Software development and function detection system and method based on artificial intelligence

Abstract

The invention provides a software development and function detection system and method based on artificial intelligence, and the method comprises the steps: obtaining the work history of a user when the user carries out the software development or function detection work; based on an artificial intelligence model, according to the work history, determining work auxiliary content; and performing work assistance on the user based on the work assistance content. When the user carries out software development or function detection work, the work auxiliary content is determined based on the artificial intelligence model according to the work history of the user, work assistance is carried out on the user based on the work auxiliary content, and targeted assistance is carried out on the user, so that the efficiency of carrying out software development or function detection work by the user is greatly improved, and the user experience is improved. The probability of human errors is reduced, and time waste is avoided.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and particularly relates to a software development and function detection system and method based on artificial intelligence. Background Art

[0002] At present, with the rapid development of information technology, software development and function detection have become indispensable important links in the modern technology industry. In this process, developers and testers need to process a large amount of information such as technical documents, codes, function requirements, test cases, etc. The work is complex and vulnerable to various factors, which may lead to low work efficiency, frequent errors, and even waste of precious development and function detection time. Therefore, improving the work efficiency in the software development and function detection process and reducing human errors have become key issues to be solved urgently. Summary of the Invention

[0003] One of the purposes of the present invention is to provide a software development and function detection system based on artificial intelligence. When a user performs software development or function detection work, based on an artificial intelligence model, according to the user's work history, determine work assistance content, and based on the work assistance content, provide work assistance to the user, providing targeted assistance to the user, greatly improving the efficiency of the user's software development or function detection work, reducing the probability of human errors, and avoiding time waste.

[0004] A software development and function detection system based on artificial intelligence provided by an embodiment of the present invention includes:

[0005] An acquisition module, configured to acquire the user's work history when the user performs software development or function detection work;

[0006] A determination module, configured to determine work assistance content based on an artificial intelligence model according to the work history;

[0007] A first assistance module, configured to provide work assistance to the user based on the work assistance content.

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

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

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

[0011] Determine a first target area related to the work assistance content from the occurrence area sequence;

[0012] Determine a second target area in which a first target area exists within both the front and rear preset sequences from the occurrence area sequence;

[0013] Extract features from the work assistance content, the first target area, and the second target area to obtain a first set of feature values;

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

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

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

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

[0018] When there is a corresponding assistance item in the work assistance content for the work operation and the deviation degree of the work operation from the assistance item exceeds the deviation degree threshold, set the assistance item and all the first target content before the logic of the assistance item in the work assistance content in the operation area; wherein, the deviation degree threshold decreases as the cumulative duration of the user's continued software development or function testing work increases;

[0019] When there is a corresponding assistance item in the work assistance content for the work operation and the deviation degree of the work operation from the assistance item does not exceed the deviation degree threshold, query the logic range library based on the difference between the deviation degree and the deviation degree threshold to determine the logic range;

[0020] Set the assistance item and the second target content within the logic range after the logic of the assistance item in the work assistance content in the operation area.

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

[0022] A representation module, used to, when the user rejects the work assistance, perform a feature map representation of the work assistance content based on the feature map representation template to obtain a content feature map;

[0023] A fine-tuning module, used to assist the user in quickly fine-tuning the work assistance content based on the content feature map;

[0024] A second assistance module, used to provide work assistance to the user again based on the fine-tuned work assistance content.

[0025] Optionally, the fine-tuning module assisting the user in quickly fine-tuning the work assistance content based on the content feature map includes:

[0026] Determine the graph region of the target content from the content feature graph; wherein, the target content is the local content in the work assistance content whose expected value expected by the artificial intelligence model does not exceed the expected value threshold or the effect value of the actual effect of assisting the user in work does not exceed the effect value threshold;

[0027] Extract features from the graph region to obtain a second set of feature values;

[0028] Based on the second set of feature values, query the sorting value library to determine the sorting value;

[0029] Guide the user to view the corresponding graph regions in the content feature graph in descending order of the sorting value;

[0030] Whenever the user stays and views the graph region under guidance for more than the duration threshold, use the corresponding graph region as the first target graph region and start recording the viewing trajectory of the user viewing the content feature graph;

[0031] Based on the viewing trajectory, plan the content slices and the standard positional relationship between the content slices and the first target graph region;

[0032] When the user views the first target graph region again, keep the content slices in the standard positional relationship with the first target graph region and continuously output and display them to the user;

[0033] Randomly output and display to the user the replacement assistance requirement quick selection table of the target content corresponding to the first target graph region;

[0034] When the user selects a replacement assistance requirement from the replacement assistance requirement quick selection table, based on the artificial intelligence model, determine the replacement content according to the target content corresponding to the first target graph region and the replacement assistance requirement;

[0035] Based on the replacement content, replace the target content corresponding to the first target graph region in the work assistance content.

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

[0037] Determine the local trajectory on the viewing trajectory that continuously exits from the first target graph region and then enters the first target graph region; wherein, the local trajectory does not pass through the first target graph region;

[0038] Determine the trajectory point farthest from the first target graph region on the local trajectory;

[0039] Use the trajectory point as a segmentation point to divide the local trajectory into two trajectory segments;

[0040] When the similarity between two trajectory segments does not exceed the similarity threshold, obtain the second target map area passed by the local trajectory;

[0041] Extract features from the first target map area and the second target map area to obtain a third feature set;

[0042] Based on the third feature set, query the slice payload content search rule library to determine the slice payload content search rule;

[0043] Based on the slice payload content search rule, search for the slice payload content in the content feature map;

[0044] Based on the slice template, generate a content slice according to the slice payload content;

[0045] Extract features from the end segment of the last preset length of the viewing trajectory to obtain a fourth feature set;

[0046] Based on the fourth feature set, query the standard position relationship library to determine the standard position relationship.

[0047] A software development and function detection method based on artificial intelligence provided by an embodiment of the present invention includes:

[0048] When the user performs software development or function detection work, obtain the user's work history;

[0049] Based on the artificial intelligence model, determine the work assistance content according to the work history;

[0050] Based on the work assistance content, assist the user in their work.

[0051] Optionally, the assisting the user in their work based on the work assistance content includes:

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

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

[0054] Determine the first target area related to the work assistance content from the occurrence area sequence;

[0055] Determine the second target area in which the first target area exists within the pre-set sequence range before and after from the occurrence area sequence;

[0056] Extract features from the work assistance content, the first target area, and the second target area to obtain a first eigenvalue set;

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

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

[0059] Optionally, the work assistance for the user based on the work assistance content includes:

[0060] Whenever the user continues with software development or function detection work, obtain the user's current work operation and the corresponding operation area;

[0061] When there is a corresponding assistance item in the work assistance content for the work operation and the deviation degree of the work operation from the assistance item exceeds the deviation degree threshold, set the assistance item and all the first target content before the logic of the assistance item in the work assistance content in the operation area; wherein, the deviation degree threshold decreases as the cumulative duration of the user's continued software development or function detection work increases;

[0062] When there is a corresponding assistance item in the work assistance content for the work operation and the deviation degree of the work operation from the assistance item does not exceed the deviation degree threshold, query the logic range library based on the difference between the deviation degree and the deviation degree threshold to determine the logic range;

[0063] Set the assistance item and the second target content within the logic range after the logic of the assistance item in the work assistance content in the operation area.

[0064] Optionally, after the work assistance for the user based on the work assistance content, it further includes:

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

[0066] Assist the user in quickly fine-tuning the work assistance content based on the content feature map;

[0067] Based on the fine-tuned work assistance content, perform work assistance for the user again.

[0068] Other features and advantages of the present invention will be described in the subsequent specification, and part of them will become obvious from the specification or be understood by implementing the present invention. The objectives and other advantages of the present invention can be achieved and obtained through the structures specifically pointed out in the written specification and the drawings.

[0069] The technical solution of the present invention will be further described in detail below through the drawings and embodiments. Description of the Drawings

[0070] The accompanying drawings are used to provide a further understanding of the present invention and form a part of the specification. They are used in conjunction with the embodiments of the present invention to explain the present invention, but do not constitute a limitation to the present invention. In the accompanying drawings:

[0071] Figure 1 It is a schematic diagram of a software development and function detection system based on artificial intelligence in an embodiment of the present invention;

[0072] Figure 2 It is a schematic diagram of a software development and function detection method based on artificial intelligence in an embodiment of the present invention. Detailed implementation manners

[0073] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to explain and illustrate the present invention, and are not used to limit the present invention.

[0074] An embodiment of the present invention provides a software development and function detection system based on artificial intelligence, as Figure 1 shown, including:

[0075] An acquisition module 1, configured to acquire the user's work history when the user performs software development or function detection work;

[0076] A determination module 2, configured to determine work assistance content based on an artificial intelligence model according to the work history;

[0077] A first assistance module 3, configured to assist the user based on the work assistance content.

[0078] In the above technical solution, the user is a software developer or a function detector; the work history at least includes: the work records of the user's software development or function detection work in the past, such as: processed technical documents, designed code, software design function requirements, test cases used for software function detection, etc.; the artificial intelligence model is a model obtained by training a neural network with a large amount of software development and function detection experience, which can, according to the work history, personalized determine the work assistance content that helps the user continue with software development or function detection work, such as: if the work content reflects that the user needs to test a certain function of the software, the work assistance content is the test case for completing the test; finally, based on the work assistance content, assist the user.

[0079] In this application, when the user performs software development or function detection work, based on the artificial intelligence model, according to the user's work history, determine the work assistance content, and based on the work assistance content, assist the user, providing targeted assistance to the user, greatly improving the efficiency of the user's software development or function detection work, reducing the probability of human errors, and avoiding time waste.

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

[0081] Predicting the user's future work content;

[0082] When the most recent generation time of the work content is not more than a time threshold from the current time and the prediction confidence of the work content exceeds a confidence threshold, obtaining the occurrence area sequence of the work content on the user's work interface;

[0083] Determining a first target area related to the work assistance content from the occurrence area sequence;

[0084] Determining a second target area in which the first target area exists within a preset sequence range before and after from the occurrence area sequence;

[0085] Performing feature extraction on the work assistance content, the first target area, and the second target area to obtain a first feature value set;

[0086] Querying a display layout policy library based on the first feature value set to determine a display layout policy;

[0087] Based on the display layout policy, displaying and laying out the work assistance content in the first target area and the second target area.

[0088] In the above technical solution, the work content at least includes: work records that may be generated when the user continues to perform software development or function detection work in the future; when making a prediction, it can be combined with a large number of work histories of the user's software development or function detection work in the past and the work history of the user's current software development or function detection work for prediction. This work history will reflect their work habits, so it can be combined for prediction; the most recent generation time of the work content refers to the time when the user will continue to perform software development or function detection work in the future for the last time; 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 during the prediction, and can be the number of times when the work history of the user's current software development or function detection work and the predicted work content are generated simultaneously in the past; the confidence level threshold can be 2; when the most recent generation time is no more than the time threshold from the current time and the prediction confidence level of the work content exceeds the confidence level threshold, it indicates that the pre-display layout of the work content can be carried out; the user's work interface refers to the computer interface when the user performs software development or function detection work; the occurrence area sequence includes the operation areas where the user successively operates on the work interface when generating the work content; being related to the work assistance content means that if the work assistance content is adopted by the user, its execution area is in the occurrence area, etc.; if it is related 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, within 3 occurrence areas; if there are first target areas both before and after a certain occurrence area within the preset sequence range in the occurrence area sequence, it indicates that for ensuring 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 at least include: the type of the work assistance content, the area type and area position of the first target area, the area type and area position of the second target area, etc.; there are display layout strategies corresponding to different first feature sets in the display layout strategy library. The first feature value set reflects the situation where the display layout of the work assistance content is required, and the display layout strategy indicates how to perform the display layout of the work assistance content in this situation; finally, based on the display layout strategy, the work assistance content is displayed and laid out in the first target area and the second target area; when the user continues to perform software development or function detection work, they can see the work assistance content with the pre-display layout, and can quickly obtain the work assistance in combination with the area where it is displayed and laid out.

[0089] In an embodiment of the present invention, a time threshold and a confidence threshold are used to accurately determine the timing for pre-display layout of work content, improving the accuracy of pre-display layout and reducing the working resources of the system; the first target area and the second target area are determined from the occurrence area sequence and used as the basis for display layout, further improving the accuracy of pre-display layout and enhancing the suitability of pre-display layout; the first eigenvalue set and the actual layout strategy library are introduced to quickly determine the display layout strategy, improving the efficiency of pre-display layout.

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

[0091] Whenever the user continues with software development or function detection work, obtain the user's current work operation and the corresponding operation area.

[0092] When there is a corresponding auxiliary item in the work assistance content for the work operation and the deviation degree of the work operation from the auxiliary item exceeds the deviation degree threshold, set the auxiliary item and all the first target content before the logic of the auxiliary item in the work assistance content in the operation area; where the deviation degree threshold decreases as the cumulative duration of the user's continued software development or function detection work increases.

[0093] When there is a corresponding auxiliary item in the work assistance content for the work operation and the deviation degree of the work operation from the auxiliary item does not exceed the deviation degree threshold, query the logic range library based on the difference between the deviation degree and the deviation degree threshold to determine the logic range.

[0094] Set the auxiliary item and the second target content within the logic range after the logic of the auxiliary item in the work assistance content in the operation area.

[0095] In the above technical solution, the current work operation of the user refers to the operation when the user continues to perform software development or function detection work, and the corresponding operation area is the area where the user generates the work operation, which is located on the work interface; the corresponding auxiliary item in the work auxiliary content refers to the content item that can assist the user to generate the work operation, and the deviation degree of the work operation from the auxiliary item refers to the difference degree between the work operation and the operation indicated by the auxiliary item. The deviation degree threshold can be, for example, 4; when there is a corresponding auxiliary item in the work auxiliary content for the work operation and the deviation degree of the work operation from the auxiliary item exceeds the deviation degree threshold, it indicates that the user needs to be assisted to restart the work at this time. Then, the auxiliary item and all the first target content before the logic of the auxiliary item in the work auxiliary content are set in the operation area. There is an execution logic sequence for each content in the work auxiliary content, and before the logic is the content arranged before the auxiliary item according to this execution logic sequence; however, when there is a corresponding auxiliary item in the work auxiliary content for the work operation and the deviation degree of the work operation from the auxiliary item does not exceed the deviation degree threshold, it indicates that the future work of the user can be indicated and assisted. The larger the difference between the deviation degree and the deviation degree threshold, the greater the degree of the work operation fitting the auxiliary item, the greater the degree of assistance available, and the larger the logic range. The logic range can be the number of contents in the work auxiliary content, for example: 3; the auxiliary item and the second target content within the logic range after the logic of the auxiliary item in the work auxiliary content are set in the operation area.

[0096] The technical solution of the embodiment of the present invention is different from that of the previous embodiment. When the user continues to perform software development or function detection work, the auxiliary content set in the operation area is accurately determined in combination with the deviation degree, deviation degree threshold, etc., and is correspondingly set in the operation area, realizing accurate and real-time assistance to the user, and greatly improving the user assistance effect.

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

[0098] A representation module, configured to, when the user refuses work assistance, perform a feature map representation on the work auxiliary content based on the feature map representation template to obtain a content feature map;

[0099] A fine-tuning module, configured to assist the user in quickly fine-tuning the work auxiliary content based on the content feature map;

[0100] A second auxiliary module, configured to perform work assistance on the user again based on the fine-tuned work auxiliary content.

[0101] In the above technical solution, the feature map representation template is a feature map template preset by technicians, which can be the data structure of data of different data types, etc.; based on the feature map representation template, the working assistance content can be represented by a feature map to obtain a content feature map; the content feature map reflects the feature situation of the working assistance content. Therefore, it can assist the user to quickly fine-tune the working assistance content based on it to meet the actual requirements of the user; based on the fine-tuned working assistance content, the user is assisted again. Generally, if the working assistance content does not meet the user's requirements, the user will reject the assistance. At this time, the artificial intelligence model needs to re-determine the new working assistance content based on the new working history generated by the user later, which is rather cumbersome and results in the user not being able to obtain effective assistance at the current stage. The embodiment of the present invention can solve this problem, represent the working assistance content by a feature map to obtain a content feature map, assist the user to quickly fine-tune the working assistance content based on the content feature map, and assist the user again based on the fine-tuned working assistance content, greatly improving the assistance efficiency and assistance effect, enhancing the applicability of the system, and being more user-friendly and intelligent.

[0102] In one embodiment, the fine-tuning module assists the user to quickly fine-tune the working assistance content based on the content feature map, including:

[0103] Determine the graph area of the target content from the content feature map; wherein, the target content is the local content in the working assistance content whose expected value expected by the artificial intelligence model does not exceed the expected value threshold or the effect value of the actual effect of assisting the user does not exceed the effect value threshold;

[0104] Extract features from the graph area to obtain a second feature value set;

[0105] Based on the second feature value set, query the sorting value library to determine the sorting value;

[0106] Guide the user to view the corresponding graph areas in the content feature map in descending order of the sorting value;

[0107] Whenever the user stays and views the graph area under guidance for more than the duration threshold, take the corresponding graph area as the first target graph area and start recording the viewing trajectory of the user viewing the content feature map;

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

[0109] When the user views the first target graph area again, keep the content slices in the standard positional relationship with the first target graph area and continuously output and display them to the user;

[0110] Randomly output and display to the user the quick selection table of replacement assistance requirements for the target content corresponding to the first target map area;

[0111] When the user selects a replacement assistance requirement from the quick selection table of replacement assistance requirements, based on the artificial intelligence model, determine the replacement content according to the target content corresponding to the first target map area and the replacement assistance requirement;

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

[0113] In the above technical solution, the expected value expected by the artificial intelligence model refers to the degree to which the artificial intelligence model expects the work assistance content to produce an auxiliary effect on the user; the expected value threshold can be, for example, 4; the effect value of the actual effect of assisting the user in work refers to the degree of the 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 in the work assistance content where the expected value expected by the artificial intelligence model does not exceed the expected value threshold or the effect value of the actual effect of assisting the user in work does not exceed the effect value threshold. Thus, the corresponding map area 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 eigenvalue set include the number of other map areas within a circular range with a radius of 8 cm around the map area, etc. There are sorting values corresponding to different second eigenvalue sets in the sorting value library. The greater the degree of need for the user to pay attention first reflected by the second eigenvalue set, the greater the corresponding sorting value. For example, the greater the number of other map areas within a circular range with a radius of 8 cm around the map area, the more convenient it is for the user to view other map areas when viewing this map area. Therefore, its corresponding sorting value is greater; guide the user to view the corresponding map areas in the content feature map in descending order of the sorting value; the duration threshold can be 30 seconds; the viewing trajectory is the trajectory formed by the landing point of the user's line of sight moving on the content feature map when viewing the content feature map; the viewing trajectory reflects the user's viewing situation; based on its 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, keep the content slices in a standard positional relationship with the first target map area and continuously output and display them to the user, so that the user can receive assistance from the best angle; there are multiple preset replacement assistance requirements generated for the target content corresponding to the first target map area in the quick selection table of replacement assistance requirements for the target content corresponding to the first target map area. Randomly output and display them to the user to facilitate the user's quick selection of replacement assistance requirements; then, based on the artificial intelligence model, determine the replacement content according to the target content corresponding to the first target map area and the replacement assistance requirement, and based on the replacement content, replace the target content corresponding to the first target map area in the work assistance content to achieve fine-tuning.

[0114] The embodiments of the present invention determine the graph area of ​​the target content, perform targeted feature extraction and sorting, and help users quickly identify the content area that needs attention, thereby efficiently optimizing the effect of work assistance content; according to the user's viewing trajectory and stay time, guide the user to gradually pay attention to important areas on the content feature map, to ensure that the fine-tuning process is orderly and meets the actual needs of the user; by randomly displaying a replacement auxiliary demand quick selection table and combining it with artificial intelligence model analysis, the most suitable replacement content is intelligently recommended to improve the adaptability and usage experience of the new work assistance content; through customized guidance and dynamic feedback, users can obtain more accurate and personalized work assistance in a shorter time, thereby enhancing work efficiency and experience.

[0115] In one embodiment, the planning of content slices and a standard position relationship between the content slices and the first target image area based on the viewing trajectory includes:

[0116] Determine, from the viewing trajectory, a continuous local trajectory that first exits the first target image region and then enters the first target image region; wherein the local trajectory does not pass through the first target image region;

[0117] Determine the trajectory point farthest from the first target image region from the local trajectory;

[0118] The local trajectory is split into two trajectory segments by using the trajectory point as a split point;

[0119] When the similarity between the two trajectory segments does not exceed the similarity threshold, obtaining a second target image region that the local trajectory passes through;

[0120] Extracting features from the first target image region and the second target image region to obtain a third feature set;

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

[0122] Based on the slice payload content search rule, searching for the slice payload content in the content feature graph;

[0123] Generate content slices based on slice templates and slice payload content;

[0124] Extract features from the last segment of the preset length of the viewing trajectory to obtain a fourth feature set;

[0125] Based on the fourth feature set, the standard position relationship library is queried to determine the standard position relationship.

[0126] In the above technical solution, the similarity threshold can be 80%; when the similarity between the two trajectory segments does not exceed the similarity threshold, it means that the user has not just viewed the second target image area and returned to the first target image area, which means that the second target image area is a new area of ​​interest for the user; feature extraction is performed on the first target image area and the second target image area, and the features in the third feature set obtained include the area type of the first target image area, the area type of the second target image area, etc., and there are slice load content search rules corresponding to different third feature sets in the slice load content search rule library, and the slice load content search rules indicate how to search for slice load content in the content feature map, and the slice load content is the content that needs to be displayed and loaded on the content slice, and the content plays a targeted auxiliary role for the user after viewing the second target image area; based on the slice load content search rules, the slice load content is searched in the content feature map, and the content slice is generated according to it; the final preset length can be 20 cm; the end segment reflects the user's last viewing situation, and the angle of the best presentation of the content slice can be determined based on it, and the user needs to view the content slice in comparison with the first target image area, so what is determined is the standard position relationship between the content slice and the first target image area; there are standard position relationships corresponding to different fourth feature sets in the standard position relationship library.

[0127] The embodiments of the present invention can identify changes in user interest in new focus areas through analysis of viewing trajectories, and provide content in a targeted manner to improve user experience; utilize feature extraction and search rule libraries to intelligently generate content slices that match user needs to ensure that the displayed content is targeted and practical; through the planning of standard position relationships, ensure that the display position of content slices is accurately relative to the user's current viewing area, optimize the visual presentation of content, and improve the smoothness of interaction; and can also automatically adjust the slice display method according to the user's viewing trajectory to adapt to the behavior patterns of different users and improve the accuracy of content recommendations.

[0128] The embodiment of the present invention provides a software development and function detection method based on artificial intelligence, such as Figure 2 As shown, including:

[0129] S1. When the user is doing software development or function testing, obtain the user's work history;

[0130] S2, based on the artificial intelligence model, determine the work assistance content according to the work history;

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

[0132] The work assistance for the user based on the work assistance content includes:

[0133] Predict the user’s future work content;

[0134] When the most recent generation time of the work content is not more than the time threshold from the current time and the predicted confidence level of the work content exceeds the confidence level threshold, obtain the sequence of occurrence regions of the work content on the user's work interface;

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

[0136] Determine the second target region where the first target region exists within the preset sequence range before and after from the sequence of occurrence regions;

[0137] Extract features from the work assistance content, the first target region, and the second target region to obtain the first set of feature values;

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

[0139] Based on the display layout strategy, display and layout the work assistance content in the first target region and the second target region.

[0140] The work assistance for the user based on the work assistance content includes:

[0141] Whenever the user continues to perform software development or function detection work, obtain the user's current work operation and the corresponding operation region;

[0142] When there is a corresponding assistance item for the work operation in the work assistance content and the deviation degree of the work operation from the assistance item exceeds the deviation degree threshold, set the assistance item and all the first target content before the logic of the assistance item in the work assistance content in the operation region; wherein, the deviation degree threshold decreases as the cumulative duration of the user's continued software development or function detection work increases;

[0143] When there is a corresponding assistance item for the work operation in the work assistance content and the deviation degree of the work operation from the assistance item does not exceed the deviation degree threshold, query the logic range library based on the difference between the deviation degree and the deviation degree threshold to determine the logic range;

[0144] Set the assistance item and the second target content within the logic range after the logic of the assistance item in the work assistance content in the operation region.

[0145] After the work assistance for the user based on the work assistance content, it further includes:

[0146] When the user rejects the work assistance, perform a feature map representation of the work assistance content based on the feature map representation template to obtain the content feature map;

[0147] Assist the user in quickly fine-tuning the work assistance content based on the content feature map;

[0148] Based on the fine-tuned work assistance content, re-provide work assistance to the user.

[0149] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. A software development and function detection system based on artificial intelligence, characterized in that: include: The acquisition module is used to obtain the user's work history when the user is doing software development or function testing; A determination module, used to determine the work assistance content based on the work history based on the artificial intelligence model; The first assistance module is used to provide work assistance to users based on work assistance content.

2. The artificial intelligence-based software development and function detection system according to claim 1, characterized in that: The first assistance module provides work assistance to the user based on the work assistance content, including: Predict the user’s future work content; When the most recent generation time of the work content does not exceed a time threshold from the current time and the prediction confidence of the work content exceeds a confidence threshold, obtaining an occurrence region sequence of the work content on the user's work interface; determining a first target area related to the work assistance content from the sequence of occurrence areas; Determine from the occurrence region sequence a second target region where both the first target region exists within the range of the preceding and following preset sequences; Extracting features of the work auxiliary content, the first target area, and the second target area to obtain a first feature value set; Based on the first feature value set, query the display layout strategy library to determine the display layout strategy; Based on the display layout strategy, the work auxiliary content is displayed and laid out in the first target area and the second target area.

3. The artificial intelligence-based software development and function detection system according to claim 1, characterized in that: The first assistance module provides work assistance to the user based on the work assistance content, including: Whenever the user continues to work on software development or function testing, the user's current work operation and the corresponding operation area are obtained; When a work operation has a corresponding auxiliary item in the work auxiliary content and the degree of deviation of the work operation from the auxiliary item exceeds a deviation threshold, the auxiliary item and all first target contents logically preceding the auxiliary item in the work auxiliary content are set in the operation area; wherein the deviation threshold decreases as the accumulated time for the user to continue the software development or function testing work increases; When the work operation has a corresponding auxiliary item in the work auxiliary content and the deviation degree of the work operation from the auxiliary item does not exceed the deviation degree threshold, based on the difference between the deviation degree and the deviation degree threshold, query the logic range library to determine the logic range; The auxiliary item and the second target content within the logical range after the auxiliary item in the work auxiliary content are set in the operation area.

4. The artificial intelligence-based software development and function detection system according to claim 1, characterized in that: After the first assistance module provides work assistance to the user based on the work assistance content, it also includes: A representation module, used for, when the user rejects the work assistance, performing feature graph representation on the work assistance content based on the feature graph representation template to obtain a content feature graph; A fine-tuning module is used to assist users in quickly fine-tuning work-assisting content based on content feature graphs; The second assistance module is used to provide work assistance to the user again based on the fine-tuned work assistance content.

5. The artificial intelligence-based software development and function detection system according to claim 4, characterized in that: The fine-tuning module assists the user to quickly fine-tune the work auxiliary content based on the content feature graph, including: Determine a target content region from the content feature graph; wherein the target content is a local content in the work assistance content whose expected value expected by the artificial intelligence model does not exceed the expected value threshold or whose effect value of the actual effect of work assistance to the user does not exceed the effect value threshold; Perform feature extraction on the image region to obtain a second eigenvalue set; Based on the second feature value set, query the ranking value library to determine the ranking value; According to the sorting values ​​from large to small, the user is guided to view the corresponding areas in the content feature graph; Whenever the user stays in the guided viewing area for more than a time threshold, the corresponding area is used as the first target area, and the viewing trajectory of the user viewing the content feature map is recorded; Planning content slices and a standard positional relationship between the content slices and the first target image region based on the viewing trajectory; When the user views the first target image area again, the content slice is kept in a standard position relationship with the first target image area and is continuously output and displayed to the user; Randomly outputting and displaying a replacement auxiliary demand quick selection list of target content corresponding to the first target image area to the user; When the user selects a replacement auxiliary requirement from the replacement auxiliary requirement quick selection table, the replacement content is determined based on the artificial intelligence model according to the target content corresponding to the first target image area and the replacement auxiliary requirement; Based on the replacement content, the target content corresponding to the first target image area in the work auxiliary content is replaced.

6. The artificial intelligence-based software development and function detection system according to claim 5, characterized in that: The planning of content slices and a standard position relationship between the content slices and the first target image area based on the viewing trajectory includes: Determine, from the viewing trajectory, a continuous local trajectory that first exits the first target image region and then enters the first target image region; wherein the local trajectory does not pass through the first target image region; Determine the trajectory point farthest from the first target image region from the local trajectory; The local trajectory is split into two trajectory segments by using the trajectory point as a split point; When the similarity between the two trajectory segments does not exceed the similarity threshold, obtaining a second target image region that the local trajectory passes through; Extracting features from the first target image region and the second target image region to obtain a third feature set; Based on the third feature set, query the slice load content search rule library to determine the slice load content search rule; Based on the slice payload content search rule, searching for the slice payload content in the content feature graph; Generate content slices based on slice templates and slice payload content; Extract features from the last segment of the preset length of the viewing trajectory to obtain a fourth feature set; Based on the fourth feature set, the standard position relationship library is queried to determine the standard position relationship.

7. A software development and function detection method based on artificial intelligence, characterized in that: include: When a user is developing software or testing functions, obtain the user's work history; Based on the AI ​​model, determine the work assistance content according to the work history; Provide work assistance to users based on the work assistance content.

8. The method for software development and function detection based on artificial intelligence as claimed in claim 7, characterized in that: The work assistance for the user based on the work assistance content includes: Predict the user’s future work content; When the most recent generation time of the work content does not exceed a time threshold from the current time and the prediction confidence of the work content exceeds a confidence threshold, obtaining an occurrence region sequence of the work content on the user's work interface; determining a first target area related to the work assistance content from the sequence of occurrence areas; Determine from the occurrence region sequence a second target region where both the first target region exists within the range of the preceding and following preset sequences; Extracting features of the work auxiliary content, the first target area, and the second target area to obtain a first feature value set; Based on the first feature value set, query the display layout strategy library to determine the display layout strategy; Based on the display layout strategy, the work auxiliary content is displayed and laid out in the first target area and the second target area.

9. The method for software development and function detection based on artificial intelligence as claimed in claim 7, characterized in that: The work assistance for the user based on the work assistance content includes: Whenever the user continues to work on software development or function testing, the user's current work operation and the corresponding operation area are obtained; When a work operation has a corresponding auxiliary item in the work auxiliary content and the degree of deviation of the work operation from the auxiliary item exceeds a deviation threshold, the auxiliary item and all first target contents logically preceding the auxiliary item in the work auxiliary content are set in the operation area; wherein the deviation threshold decreases as the accumulated time for the user to continue the software development or function testing work increases; When the work operation has a corresponding auxiliary item in the work auxiliary content and the deviation degree of the work operation from the auxiliary item does not exceed the deviation degree threshold, based on the difference between the deviation degree and the deviation degree threshold, query the logic range library to determine the logic range; The auxiliary item and the second target content within the logical range after the auxiliary item in the work auxiliary content are set in the operation area.

10. The method for software development and function detection based on artificial intelligence according to claim 7, characterized in that: After providing work assistance to the user based on the work assistance content, the method further includes: When the user rejects the work assistance, a feature graph is performed on the work assistance content based on the feature graph representation template to obtain a content feature graph; Assist users to quickly fine-tune work-assistance content based on content feature graphs; Based on the fine-tuned work assistance content, users are provided with work assistance again.

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