistance content comprises the following steps: predicting future work content of the user; when a time interval between a latest generation time of the work content and a current time is less than a time threshold and a prediction confidence of the work content is greater than a confidence threshold, acquiring a sequence of occurrence regions of the work content on a work interface of the user; determining a first target region related to the work assistance content from the sequence of occurrence regions; determining a second target region from the sequence of occurrence regions, wherein the first target region and the second target region are both within a preset sequence range; extracting features of the work assistance content, the first target region and the second target region to obtain a first feature value set; querying a display layout strategy library based on the first feature value set to determine a display layout strategy; displaying the work assistance content in the first target region and the second target region based on the display layout strategy.

2. The artificial intelligence based software development and functionality detection system as claimed in claim 1, wherein, The assisting the user in the work based on the work assistance content comprises the following steps: acquiring a current work operation and a corresponding operation region of the user every time the user continues to develop software or perform function detection work; when the work operation has a corresponding assistance item in the work assistance content and a deviation degree of the work operation from the assistance item is greater than a deviation degree threshold, setting first target content before the assistance item in the work assistance content in the operation region; wherein the deviation degree threshold decreases with an increase in a cumulative length of time during which the user continues to develop software or perform function detection work; when the work operation has a corresponding assistance item in the work assistance content and the deviation degree of the work operation from the assistance item is not greater than the deviation degree threshold, querying a logic range library based on a difference between the deviation degree and the deviation degree threshold to determine a logic range; setting second target content in the logic range after the assistance item in the work assistance content in the operation region.

3. The artificial intelligence based software development and functionality detection system as claimed in claim 1, wherein, The assisting the user in the work based on the work assistance content further comprises the following steps: when the user refuses the work assistance, representing the work assistance content in a feature map based on a feature map representation template to obtain a content feature map; fine-tuning the work assistance content based on the content feature map; reassisting the user in the work based on the fine-tuned work assistance content.

4. The artificial intelligence based software development and functionality detection system of claim 3, wherein, The fine-tuning the work assistance content based on the content feature map comprises the following steps: determining a graph region of target content from the content feature map; wherein the target content is local content in the work assistance content that has an expected value not greater than an expected value threshold or an actual effect value of the work assistance content on the user not greater than an effect value threshold; extracting features of the graph region to obtain a second feature value set; query a ranking value library based on the second feature value set to determine a ranking value; direct the user to view corresponding graph regions in the content feature graph in order from large to small according to the ranking value; when the user stays in a graph region in the guided view for more than a time threshold, the corresponding graph region is taken as a first target graph region, and a viewing track of the user viewing the content feature graph is started to be recorded; based on the viewing track, a content slice and a standard position relationship between the content slice and the first target graph region are planned; when the user views the first target graph region again, the content slice is kept in the standard position relationship with the first target graph region and is continuously output and displayed to the user; a replacement auxiliary requirement quick selection table corresponding to the first target graph region is randomly output and displayed to the user; when the user selects a replacement auxiliary requirement from the replacement auxiliary requirement quick selection table, based on an artificial intelligence model, replacement content is determined according to the target content corresponding to the first target graph region and the replacement auxiliary requirement; based on the replacement content, the target content corresponding to the first target graph region in the work assistance content is replaced.

5. The artificial intelligence based software development and functionality detection system of claim 4, wherein, The planning of the content slice and the standard position relationship between the content slice and the first target graph region based on the viewing track includes: determining a local track continuously from the first target graph region to the first target graph region from the viewing track; wherein the local track does not pass through the first target graph region; determining a track point farthest from the first target graph region from the local track; segmenting the local track into two track segments with the track point as a segmentation point; when the similarity between the two track segments does not exceed a similarity threshold, a second target graph region passed through by the local track is obtained; performing feature extraction on the first target graph region and the second target graph region to obtain a third feature set; based on the third feature set, querying a slice load content search rule library to determine a slice load content search rule; based on the slice load content search rule, searching for slice load content in the content feature graph; generating the content slice based on the slice template and the slice load content; performing feature extraction on the last end segment of the viewing track of a preset length to obtain a fourth feature set; based on the fourth feature set, querying a standard position relationship library to determine a standard position relationship.

6. An artificial intelligence-based software development and function detection method, characterized by, It includes: when the user is developing software or performing function detection work, obtaining a work history of the user; based on an artificial intelligence model, determining work assistance content according to the work history; based on the work assistance content, assisting the user in work; The work assistance to the user based on the work assistance content includes: predicting future work content of the user; when the work content is generated within a time threshold from the current time and the prediction confidence of the work content exceeds a confidence threshold, obtaining a sequence of occurrence regions of the work content on a work interface of the user; determining a first target region related to the work assistance content from the sequence of occurrence regions; determining a second target region in which the first target region exists within a preset sequence range from the sequence of occurrence regions; performing feature extraction on the work assistance content, the first target region, and the second target region to obtain a first feature value set; Based on the first feature value set, a display layout strategy library is queried to determine a display layout strategy; Based on the display layout strategy, the work assistance content is displayed in the first target area and the second target area. 7.The artificial intelligence-based software development and function detection method of claim 6, wherein The work assistance to the user based on the work assistance content includes: Whenever the user continues the software development or function detection work, the current work operation of the user and the corresponding operation area are acquired; When the work operation has a corresponding assistance item in the work assistance content and the deviation degree of the work operation from the assistance item exceeds a deviation degree threshold, the assistance item and the first target content before the logic of the assistance item in the work assistance content are set in the operation area; wherein the deviation degree threshold decreases as the cumulative length of time of the user continuing the software development or function detection work increases; When the work operation has a corresponding assistance item in the work assistance content and the deviation degree of the work operation from the assistance item does not exceed the deviation degree threshold, a logic range library is queried based on the difference between the deviation degree and the deviation degree threshold to determine the logic range; The assistance item and the second target content in the logic range after the logic of the assistance item in the work assistance content are set in the operation area. 8.The AI-based software development and function detection method of claim 6, wherein After the work assistance to the user based on the work assistance content, the method further includes: When the user refuses the work assistance, a feature map representation of the work assistance content is performed based on a feature map representation template to obtain a content feature map; The user is assisted in quickly fine-tuning the work assistance content based on the content feature map; The user is re-assisted in the work based on the fine-tuned work assistance content.

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