System repair method and device, electronic equipment, storage medium and program product

Abstract

The invention discloses a system repairing method and device, electronic equipment, a storage medium and a program product. The method comprises the following steps: in response to a defect repair instruction input by a first object for a to-be-repaired system through a remote debugging configuration system, obtaining transaction associated information corresponding to at least one target abnormal transaction of the to-be-repaired system within a first historical duration; determining at least one candidate defect repair patch corresponding to the target abnormal transaction according to transaction association information corresponding to the target abnormal transaction; and determining a defect repair patch corresponding to the target abnormal transaction according to the at least one candidate defect repair patch and a pre-constructed test case set, so as to repair a system defect corresponding to the target abnormal transaction based on the defect repair patch. According to the technical scheme, the effects of remotely triggering the repair process, automatically obtaining comprehensive abnormal transaction information and directionally generating and verifying the repair patch are achieved, and the communication cost of cross-regional system faults is reduced.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and in particular to a system repair method, apparatus, electronic device, storage medium, and program product. Background Technology

[0002] As the financial business continues to expand, branches are gradually being established around the world to provide business services. The time zones of the various branches differ, and the systems used by the branches are generally developed and maintained by the head office. During the development, testing, and acceptance of business requirements, projects typically require the collaborative efforts of multiple teams from both the head office and branches, and these teams often cannot communicate face-to-face due to geographical limitations.

[0003] In related technologies, when a branch's business system encounters a problem, the head office developers typically provide remote guidance to the relevant personnel at the branch to take screenshots of the issue. The screenshots and communication are then used to reproduce the steps in the head office's business system to help locate the problem. This troubleshooting method may suffer from high communication costs, low efficiency, incomplete fault information, low repair efficiency, and even misdiagnosis or misjudgment. Summary of the Invention

[0004] This invention provides a system repair method, apparatus, electronic device, storage medium, and program product to achieve the effects of remotely triggering the repair process, automatically acquiring comprehensive abnormal transaction information, and generating and verifying repair patches, thereby effectively reducing the communication costs of cross-regional system failures.

[0005] According to one aspect of the present invention, a system repair method is provided, the method comprising:

[0006] In response to a defect repair instruction input by a first object to the system to be repaired via a remote debugging and configuration system, transaction association information corresponding to at least one target abnormal transaction of the system to be repaired within a first historical time period prior to the current moment is obtained; wherein, the target abnormal transaction corresponds to a system defect in the system to be repaired; the transaction association information includes at least transaction details and the cause of the abnormality; the remote debugging and configuration system and the system to be repaired are located in different geographical regions;

[0007] For at least one of the target abnormal transactions, at least one candidate defect repair patch corresponding to the target abnormal transaction is determined based on the transaction association information corresponding to the target abnormal transaction;

[0008] Based on at least one of the candidate defect repair patches and a pre-built test case set, a defect repair patch corresponding to the target abnormal transaction is determined, so as to repair the system defect corresponding to the target abnormal transaction based on the defect repair patch.

[0009] According to another aspect of the present invention, a system repair apparatus is provided, the apparatus comprising:

[0010] The transaction information acquisition module is used to respond to a defect repair instruction input by a first object to the system to be repaired through a remote debugging and configuration system, and to acquire transaction association information corresponding to at least one target abnormal transaction of the system to be repaired within a first historical time period before the current moment; wherein, the target abnormal transaction corresponds to a system defect in the system to be repaired; the transaction association information includes at least transaction details and the cause of the abnormality; the remote debugging and configuration system and the system to be repaired are located in different geographical regions;

[0011] The candidate patch determination module is used to determine, for at least one target abnormal transaction, at least one candidate defect repair patch corresponding to the target abnormal transaction based on the transaction association information corresponding to the target abnormal transaction;

[0012] The defect repair module is used to determine the defect repair patch corresponding to the target abnormal transaction based on at least one of the candidate defect repair patches and a pre-built test case set, so as to repair the system defect corresponding to the target abnormal transaction based on the defect repair patch.

[0013] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0014] At least one processor; and

[0015] A memory communicatively connected to the at least one processor; wherein,

[0016] The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the system repair method according to any embodiment of the present invention.

[0017] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the system repair method described in any embodiment of the present invention.

[0018] According to another aspect of the present invention, a computer program product is provided, the computer program product comprising a computer program that, when executed by a processor, implements the system repair method described in any embodiment of the present invention.

[0019] The technical solution of this invention, in response to a defect repair command input by a first object through a remote debugging and configuration system for the system to be repaired, obtains transaction association information corresponding to at least one target abnormal transaction within a first historical time period prior to the current moment in the system to be repaired. This supports the first object remotely triggering the defect repair process across regions, accurately obtaining core information (transaction details and reasons for abnormality) of recent target abnormal transactions in the system to be repaired without on-site operation. This quickly identifies the actual scenario and cause of the system defect association, breaking geographical limitations and improving defect response efficiency, laying a data foundation for subsequent targeted repairs. Furthermore, by identifying at least one candidate defect repair patch corresponding to the target abnormal transaction based on the transaction association information, it ensures that each candidate defect repair patch focuses on resolving a specific defect, reducing the number of invalid repair patches, lowering the cost of trial and error in repair, shortening the time from defect discovery to candidate solution output, and improving the initial efficiency of the repair process. Furthermore, by determining the defect repair patch corresponding to the target abnormal transaction based on at least one candidate defect repair patch and a pre-built test case set, the system defect corresponding to the target abnormal transaction is repaired based on the defect repair patch, ensuring the effectiveness and security of the defect repair patch and improving its accuracy. The technical solution of this invention addresses the problems of high communication costs, low communication efficiency, incomplete fault information, low repair efficiency, and even misdiagnosis and misjudgment in related system fault handling methods. By remotely triggering the repair process, automatically acquiring comprehensive abnormal transaction information, and generating and verifying repair patches in a targeted manner, it effectively reduces the communication costs of cross-regional system faults, improves communication and repair efficiency, ensures complete and accurate fault information, reduces misdiagnosis and misjudgment, and achieves efficient, accurate, and remote repair of system defects.

[0020] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart of a system repair method provided according to an embodiment of the present invention;

[0023] Figure 2This is a flowchart of a system repair method provided according to an embodiment of the present invention;

[0024] Figure 3 This is a flowchart of a system repair method provided according to an embodiment of the present invention;

[0025] Figure 4 This is a schematic diagram of the structure of a system repair device according to an embodiment of the present invention;

[0026] Figure 5 This is a schematic diagram of the structure of an electronic device that implements the system repair method of the present invention. Detailed Implementation

[0027] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0028] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0029] Figure 1 This is a flowchart of a system repair method provided by an embodiment of the present invention. This embodiment is applicable to the repair of system defects. The method can be executed by a system repair device, which can be implemented in hardware and / or software, and can be configured in a terminal and / or server. Figure 1 As shown, the method includes:

[0030] S110. In response to the defect repair instruction input by the first object to the system to be repaired through the remote debugging configuration system, obtain the transaction association information corresponding to at least one target abnormal transaction in the system to be repaired within the first historical time period before the current moment.

[0031] The first entity can be the one initiating the defect repair action and possessing operational and maintenance permissions for the system to be repaired. For example, if the system to be repaired is a transaction system used by a branch of a bank, the first entity could be the bank's head office developers, operations and maintenance personnel, testers, or other relevant roles. The remote debugging and configuration system can be a tool or platform accessible remotely via a network, capable of cross-regional data interaction, command transmission, and configuration debugging with the system to be repaired. The core function of the remote debugging and configuration system is to allow the first entity to initiate defect repair operations without being physically present in the system to be repaired. The system to be repaired can be a transaction system, software platform, application, or service that has functional abnormalities, vulnerabilities, or defects and needs repair to restore normal operation. For example, an e-commerce platform's resource transfer system or a bank's transfer system with defects causing transaction abnormalities can be considered a system to be repaired. The remote debugging and configuration system and the system to be repaired are located in different geographical regions. That is, the remote debugging and configuration system and the system to be repaired are physically separated; for example, the remote debugging and configuration system is deployed in region A, and the system to be repaired is deployed in region B, reflecting the cross-regional nature of remote operation. A defect repair instruction can refer to an instruction issued by the first object to initiate a repair process in response to a defect or anomaly in the system to be repaired.

[0032] For example, suppose the system to be repaired is a transaction system used by a branch of a bank in region A. The first object can be a system developer at the bank's head office in region B. Before the first object inputs defect repair instructions for the transaction system to be repaired, the permissions of the first object can be authenticated through the remote debugging and configuration system on the head office server. Then, if the authentication is successful, the defect repair instructions can be input remotely for the transaction system to be repaired through the remote debugging and configuration system on the head office server.

[0033] The current moment refers to the specific time point at which the defect repair instruction is received or responded to. The first historical duration can refer to a specific time interval tracing back from the current moment, used to limit the time range for obtaining abnormal transaction data. Optionally, the first historical duration includes, but is not limited to, 1 day, 1 week, 1 month, and 3 months. For example, assuming the first historical duration is 1 week, it is necessary to obtain the transaction association information corresponding to the target abnormal transactions in the system to be repaired during the week prior to the current moment. The target abnormal transaction can refer to an abnormal transaction operation that occurred within the first historical duration and is directly related to the system defect in the system to be repaired. The target abnormal transaction corresponds to a system defect in the system to be repaired. That is, the abnormality of the target abnormal transaction is caused by a system defect in the system to be repaired. For example, assuming the system to be repaired is a transaction system, a transaction in the transaction system that results in "successful deduction but incomplete order" due to a code vulnerability is a target abnormal transaction. The system defect can refer to a problem or vulnerability existing in the system to be repaired itself, which is the root cause of the target abnormal transaction. This can manifest as code errors, design logic flaws, incorrect configuration parameters, algorithm defects, or compatibility issues. For example, a code flaw in a trading system that fails to validate "negative amount" inputs is considered a "system defect." Transaction-related information refers to core data related to the target abnormal transaction. This core data can be used to analyze the specific circumstances and root causes of the transaction anomaly. Transaction-related information can include multiple pieces of information associated with the transaction details of the target abnormal transaction, optionally including at least transaction details and the cause of the anomaly. Transaction details can be basic transaction data, such as transaction number, transaction time, transaction account, transaction resource amount, system modules involved in the transaction, transaction process nodes, and operation logs. The cause of the anomaly can be a specific description of the reason that led to the transaction anomaly. The cause of the target abnormal transaction can include multiple causes related to system defects, such as interface call failure, invalid data validation rules, code logic execution errors, and insufficient resource allocation.

[0034] In this embodiment, typically, when a fault is determined in any system, that system is designated as the system to be repaired. Then, the first object can input defect repair instructions for the system to be repaired. Furthermore, to quickly locate, accurately troubleshoot, and repair the problem, transaction association information of target abnormal transactions associated with the system defect within a first historical time period can be obtained for the system to be repaired. Subsequently, data analysis of the transaction association information can be performed to determine a system repair plan for the system to be repaired, thereby achieving the repair of the system defect.

[0035] It should be noted that the system to be repaired may have multiple anomalous transactions within the first historical period prior to the current moment. Among these anomalous transactions, those directly related to the system defect are usually those that can be used to repair the system. Therefore, in order to quickly locate and repair the system defect, target anomalous transactions associated with the system defect can be selected from the multiple anomalous transactions.

[0036] Based on this, before obtaining the transaction association information corresponding to at least one target abnormal transaction in the first historical period before the current time of the system to be repaired, the method further includes: obtaining at least one abnormal transaction in the first historical period before the current time of the system to be repaired; filtering at least one abnormal transaction according to a preset abnormal reason field, and taking the abnormal transaction that matches the abnormal reason field as the target abnormal transaction.

[0037] Abnormal transactions refer to transactions that deviate from normal processes or expected results in a system under repair. The causes of abnormal transactions can include various factors such as system defects, operational errors, and external interference. Anomaly cause fields can be predefined feature fields or keyword sets used to identify anomalies caused by system defects. Anomaly cause fields can characterize at least one system defect. In other words, based on the anomaly cause fields, abnormal transactions whose cause is a system defect can be filtered out. Anomaly cause fields typically include fields based on historical system defect cases, common fault types, or technical specifications, and / or may also include anomaly cause fields manually set by system developers. Anomaly cause fields can be used to precisely locate anomalies related to defects within the system itself. For example, anomaly cause fields may include "code logic error," "interface call failure," "data validation rule failure," and "permission configuration error," etc.

[0038] In specific implementation, upon receiving a defect repair instruction from the first object via the remote debugging and configuration system for the system to be repaired, in response to the defect repair instruction, at least one abnormal transaction of the system to be repaired within a first historical period prior to the current moment is retrieved from the system operation log associated with the system to be repaired. Further, for each abnormal transaction, the cause of the abnormality can be obtained, and a field matching operation is performed based on a preset cause of abnormality field. Further, if the cause of abnormality matches any field in the cause of abnormality field, the abnormal transaction can be designated as the target abnormal transaction. Thus, at least one target abnormal transaction of the system to be repaired within the first historical period prior to the current moment can be obtained, and the transaction association information corresponding to the at least one target abnormal transaction can be acquired.

[0039] S120. For at least one target abnormal transaction, determine at least one candidate defect repair patch corresponding to the target abnormal transaction based on the transaction association information corresponding to the target abnormal transaction.

[0040] Here, a candidate defect repair patch can refer to a potential repair solution initially identified for a system defect corresponding to a target abnormal transaction. Multiple repair solutions may exist for the system defect corresponding to the target abnormal transaction; therefore, at least one candidate defect repair patch will be generated. Subsequently, an effective defect repair patch can be determined from at least one candidate defect repair patch. Optionally, the candidate defect repair patch may take the form of at least one of the following: code repair snippets, configuration file adjustments, logic rule optimization scripts, algorithm parameter corrections, etc.

[0041] In this embodiment, the acquired target abnormal transactions all correspond to system defects in the system to be repaired. Furthermore, in order to repair the system defects in the system to be repaired, the transaction association information corresponding to each target abnormal transaction can be analyzed, and at least one candidate defect repair patch applicable to the system defect corresponding to each target abnormal transaction can be determined.

[0042] In this embodiment, the determination of at least one candidate defect repair patch corresponding to the target abnormal transaction may include at least one of the following methods: processing the transaction association information through a pre-trained defect repair model and outputting at least one candidate defect repair patch corresponding to the target abnormal transaction; querying the associated knowledge base through a patch query model and determining at least one candidate defect repair patch corresponding to the target abnormal transaction based on the query results; or determining at least one candidate defect repair patch corresponding to the target abnormal transaction based on a pre-built rule base. These determination methods are described below.

[0043] Optionally, based on the transaction association information corresponding to the target abnormal transaction, at least one candidate defect repair patch corresponding to the target abnormal transaction is determined, including: inputting the transaction association information corresponding to the target abnormal transaction into a pre-trained defect repair model, and obtaining at least one candidate defect repair patch corresponding to the target abnormal transaction.

[0044] The defect repair model is trained on a machine learning model based on the transaction association information corresponding to sample abnormal transactions and the actual defect repair patches corresponding to those transactions. The defect repair model can be a machine learning model used to generate system defect repair solutions. It automatically outputs candidate defect repair patches that may be effective for the system defects corresponding to the abnormal transactions based on the transaction association information. Sample abnormal transactions can refer to historical abnormal transaction cases used to train the defect repair model. These transactions can be abnormal transactions that have occurred in the past and have been repaired. The transaction association information corresponding to the sample abnormal transaction can refer to the detailed data of the sample abnormal transaction, including the transaction details and the cause of the abnormality. This information can be consistent with the transaction association information structure of the target abnormal transaction and serve as input samples for model training. The actual defect repair patch can refer to a repair solution that has been verified as effective for the system defects corresponding to the sample abnormal transaction. The actual defect repair patch can be the target output sample for model training.

[0045] It should be noted that the model structure of the defect repair model can be a neural network model that includes an encoder and a decoder, such as a convolutional neural network, a recurrent neural network, a Transformer, a long short-term memory network, etc.

[0046] In some embodiments, the training process of the defect repair model can be as follows: First, a defect dataset is obtained from an open-source dataset, which includes transaction association information and actual defect repair patches corresponding to multiple abnormal sample transactions. Further, the defect dataset is preprocessed by lexicalizing the data to facilitate input into the model. Further, the transaction association information from the defect dataset can be input into the model to be trained to obtain the model output repair patch corresponding to the abnormal sample transaction. Further, based on the actual defect repair patch and the model output repair patch of the abnormal sample transaction, the loss function can be determined, and the model parameters in the model to be trained can be corrected based on the loss value. The convergence of the loss function in the model to be trained is taken as the training objective to obtain the defect repair model.

[0047] In practice, after obtaining the transaction association information corresponding to at least one target abnormal transaction within the first historical time period before the current moment of the system to be repaired, the transaction association information corresponding to the target abnormal transaction can be input into the pre-trained defect repair model for the target abnormal transaction. The defect repair model processes the transaction association information and outputs at least one candidate defect repair patch corresponding to the target abnormal transaction.

[0048] Optionally, based on the transaction association information corresponding to the target abnormal transaction, at least one candidate defect repair patch corresponding to the target abnormal transaction is determined, including: querying the associated knowledge base based on the transaction association information using a patch query model, and determining at least one candidate defect repair patch corresponding to the target abnormal transaction based on the query results.

[0049] The patch query model is a model used for retrieving and matching information. It can accurately find relevant content in a pre-defined knowledge base based on input transaction-related information. The patch query model understands the semantics of the transaction-related information and locates matching remediation solutions from the knowledge base. The knowledge base can be a structured or unstructured database storing knowledge, rules, and solutions related to business scenarios (such as transaction systems and business processes). The knowledge base may include remediation cases of historical abnormal transactions, patch solutions for system defects, business rule documents, technical manuals, common problems and solutions, etc. The query results are the matching content obtained by the patch query model after retrieving the transaction-related information from the knowledge base; these are typically remediation solutions, historical cases, or patch records related to the target abnormal transaction.

[0050] In practical implementation, for at least one target abnormal transaction, the transaction association information corresponding to the target abnormal transaction can be input into the patch query model. Then, the patch query model can query the associated knowledge base based on the transaction association information to obtain query results matching the target abnormal transaction. Furthermore, the query results can be input into the information processing model for integrated analysis to obtain at least one candidate defect repair patch corresponding to the target abnormal transaction.

[0051] Optionally, based on the transaction association information corresponding to the target abnormal transaction, at least one candidate defect repair patch corresponding to the target abnormal transaction is determined, including: extracting features from the transaction association information corresponding to the target abnormal transaction to obtain abnormal transaction features corresponding to the target abnormal transaction; matching the abnormal transaction features through a pre-built rule base, and determining at least one candidate defect repair patch corresponding to the target abnormal transaction based on the matching results.

[0052] Among them, abnormal transaction features can be a structured abstract description of the core attributes, abnormal behavior, and key causes of a target abnormal transaction. They are key information extracted from complex transaction-related information that accurately identifies "why the transaction is abnormal" and "what the cause of the abnormality is." Optionally, abnormal transaction features include at least one of the following: abnormal behavior features, scenario and environment features, and abnormal cause-related features. Abnormal behavior features can be used to describe the external manifestations of a transaction deviating from its normal state, such as abnormal transaction results, abnormal processes, and abnormal data. Scenario and environment features can be used to identify the specific scenario and system environment in which the abnormal transaction occurs, such as the business modules involved in the abnormal transaction (e.g., payment module, inventory module, user authentication module), transaction type, time and frequency characteristics, etc. Abnormal cause-related features can be key clues pointing to the root cause of the transaction abnormality, such as error type (e.g., null pointer exception, database connection timeout, permission verification failure, etc.), triggering conditions, etc. The rule base can refer to a pre-built structured database that stores the correspondence between abnormal features and remediation solutions.

[0053] It should be noted that transaction-related information may be fragmented and unstructured, making information matching difficult. However, abnormal transaction features, through feature extraction, transform transaction-related information into structured, matchable key indicators, thereby facilitating information matching based on abnormal transaction features.

[0054] In practical implementation, for at least one target abnormal transaction, features can be extracted from the transaction association information corresponding to the target abnormal transaction to obtain abnormal transaction features. Furthermore, the abnormal transaction features can be matched with information stored in a pre-built rule base. Further, if the abnormal transaction features match any abnormal feature in the rule base, the corresponding repair scheme in the rule base can be directly invoked as a candidate defect repair patch for the target abnormal transaction.

[0055] S130. Based on at least one candidate defect repair patch and a pre-built test case set, determine the defect repair patch corresponding to the target abnormal transaction, so as to repair the system defect corresponding to the target abnormal transaction based on the defect repair patch.

[0056] The test case set can be a pre-defined and stored set of standardized test scenarios and steps used to verify the effectiveness of the repair solution. The core function of the test case set is to simulate real-world scenarios and verify whether the patch can solve the problem or introduce new problems. Optionally, the test case set may include at least one of the following: basic test cases, abnormal scenario test cases, and boundary test cases. The test case set may include multiple test cases, which are typically pre-defined based on historical defect cases, system requirements documents, or industry testing standards. It can be understood that basic test cases can cover the normal transaction process, verifying that the patch does not affect the original functionality of the system. Abnormal scenario test cases can reproduce the triggering conditions of the target abnormal transaction, verifying whether the patch can fix the anomaly. Boundary test cases can simulate extreme scenarios (such as high-frequency requests or large-volume transactions), verifying the stability and compatibility of the patch. A defect repair patch can refer to a truly effective repair solution for the system defect corresponding to the target abnormal transaction, selected from candidate defect repair patches through testing. In this embodiment, the defect repair patch is determined based on at least one candidate defect repair patch. That is, the defect repair patch can be any one of the at least one candidate defect repair patches, or it can be a defect repair patch generated by performing data analysis on at least one candidate defect repair patch.

[0057] In this embodiment, for at least one target abnormal transaction, after obtaining at least one candidate defect repair patch corresponding to the target abnormal transaction, in order to determine the effective defect repair patch for the system defect corresponding to the target abnormal transaction based on the at least one candidate defect repair patch, a pre-built test case set can be used to test the at least one candidate defect repair patch. Then, the defect repair patch corresponding to the target abnormal transaction can be determined based on the test results.

[0058] In practical implementation, for at least one candidate defect fix patch, the patch can be applied to the test environment, and a pre-built test case set can be invoked to verify it. The results will record whether the patch resolves the target abnormal transaction, whether it introduces new problems, and the patch's compatibility and stability. This will yield at least one test result. Further, these test results can be filtered to exclude invalid or problematic candidate patches, selecting those that fix the system defect without affecting other functions as the final defect fix patch. Finally, the defect fix patch can be applied to the system to be repaired to fix the system defect corresponding to the target abnormal transaction.

[0059] It should be noted that in some embodiments, there may be a situation where at least one candidate defect repair patch fails the test. In this case, at least one candidate defect repair patch can be processed according to a preset patch processing method to obtain an effective defect repair patch. Optionally, the patch processing method may include at least one of iterative optimization algorithms, candidate defect repair patch combination and fusion, and candidate defect repair patch mutation and fine-tuning.

[0060] The technical solution of this invention, in response to a defect repair command input by a first object through a remote debugging and configuration system for the system to be repaired, obtains transaction association information corresponding to at least one target abnormal transaction within a first historical time period prior to the current moment in the system to be repaired. This supports the first object remotely triggering the defect repair process across regions, accurately obtaining core information (transaction details and reasons for abnormality) of recent target abnormal transactions in the system to be repaired without on-site operation. This quickly identifies the actual scenario and cause of the system defect association, breaking geographical limitations and improving defect response efficiency, laying a data foundation for subsequent targeted repairs. Furthermore, by identifying at least one candidate defect repair patch corresponding to the target abnormal transaction based on the transaction association information, it ensures that each candidate defect repair patch focuses on resolving a specific defect, reducing the number of invalid repair patches, lowering the cost of trial and error in repair, shortening the time from defect discovery to candidate solution output, and improving the initial efficiency of the repair process. Furthermore, by determining the defect repair patch corresponding to the target abnormal transaction based on at least one candidate defect repair patch and a pre-built test case set, the system defect corresponding to the target abnormal transaction is repaired based on the defect repair patch, ensuring the effectiveness and security of the defect repair patch and improving its accuracy. The technical solution of this invention addresses the problems of high communication costs, low communication efficiency, incomplete fault information, low repair efficiency, and even misdiagnosis and misjudgment in related system fault handling methods. By remotely triggering the repair process, automatically acquiring comprehensive abnormal transaction information, and generating and verifying repair patches in a targeted manner, it effectively reduces the communication costs of cross-regional system faults, improves communication and repair efficiency, ensures complete and accurate fault information, reduces misdiagnosis and misjudgment, and achieves efficient, accurate, and remote repair of system defects.

[0061] Figure 2 This is a flowchart of a system repair method provided by an embodiment of the present invention. Based on the foregoing embodiments, the method for determining the defect repair patch corresponding to the target abnormal transaction is further refined. For specific implementation details, please refer to the technical solution of this embodiment. Technical terms that are the same as or similar to those in the above embodiments will not be repeated here. Figure 2 As shown, the method includes:

[0062] S210. In response to the defect repair instruction input by the first object to the system to be repaired through the remote debugging configuration system, obtain transaction association information corresponding to at least one target abnormal transaction of the system to be repaired within a first historical time period before the current moment; wherein, the transaction association information also includes system fault information.

[0063] S220. For at least one target abnormal transaction, determine at least one candidate defect repair patch corresponding to the target abnormal transaction based on the transaction association information corresponding to the target abnormal transaction.

[0064] S230. Based on the system fault information, determine the test cases corresponding to the target abnormal transaction from the pre-built test case set.

[0065] System fault information refers to the details of faults generated by the system when processing the target abnormal transaction, such as error logs (e.g., database connection timeout, parameter validation failure), exception stack information, function error messages, performance bottleneck data, etc. System fault information can be used to locate the position and type of system defects. In this embodiment, the test case set can be used to characterize the correspondence between fault information and test cases; that is, the test case set can store at least one fault piece of information and its corresponding test case. The test cases corresponding to the target abnormal transaction can be selected from the test case set and are specifically used to simulate or reproduce the target abnormal transaction scenario. The selection criteria for test cases are system fault information, ensuring that the test cases accurately match the defect scenario.

[0066] In practical implementation, for at least one target abnormal transaction, system fault information corresponding to the target abnormal transaction can be obtained from the transaction association information corresponding to the target abnormal transaction. Furthermore, the system fault information can be matched with at least one fault information stored in the test case set. Further, if the matching degree between the system fault information and any fault information in the test case set is the highest value among at least one matching degree values, the test case in the test case set corresponding to that fault information can be used as the test case corresponding to the target abnormal transaction.

[0067] S240. For at least one candidate defect fix patch, process the test cases according to the candidate defect fix patch to obtain the test results corresponding to the candidate defect fix patch.

[0068] The test result refers to the verification result obtained by running the test cases corresponding to the target abnormal transaction after applying the candidate defect fix patch to the test environment. The test result can usually be divided into test pass (the patch fixes the defect and the test case executes without exception) or test fail (the patch does not fix the defect and / or introduces a new problem).

[0069] In practice, for at least one candidate defect fix patch, it can be applied to the test environment system to ensure its correct effectiveness. Furthermore, test cases corresponding to the target abnormal transaction are run, and the execution status of the test cases after applying the candidate defect fix patch is recorded. Further, after the test cases have completed running, the test results corresponding to the candidate defect fix patch can be obtained. Then, the defect fix patch corresponding to the target abnormal transaction can be determined based on the test results.

[0070] S250. Based on at least one test result, determine the defect repair patch corresponding to the target abnormal transaction, and repair the system defect corresponding to the target abnormal transaction based on the defect repair patch.

[0071] In this embodiment, the test results are divided into test pass and test fail. Therefore, based on at least one test result, determining the defect repair patch corresponding to the target abnormal transaction can correspond to two cases: at least one test result includes a test pass result; at least one test result does not include a test pass result. These two cases are described below.

[0072] Optionally, based on at least one test result, a defect repair patch corresponding to the target abnormal transaction is determined, including: if there is a test result that passes in at least one test result, the defect repair patch corresponding to the target abnormal transaction is determined based on the candidate defect repair patches that pass the test.

[0073] In this embodiment, at least one test result may include at least one test result that passed. If there is only one test result that passed, the candidate defect repair patch corresponding to that test result can be directly used as the defect repair patch corresponding to the target abnormal transaction. If there are multiple test results that passed, determining the defect repair patch corresponding to the target abnormal transaction may include at least one of the following methods: directly using the candidate defect repair patches corresponding to multiple test results that passed as the defect repair patch corresponding to the target abnormal transaction; or integrating and analyzing the candidate defect repair patches corresponding to multiple test results that passed, and using the integrated result as the defect repair patch corresponding to the target abnormal transaction.

[0074] In some embodiments, if at least one test result is a pass, the candidate defect repair patch corresponding to that test result can be used as the defect repair patch corresponding to the target abnormal transaction.

[0075] In other embodiments, if multiple test results pass in at least one test, all candidate defect repair patches corresponding to these multiple test results can be used as defect repair patches corresponding to the target abnormal transaction. Then, any one of these defect repair patches can be selected to repair the system defect in the system to be repaired.

[0076] In other embodiments, if multiple test results pass in at least one test result, the candidate defect repair patches corresponding to these multiple test results can be input into the information processing model. The information processing model can then integrate and analyze these multiple candidate defect repair patches, and the integrated repair patch can be used as the defect repair patch corresponding to the target abnormal transaction.

[0077] Optionally, based on at least one test result, a defect repair patch corresponding to the target abnormal transaction is determined, including: if no test result is found to be passed in at least one test result, processing at least one candidate defect repair patch according to an iterative search algorithm to obtain a defect repair patch corresponding to the target abnormal transaction.

[0078] The iterative search algorithm can be used to derive a valid patch when all candidate defect repair patches fail the tests. The algorithm logic involves iterative optimization, continuously adjusting the repair logic of candidate defect repair patches based on the reasons for their failures and system fault information, and retesting until a valid patch that passes the tests is generated.

[0079] Optionally, at least one candidate defect repair patch is processed according to an iterative search algorithm to obtain a defect repair patch corresponding to the target abnormal transaction. This includes: constructing an initial population based on at least one candidate defect repair patch; wherein the candidate defect repair patch is an initial individual in the initial population; determining the fitness of each initial individual in the initial population according to a fitness function, and determining at least one target individual based on at least one fitness; performing crossover and mutation processing on the at least one target individual to obtain a mutated individual; adjusting the mutated individual according to the transaction association information corresponding to the target abnormal transaction to obtain a defect repair patch to be evaluated; evaluating the defect repair patch to be evaluated according to a fitness function to obtain the fitness corresponding to the defect repair patch to be evaluated, and if the fitness reaches a preset threshold, using the defect repair patch to be evaluated as the defect repair patch corresponding to the target abnormal transaction; if the fitness does not reach the preset threshold, repeating the steps of determining the target individual, performing crossover and mutation processing on the target individual, adjusting the mutated individual, and determining the fitness until the fitness reaches the preset threshold or a preset search stopping condition is met, to obtain a patch search result corresponding to the target abnormal transaction; wherein the patch search result includes a defect repair patch or defect repair failure information corresponding to the target abnormal transaction.

[0080] The initial population can be the first set of candidate patches built when the iterative search algorithm starts. This set can be based on existing candidate defect repair patches as basic elements (i.e., initial individuals), serving as the starting point for algorithm evolution. For example, assuming there are 3 candidate defect repair patches, the initial population includes these 3 candidate defect repair patches as initial individuals. The fitness function can be a quantitative function used to evaluate the repair effect of an individual (i.e., a candidate defect repair patch). Fitness is calculated based on indicators such as the pass rate of the candidate defect repair patch for test cases, whether it solves the system fault, and whether it introduces new problems. A higher fitness value indicates that the candidate defect repair patch is closer to an effective repair solution. Fitness can be a numerical value calculated by the fitness function, representing the degree of excellence of an individual. Generally, a higher fitness value indicates a higher accuracy of the individual; a lower fitness value indicates a lower accuracy of the individual. The target individual can be a candidate defect repair patch with high fitness selected from the initial population. Although these patches have not completely passed the test, they are relatively closer to an effective solution and serve as parent individuals for subsequent optimization, used to generate better new individuals.

[0081] Cross-processing can be the process of fusing the effective parts of two or more target individuals to generate a new individual containing the effective components of both. Cross-processing helps improve the potential repair capability of the processed candidate defect repair patch. Mutation processing can be the process of randomly or purposefully modifying part of the content of the target individual to introduce new changes. Cross-processing and mutation processing can prevent the algorithm from getting stuck in local optima and increase the probability of finding an effective patch. A mutated individual can be a new individual generated after cross-processing and mutation processing, that is, a new defect repair patch generated based on a candidate defect repair patch (target individual) with high fitness. The defect repair patch to be evaluated can be a patch to be verified obtained by specifically adjusting the mutated individual in combination with the transaction association information of the target abnormal transaction. Usually, it can be based on the system fault information and abnormal cause in the transaction association information to make targeted adjustments to the mutated individual to obtain the defect repair patch to be evaluated. The preset threshold can be a pre-set fitness qualification standard. When the fitness of the defect repair patch to be evaluated reaches or exceeds the threshold, it indicates that it can effectively repair the system defect corresponding to the target abnormal transaction and can be used as the final repair patch.

[0082] The preset search stopping condition can be a termination rule set to prevent iteration from going on indefinitely. The preset search stopping condition can include at least one of the following: the number of iterations reaches a preset threshold (e.g., a maximum of 50 or 100 iterations); fitness shows no significant improvement over multiple consecutive rounds (e.g., fitness fluctuation is less than 1% over 10 consecutive rounds); the search time exceeds a threshold (e.g., a maximum search time of 30 or 60 minutes). The patch search result can be the final result output after the iteration process terminates, typically including two situations: a defect repair patch corresponding to the target abnormal transaction or defect repair failure information. Defect repair failure information can refer to the failure message output when the preset search stopping condition is reached but no effective patch is found. Defect repair failure information can at least include the reason for the repair failure.

[0083] In practical implementation, if no test result is passed in at least one test, a candidate defect repair patch can be used as the initial individual to construct an initial population. Further, the initial individuals in the initial population are tested, and their performance in the tests is quantified using a fitness function to obtain a quantified fitness. This results in at least one fitness. Further, the at least one fitness can be sorted from high to low, and a predetermined number of initial individuals at the top can be used as target individuals to obtain at least one target individual. Further, at least one target individual can undergo crossover and mutation processing to obtain mutated individuals. These mutated individuals are then adjusted based on transaction association information corresponding to the target abnormal transaction, and the adjusted individuals are used as defect repair patches to be evaluated. Further, the defect repair patches to be evaluated can be tested, and their performance in the tests is quantified using a fitness function to obtain a quantified fitness. Furthermore, the fitness of the defect repair patch to be evaluated can be compared with a preset threshold. If the fitness reaches the preset threshold, the defect repair patch to be evaluated can be output as the defect repair patch corresponding to the target abnormal transaction. If the fitness does not reach the preset threshold, the steps of identifying the target individual, performing crossover and mutation on the target individual, adjusting the mutated individual, and determining the fitness can be repeated until the fitness reaches the preset threshold or the preset search stopping condition is met, thus obtaining the patch search result corresponding to the target abnormal transaction.

[0084] The technical solution of this invention determines test cases corresponding to the target abnormal transaction from a pre-built test case set based on the system fault information; further, for at least one candidate defect repair patch, the test cases are processed according to the candidate defect repair patch to obtain test results corresponding to the candidate defect repair patch; further, based on at least one test result, the defect repair patch corresponding to the target abnormal transaction is determined. This achieves accurate screening of matching test cases through system fault information, targeted verification of the effectiveness of candidate patches, accurate determination of the effectiveness of effective repair patches based on test results, ensuring that testing focuses on real defect scenarios, improving the accuracy and efficiency of patch verification, reducing invalid tests, and thus achieving accurate repair of system defects.

[0085] To facilitate a better understanding of the system repair method provided in the embodiments of the present invention, the following examples are provided for illustrative purposes. Figure 3 This is a flowchart of a system repair method provided in an embodiment of the present invention. Next, in conjunction with… Figure 3 The system repair method provided in the embodiments of the present invention will be described by way of example. Figure 3 As shown, the system repair methods may specifically include:

[0086] First, a defect dataset is obtained from an open-source defect dataset. This dataset includes transaction association information and actual defect repair patches for multiple abnormal transactions. Next, the defect dataset is preprocessed by lexicalizing the data to facilitate input into the model and to select a suitable neural network model for defect repair. Then, the transaction association information from the defect dataset is input into the selected neural network model to obtain the model output repair patch corresponding to the abnormal transaction. Finally, based on the actual defect repair patch and the model output repair patch for the abnormal transaction, a loss value is determined. The model parameters in the training model are then adjusted based on this loss value, and the convergence of the loss function in the training model is used as the training objective to obtain the defect repair model.

[0087] Furthermore, the transaction association information of the target abnormal transaction is obtained, a pre-trained defect repair model is loaded, and the transaction association information is input into the defect repair model to generate a candidate patch set, which includes at least one candidate patch. Further, a test suite (i.e., a test case set) is used to verify the correctness of each candidate patch in the candidate patch set. If the candidate patch set includes a correct candidate patch, that candidate patch is considered a valid patch and output. If the candidate patch set does not include a correct candidate patch, the candidate patches in the set are used to form an initial population. Variants in the population are selected and crossoverdone to form sub-variants. Mutation operations are used to mutate the variants, and candidate defect patches are generated based on the transaction association information. A fitness function is used to verify the candidate defect patches and confirm their correctness. If correct, a valid patch is output; if incorrect, iterative search techniques are repeated to generate candidate defect patches. If a preset search stopping condition is met and no valid patch is obtained, a defect repair failure is output, and the reason for the failure is reported.

[0088] Figure 4 This is a schematic diagram of the structure of a system repair device provided in an embodiment of the present invention. Figure 4As shown, the device includes: a transaction information acquisition module 310, a candidate patch determination module 320, and a defect repair module 330. The transaction information acquisition module 310 is used to, in response to a defect repair instruction input by a first object through a remote debugging configuration system for the system to be repaired, acquire transaction association information corresponding to at least one target abnormal transaction within a first historical time period prior to the current moment for the system to be repaired; wherein the target abnormal transaction corresponds to a system defect in the system to be repaired; the transaction association information includes at least transaction details and the cause of the abnormality; the remote debugging configuration system and the system to be repaired are located in different geographical areas; the candidate patch determination module 320 is used to, for at least one target abnormal transaction, determine at least one candidate defect repair patch corresponding to the target abnormal transaction based on the transaction association information corresponding to the target abnormal transaction; the defect repair module 330 is used to, based on at least one candidate defect repair patch and a pre-built test case set, determine a defect repair patch corresponding to the target abnormal transaction, so as to repair the system defect corresponding to the target abnormal transaction based on the defect repair patch.

[0089] The technical solution of this invention, in response to a defect repair command input by a first object through a remote debugging and configuration system for the system to be repaired, obtains transaction association information corresponding to at least one target abnormal transaction within a first historical time period prior to the current moment in the system to be repaired. This supports the first object remotely triggering the defect repair process across regions, accurately obtaining core information (transaction details and reasons for abnormality) of recent target abnormal transactions in the system to be repaired without on-site operation. This quickly identifies the actual scenario and cause of the system defect association, breaking geographical limitations and improving defect response efficiency, laying a data foundation for subsequent targeted repairs. Furthermore, by identifying at least one candidate defect repair patch corresponding to the target abnormal transaction based on the transaction association information, it ensures that each candidate defect repair patch focuses on resolving a specific defect, reducing the number of invalid repair patches, lowering the cost of trial and error in repair, shortening the time from defect discovery to candidate solution output, and improving the initial efficiency of the repair process. Furthermore, by determining the defect repair patch corresponding to the target abnormal transaction based on at least one candidate defect repair patch and a pre-built test case set, the system defect corresponding to the target abnormal transaction is repaired based on the defect repair patch, ensuring the effectiveness and security of the defect repair patch and improving its accuracy. The technical solution of this invention addresses the problems of high communication costs, low communication efficiency, incomplete fault information, low repair efficiency, and even misdiagnosis and misjudgment in related system fault handling methods. By remotely triggering the repair process, automatically acquiring comprehensive abnormal transaction information, and generating and verifying repair patches in a targeted manner, it effectively reduces the communication costs of cross-regional system faults, improves communication and repair efficiency, ensures complete and accurate fault information, reduces misdiagnosis and misjudgment, and achieves efficient, accurate, and remote repair of system defects.

[0090] Optionally, the device further includes: an abnormal transaction acquisition module and a target abnormal transaction determination module. The abnormal transaction acquisition module is used to acquire at least one abnormal transaction of the system to be repaired within a first historical period before the current time, before acquiring transaction association information corresponding to at least one target abnormal transaction of the system to be repaired within a first historical period before the current time. The target abnormal transaction determination module is used to filter the at least one abnormal transaction according to a preset abnormal cause field, and to identify the abnormal transaction matching the abnormal cause field as the target abnormal transaction; wherein the abnormal cause field is used to characterize at least one system defect.

[0091] Optionally, the candidate patch determination module 320 is specifically used to input the transaction association information corresponding to the target abnormal transaction into the pre-trained defect repair model, and obtain at least one candidate defect repair patch corresponding to the target abnormal transaction; wherein, the defect repair model is obtained by training a machine learning model based on the transaction association information corresponding to the sample abnormal transaction and the actual defect repair patch corresponding to the sample abnormal transaction.

[0092] Optionally, the transaction association information further includes system fault information; the defect repair module 330 includes: a test case determination submodule, a test result determination submodule, and a defect repair patch determination submodule. The test case determination submodule is used to determine test cases corresponding to the target abnormal transaction from a pre-built test case set based on the system fault information; the test result determination submodule is used to process the test cases according to at least one candidate defect repair patch to obtain a test result corresponding to the candidate defect repair patch; the defect repair patch determination submodule is used to determine the defect repair patch corresponding to the target abnormal transaction based on at least one test result.

[0093] Optionally, the defect repair patch determination submodule includes: a first defect repair patch determination unit and a second defect repair patch determination unit. The first defect repair patch determination unit is used to determine the defect repair patch corresponding to the target abnormal transaction based on the candidate defect repair patches that have passed the test, provided that at least one of the test results shows a test pass. The second defect repair patch determination unit is used to process at least one of the candidate defect repair patches according to an iterative search algorithm to obtain the defect repair patch corresponding to the target abnormal transaction, provided that no test result shows a test pass among at least one of the test results.

[0094] Optionally, the second defect repair patch determination unit includes: an initial population construction subunit, a target individual determination subunit, a mutated individual determination subunit, a patch to be evaluated determination subunit, and a defect repair patch determination subunit. The initial population construction subunit is used to construct an initial population based on at least one of the candidate defect repair patches; wherein the candidate defect repair patches are initial individuals in the initial population. The target individual determination subunit is used to determine the fitness of each initial individual in the initial population based on a fitness function, and to determine at least one target individual based on at least one fitness. The mutated individual determination subunit is used to perform crossover and mutation processing on at least one target individual to obtain a mutated individual. The patch to be evaluated determination subunit is used to adjust the mutated individual based on transaction association information corresponding to the target abnormal transaction to obtain a defect repair patch to be evaluated. The defect repair patch determination subunit is used to evaluate the defect repair patch to be evaluated based on the fitness function, obtain the fitness corresponding to the defect repair patch to be evaluated, and, if the fitness reaches a preset threshold, designate the defect repair patch to be evaluated as the defect repair patch corresponding to the target abnormal transaction.

[0095] Optionally, the second defect repair patch determination unit further includes an iterative execution subunit. The iterative execution subunit is used to repeatedly execute the steps of determining the target individual, performing crossover and mutation processing on the target individual, adjusting the mutated individual, and determining the fitness when the fitness does not reach a preset threshold, until the fitness reaches the preset threshold or a preset search stopping condition is met, thereby obtaining a patch search result corresponding to the target abnormal transaction; wherein the patch search result includes a defect repair patch or defect repair failure information corresponding to the target abnormal transaction.

[0096] The system repair device provided in the embodiments of the present invention can execute the system repair method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the method.

[0097] Figure 5 A schematic diagram of an electronic device 10 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0098] like Figure 5 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0099] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0100] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as system repair methods.

[0101] In some embodiments, the system repair method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the system repair method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the system repair method by any other suitable means (e.g., by means of firmware).

[0102] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0103] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0104] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0105] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0106] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), target blockchain networks, and the Internet.

[0107] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0108] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication unit 19, or installed from storage unit 18, or installed from ROM 12. When the computer program is executed by processor 11, it performs the functions defined in the methods of the embodiments of the present invention.

[0109] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0110] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A system repair method, characterized in that, include: In response to a defect repair instruction input by a first object to the system to be repaired via a remote debugging and configuration system, transaction association information corresponding to at least one target abnormal transaction of the system to be repaired within a first historical time period prior to the current moment is obtained; wherein, the target abnormal transaction corresponds to a system defect in the system to be repaired; the transaction association information includes at least transaction details and the cause of the abnormality; the remote debugging and configuration system and the system to be repaired are located in different geographical regions; For at least one of the target abnormal transactions, at least one candidate defect repair patch corresponding to the target abnormal transaction is determined based on the transaction association information corresponding to the target abnormal transaction; Based on at least one of the candidate defect repair patches and a pre-built test case set, a defect repair patch corresponding to the target abnormal transaction is determined, so as to repair the system defect corresponding to the target abnormal transaction based on the defect repair patch.

2. The system repair method according to claim 1, characterized in that, Before obtaining the transaction association information corresponding to at least one target abnormal transaction in the first historical time period prior to the current moment of the system to be repaired, the method further includes: Obtain at least one abnormal transaction from the system to be repaired within a first historical time period prior to the current moment; At least one abnormal transaction is filtered based on a preset abnormality reason field, and the abnormal transaction that matches the abnormality reason field is taken as the target abnormal transaction; wherein, the abnormality reason field is used to characterize at least one system defect.

3. The system repair method according to claim 1, characterized in that, The step of determining at least one candidate defect repair patch corresponding to the target abnormal transaction based on the transaction association information corresponding to the target abnormal transaction includes: The transaction association information corresponding to the target abnormal transaction is input into the pre-trained defect repair model, and at least one candidate defect repair patch corresponding to the target abnormal transaction is obtained. The defect repair model is obtained by training a machine learning model based on the transaction association information corresponding to the abnormal sample transaction and the actual defect repair patch corresponding to the abnormal sample transaction.

4. The system repair method according to claim 1, characterized in that, The transaction association information also includes system fault information; determining the defect repair patch corresponding to the target abnormal transaction based on at least one candidate defect repair patch and a pre-built test case set includes: Based on the system fault information, test cases corresponding to the target abnormal transaction are determined from a pre-built set of test cases; For at least one of the candidate defect fix patches, the test cases are processed according to the candidate defect fix patches to obtain test results corresponding to the candidate defect fix patches; Based on at least one of the test results, determine the defect fix patch corresponding to the target abnormal transaction.

5. The system repair method according to claim 4, characterized in that, The step of determining the defect repair patch corresponding to the target abnormal transaction based on at least one of the test results includes: If at least one of the test results is a test pass, the defect repair patch corresponding to the target abnormal transaction is determined based on the candidate defect repair patch that passed the test. If none of the test results are deemed to be passed, at least one of the candidate defect repair patches is processed according to an iterative search algorithm to obtain a defect repair patch corresponding to the target abnormal transaction.

6. The system repair method according to claim 5, characterized in that, The step of processing at least one of the candidate defect repair patches according to an iterative search algorithm to obtain a defect repair patch corresponding to the target abnormal transaction includes: An initial population is constructed based on at least one of the candidate defect repair patches; wherein the candidate defect repair patch is an initial individual in the initial population; The fitness of each initial individual in the initial population is determined according to the fitness function, and at least one target individual is determined according to at least one fitness. At least one of the target individuals is subjected to crossover and mutation processes to obtain a mutant individual; The mutated individual is adjusted based on the transaction association information corresponding to the target abnormal transaction to obtain a defect repair patch to be evaluated; The defect repair patch to be evaluated is evaluated according to the fitness function to obtain the fitness corresponding to the defect repair patch to be evaluated. When the fitness reaches a preset threshold, the defect repair patch to be evaluated is used as the defect repair patch corresponding to the target abnormal transaction.

7. The system repair method according to claim 6, characterized in that, Also includes: If the fitness does not reach a preset threshold, the steps of determining the target individual, performing crossover and mutation on the target individual, adjusting the mutated individual, and determining the fitness are repeated until the fitness reaches the preset threshold or the preset search stopping condition is met, thereby obtaining the patch search results corresponding to the target abnormal transaction; wherein, the patch search results include defect repair patches or defect repair failure information corresponding to the target abnormal transaction.

8. A system repair device, characterized in that, include: The transaction information acquisition module is used to respond to a defect repair instruction input by a first object to the system to be repaired through a remote debugging and configuration system, and to acquire transaction association information corresponding to at least one target abnormal transaction of the system to be repaired within a first historical time period before the current moment; wherein, the target abnormal transaction corresponds to a system defect in the system to be repaired; the transaction association information includes at least transaction details and the cause of the abnormality; the remote debugging and configuration system and the system to be repaired are located in different geographical regions; The candidate patch determination module is used to determine, for at least one target abnormal transaction, at least one candidate defect repair patch corresponding to the target abnormal transaction based on the transaction association information corresponding to the target abnormal transaction; The defect repair module is used to determine the defect repair patch corresponding to the target abnormal transaction based on at least one of the candidate defect repair patches and a pre-built test case set, so as to repair the system defect corresponding to the target abnormal transaction based on the defect repair patch.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the system repair method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the system repair method of any one of claims 1-7.

Images