17 results about "Software bug" patented technology

The invention discloses a server link fault positioning system and method, electronic equipment and a storage medium, and relates to the technical field of server link tests.Physical layer data and protocol layer data of a data collection point are collected through a collection assembly, a test assembly is controlled to conduct server link testing, the test assembly comprises a control module and an analysis module, and the control module is connected with the analysis module. The test instruction is issued to the server link through the control module, when the server link fault is monitored, the analysis module acquires the physical layer data and the protocol layer data of each data acquisition point from the acquisition assembly, and determines the fault position and / or the fault type of the server link according to the acquired data, so that the automatic link quality evaluation is realized, and the efficiency is improved. And the fault position and the fault type can be determined, so that the problems of difficulty in accurately positioning the fault position and distinguishing the hardware link fault and the software error through a single pressure test and low troubleshooting efficiency caused by manual fault positioning in the prior art are solved, and the technical effect of improving the troubleshooting efficiency is achieved.

A computer-implemented method for runtime detection of a software error in a predetermined software element is disclosed. The method comprises the following steps: a) Creating at least one replica software element of the predetermined software element, in particular during a creation phase, such that a test set of executable software elements is created based on the predetermined software element, wherein one or more delay elements (716) are embedded in the at least one replica software element; b) Deployment of at least two executable software elements of the test set on at least one computing platform, in particular during a test phase; c) Comparing the processing results of the at least two executable software elements used on the computing platform in order to detect the software error.

An automated functional fault localization method for software product lines integrates techniques such as Spectrum-Based Fault Localization (SBFL), machine learning algorithms, data mining, and information theory. By utilizing code blocks as the detection granularity, it performs probability allocation from three perspectives: prediction, actual execution, and correlation. The suspiciousness values of code blocks are then computed using uncertainty reasoning algorithms, enabling the rapid identification of suspicious statements. Building on this, a more precise assessment of these statements is achieved by evaluating at three levels of granularity: global, local, and code block. This ultimately results in more efficient and accurate fault localization. The code blocks directly correspond to internal feature interactions, significantly enhancing the efficiency of searching for these interactions. For program statements with a higher likelihood of causing software errors, this method allows for quick identification and localization without manual intervention, thereby facilitating the maintenance of software product line systems.

Embodiments of the present disclosure include techniques for decentralizing feature toggles. In certain embodiments, feature toggles are generated for features across a plurality of software systems. The feature toggles include annotations specifying dependencies across the plurality of software systems. The annotated feature toggles are stored in a central repository accessible by the plurality of software systems. When a feature toggle is enabled, feature toggles across the systems may be retrieved from the repository and analyzed by an LLM to determine which feature toggles may need to be updated to prevent a software error.

Methods, systems, and apparatus for automatic detection of software bugs are disclosed. An example apparatus includes a comparator to compare reference code to input code to detect a source code error in the input code; a graph generator to generate a graphical representation of the reference code or the input code, the graphical representation to identify non-overlapping code regions; and a root cause determiner to determine a root cause of the source code error in the input code, the root cause based on the non-overlapping code regions.

The present invention discloses a method, device, medium and product for enhancing the reliability of an embedded system. The method of the present invention includes deploying a hardware platform that can realize memory controller differentiation of different processor cores; running a target program in a learning mode to obtain the resources required by the target program; running the target program in a running mode and combining the resources required by the target program to generate multiple program instances with isolated resources and different processor cores, monitoring and comparing the call sequence and return value of each program instance during operation, and triggering an alarm if the call sequence or return value of any program instance is inconsistent with that of other program instances to realize monitoring of hardware failures, software errors or malicious attacks during operation. The present invention aims to achieve reliability enhancement at the software level, solve the problems of program execution reliability, resource conflict isolation, and real-time anomaly detection, and improve the system reliability in automobile, aircraft, and production manufacturing scenarios in the embedded field.

The present invention relates to a software error location method based on error propagation modeling and defect feature enhancement. The present invention first eliminates the interference of accidentally correct test cases on software error location to enhance defect features and obtain an optimized program spectrum. Then, a complex network model based on statements and test cases is constructed according to the program spectrum and the program execution context. The importance of nodes is calculated by a ranking algorithm. Different weights are then assigned to test cases based on the importance of nodes. Finally, the suspicion of statements is calculated by combining the test case weights and a coverage-based error location calculation formula. The present invention improves the efficiency of software error location by reducing the coverage of truly erroneous statements by accidentally correct test cases, and uses a complex network ranking algorithm to calculate the contribution of test cases to error location, thereby reducing the impact of multiple statements with the same error suspicion value on the error location efficiency and saving software development costs.

The invention discloses an abnormal job processing method and device, electronic equipment and a storage medium, and relates to the technical field of high-performance computing, by setting parameters of job reminding and storing job reminding information to a time wheel, the number of times of access to systems such as a database can be effectively reduced, the number of times of invalid judgment of job reminding can be reduced, and the working efficiency can be improved. The timing task period can be set more finely and flexibly, occupation of computer resources is reduced to the maximum extent on the basis of guaranteeing reminding timeliness, system stability is maintained, abnormal operation is reminded in time, and the working efficiency is improved. According to the method, the problem of insufficient server cluster resources or improper resource management as well as server hardware faults or software errors can be found earlier by the user, so that the operation cycle of the user is shortened, the operation efficiency is improved, and time and resource waste caused by abnormal operation is reduced.

The present invention discloses an automatic monitoring and synthesis method for defect reports for chatting among developer groups, and its steps include: 1) Collecting online chat data, decoupling the collected chat data, and performing data augmentation on the decoupled data to obtain a conversation decoupled data set after data augmentation; 2) Feeding the conversation decoupled data set into a conversation classification model to classify conversations containing software error information and conversations not containing software error information; 3) Feeding the conversations containing software error information into a software error information extraction model to obtain the category to which each sentence in the conversation belongs, and generating a software error report based on the sentences and their corresponding categories. The present invention realizes the full automation of the process from chat information to the generation of software error reports, can quickly and accurately generate software error reports, reduces the cost of obtaining software error reports in the software development process, broadens the access channels for software error reports, and improves the software development and maintenance efficiency.

This application discloses a server link fault location system, method, electronic device, and storage medium, relating to the field of server link testing technology. It collects physical layer and protocol layer data from data acquisition points using a data acquisition component, and controls a testing component to perform server link testing. The testing component includes a control module and an analysis module. The control module sends test commands to the server link. When a server link fault is detected, the analysis module obtains physical layer and protocol layer data from each data acquisition point from the acquisition component. Based on the acquired data, it determines the fault location and / or fault type of the server link, achieving automated link quality assessment and determining the fault location and fault type. This solves the problem that related technologies struggle to accurately locate fault locations and distinguish between hardware link faults and software errors through single stress tests, requiring manual fault location and resulting in low troubleshooting efficiency. Therefore, it achieves the technical effect of improving troubleshooting efficiency.

An automated program repair tool utilizes a neural transformer model with attention to predict the contents of a bug repair in the context of source code having a bug of an identified bug type. The neural transformer model is trained on a large unsupervised corpus of source code using a span-masking denoising optimization objective, and fine-tuned on a large supervised dataset of triplets containing a bug-type annotation, software bug, and repair. The bug-type annotation is derived from an interprocedural static code analyzer. A bug type edit centroid is computed for each bug type and used in the inference decoding phase to generate the bug repair.

Methods, systems, and computer-readable storage media for debugging errors occurring in software and can include actions of receiving a current bug ticket (CBT) including bug data descriptive of a bug occurring within a software system, prompting a LLM using a first prompt to return a first embedding representative of at least a portion of the CBT, determining a set of candidate historical bug tickets (HBTs) using the first embedding and a set of HBT embeddings, prompting the LLM using a second prompt to return a set of similar HBTs, the second prompt generated using the set of candidate HBTs, and updating the CBT to identify one or more HBTs in the set of similar HBTs.

The invention discloses an embedded system reliability enhancement method and device, a medium and a product. The method comprises the steps that a hardware platform capable of achieving differentiation of memory controllers of different processor cores is deployed; running the target program in the learning mode to obtain resources required by the target program; the method comprises the following steps: running a target program in a running mode, generating a plurality of program instances with mutually isolated resources and different processor cores by combining the resources required by the target program, and monitoring and comparing calling sequences and return values of the program instances in a running process; and if the calling sequence or the return value of any program instance is inconsistent with that of other program instances, triggering an alarm so as to monitor hardware faults, software errors or hostile attacks in the running process. The invention aims to realize the reliability enhancement of the software level, solve the problems of program execution reliability, resource conflict isolation and real-time anomaly detection, and improve the system reliability under the scenes of automobiles, airplanes and production and manufacturing in the embedded field.

The invention discloses an exception monitoring method and device, a storage medium and electronic equipment, and relates to the technical field of chips, and the method comprises the steps: responding to a condition that a target chip is in a running state, and monitoring the target chip through an exception monitoring program; when it is monitored that the target chip has the abnormal operation event, obtaining monitoring information of the abnormal monitoring program on the abnormal operation event and operation environment parameters corresponding to the abnormal operation event; determining an abnormal event type of the abnormal operation event based on the monitoring information; and generating abnormal information corresponding to the abnormal operation event according to the abnormal event type and the operation environment parameters, wherein the abnormal information is used for performing abnormal recovery on the abnormal operation event. According to the method, the abnormal event type of the abnormal operation event is determined based on the monitoring information, so that the exception is accurately distinguished; the exception information used for exception recovery is generated according to the exception event type and the operation environment parameters, so that the exception operation event is effectively recovered, and the processing efficiency of software errors is improved.

Managing source code change set commits to decrease time to identify software bugs in versions of a software project is provided. An initial order of relevant commits corresponding to the software project is determined in a priority queue based on a weight of each respective commit of the relevant commits. The initial order of the relevant commits is adjusted in the priority queue based on a build distance between the relevant commits of affected source files. A particular commit having a highest priority ranking is selected in the priority queue for build and test. The build and the test of the particular commit having the highest priority ranking in the priority queue is executed.

An automated program repair tool utilizes a neural transformer model with attention to predict the contents of a bug repair in the context of source code having a bug of an identified bug type. The neural transformer model is trained on a large unsupervised corpus of source code using a span-masking denoising optimization objective, and fine-tuned on a large supervised dataset of triplets containing a bug-type annotation, software bug, and repair. The bug-type annotation is derived from an interprocedural static code analyzer. A bug type edit centroid is computed for each bug type and used in the inference decoding phase to generate the bug repair.

Embodiments of the present disclosure include techniques for decentralizing feature toggles. In certain embodiments, feature toggles are generated for features across a plurality of software systems. The feature toggles include annotations specifying dependencies across the plurality of software systems. The annotated feature toggles are stored in a central repository accessible by the plurality of software systems. When a feature toggle is enabled, feature toggles across the systems may be retrieved from the repository and analyzed by an LLM to determine which feature toggles may need to be updated to prevent a software error.