41 results about "Software analytics" patented technology

According to the safety coprocessor hybrid encryption method based on the SM2 / 3 / 4 domestic cryptographic algorithm, a novel hybrid encryption system based on the SM2 / 3 / 4 and considering encryption safety, efficiency and key management convenience is constructed on the basis of the characteristics of the SM2 algorithm, the SM3 algorithm and the SM4 algorithm, and the encryption / decryption and signature / verification process is achieved. And aiming at the efficiency problem of a domestic SM algorithm, an encryption / decryption and signature / verification scheme is designed and realized through pure software analysis and software and hardware (SW / HW) collaboration, so that optimal division of software and hardware is realized, and the algorithm efficiency is greatly improved.

Embodiments facilitate software analysis by machine learning (ML) models, through extensible software analysis architecture (ESAA) or software analysis work allocation (SAWA). Pluggable ESAA ML modules include a vetted prompt which is actionable for software analysis, with a vetting certification. Some ML modules contain computational cost information such as a token count or model round trip time. Tools are tailored to ML analyzers to control background execution, availability offerings, and results displays. SAWA determines how well a software analyzer meets a prompt's software analysis requirements, and an ML planning model generates an analysis plan that balances software analysis workloads among ML analyzers and non-ML analyzers. ML analyzers are favored for summarization, task decomposition, task scheduling, and source code change review, while non-ML analyzers are otherwise favored. Non-ML analyzers gather control flow, data flow, internal structure, and similar context which is then supplied to an ML analyzer.

The invention discloses an open source component analysis method based on a static program, and relates to the technical field of software analysis. Project source codes are input and analyzed, a semantic calling graph is obtained, and hierarchical management is conducted on the semantic calling graph; carrying out topological sorting on the layered semantic calling atlas to obtain a component check sequence; and comparing the semantic call maps corresponding to the new version and the old version of the to-be-upgraded component, performing software detection operation on the to-be-upgraded component, and outputting a conflict report. According to the method, a layered semantic calling graph is constructed and contains rich context information, so that analysis can go deep into the level of whether behaviors and semantics of the method are changed or not, topological sorting is carried out on component dependency relationships, a component checking sequence is provided, the problem that a conflict is repaired in reality is solved, and the method is more practical. By means of the method, it is guaranteed that before any component is checked, all the components on which the component depends are analyzed and confirmed, and orderliness and certainty of the analysis process are guaranteed.

An object of the invention is to quickly specify a version of software that causes a defect even when update histories of a plurality of pieces of software are managed separately. In a software analysis system according to the invention, when a performance degradation of second software is detected by a performance test, a version updated by merging first software is specified or a version where reference information of the first software is changed from that before is specified among past versions of the second software, thereby specifying a candidate in the first software which is a cause of the degradation (see FIG. 4B).

The invention relates to the technical field of maintenance planning, and particularly discloses a landscape greening maintenance planning method and system based on big data, and the method comprises the steps: constructing a maintenance region model, and updating the maintenance region model based on a shot image; rasterizing the maintenance area model, and determining greening parameters in each grid area; and clustering the grid areas based on the greening parameters, synchronously determining the demand precision of each type of grid areas, obtaining the real-time information of each grid area, feeding back the real-time information to the management end, and receiving a maintenance scheme which is fed back by the management end and contains a maintenance target. According to the method, the landscape model is constructed based on the BIM model, the landscape model is updated based on a large amount of image data, the real-time performance of the landscape model is extremely high, software analysis is performed on the landscape model, a maintenance target can be determined, a more targeted maintenance task can be generated, and the labor workload is greatly reduced under the condition that the maintenance effect is almost unchanged.

The invention provides an abnormal log analysis method and device, and relates to the technical field of software analys.The method comprises the steps that an Android device log information set is obtained, the log information set comprises multiple pieces of original log information, format standardization processing is conducted on each piece of original log information, and the original log information is obtained; obtaining a first log set comprising a plurality of pieces of log information with uniform formats; a second log set is obtained through screening according to the first log set, the second log set does not comprise redundant log information in the first log set, and the second log set comprises missing fields of the log information in the first log set; classifying multiple pieces of log information in the second log set to obtain a third log set; sorting each piece of log information in the third log set according to the time sequence and the category label to generate a plurality of log information sequences; and sending the plurality of log information sequences to a local server, and obtaining an abnormal log report and a repair suggestion corresponding to the abnormal log report according to the plurality of log information sequences.

A computer-implemented system analyzes program and data flows in a software system comprising code written in multiple programming languages using a generative large language model (LLM) directed by programming-language-specific prompts. The LLM identifies functional components within the code, generating labeled graph nodes that include a node type, a node name, and dependency information. A graph construction computer system processes the labeled graph nodes to generate a directed graph, where nodes represent functional components and directed edges represent dependencies. The system stores the graph in a database and provides a web-based interface for visualization, allowing users to explore, query, and analyze program and data flows across the software system. The system enables automated, language-agnostic dependency mapping, facilitating software analysis, debugging, and modernization.

To quickly specify a version of software causing failure even when update histories of a plurality of pieces of software are individually managed.SOLUTION: In response to detecting degradation in performance of second software by a performance test, a software analysis system relating to the present invention specifies a candidate for first software causing the degradation by specifying a past version updated by merging the first software out of past versions of the second software or specifying reference information changed from the original, of the first software.SELECTED DRAWING: Figure 4B

[Problem] The objective is to realize a technical due diligence support method that makes it easier to understand the results of software analysis. [Solution] A due diligence support method for evaluating the technical capabilities and software quality of a prospective acquisition company of a business operator, which is performed by a management terminal according to one embodiment of the present invention, wherein the control unit of the management terminal refers to information on the source code disclosed by the developer of the prospective acquisition company, performs an analysis to evaluate the technical capabilities and quality based on the information on the source code, and generates a report evaluating the technical capabilities and quality based on the analysis results. [Selected Figure] Figure 8

The application relates to the technical field of maintenance planning, and particularly discloses a landscape greening maintenance planning method and system based on big data, which comprises the following steps: constructing a maintenance area model, updating the maintenance area model based on a photographed image; performing rasterization processing on the maintenance area model to determine greening parameters in each grid area; clustering the grid areas based on the greening parameters, synchronously determining the demand accuracy of each type of grid area, obtaining real-time information of each grid area, feeding back the real-time information to a management end, and receiving a maintenance scheme containing a maintenance target fed back by the management end. The application constructs a landscape model based on a BIM model, updates the landscape model based on a large amount of image data, the real-time performance of the landscape model is extremely high, software analysis is performed on the landscape model, the maintenance target can be determined, more targeted maintenance tasks can be generated, and the manpower workload is greatly reduced while the maintenance effect is almost unchanged.

A method for developing or improving a process for producing a product from a material comprising steps of acquiring process data from at least two different sources for the production process and its relevant parameters by using a Data Collecting computer; using the acquired process data related to the production process to perform a Process Mapping step by using a Process Mapping computer; assigning the acquired process data related to the relevant parameters of the production process to its corresponding process parts by performing a Data Mapping step by using a Data Mapping computer; analyzing the therefore mapped process data with a specific software performed on an Analyzing computer thereby identifying and validating one or more existing characteristics related to the quality or performance of the production process; and using the identified and validated characteristics to develop the production process or improve its performance.

The application discloses a static program open source component analysis method, relates to the technical field of software analysis, and comprises the following steps: inputting project source code, parsing, obtaining a semantic call graph, and performing hierarchical management on the semantic call graph; performing topological sorting on the hierarchical semantic call graph, obtaining a component inspection sequence; comparing the semantic call graphs corresponding to the new and old versions of a to-be-upgraded component, performing a software detection operation on the to-be-upgraded component, and outputting a conflict report. The method constructs a hierarchical semantic call graph, which contains rich context information, so that analysis can be deepened to the level of whether the behavior and semantics of a method change; through topological sorting on component dependency relations, the application proposes a component inspection sequence, which solves the circular dilemma that repairing one conflict causes another conflict in reality, guarantees that the components depended on by any component to be inspected have been analyzed and confirmed before the component is inspected, and ensures the orderliness and certainty of the analysis process.

The application discloses a high-voltage switch cabinet fault diagnosis system, and relates to the technical field of power equipment monitoring and fault diagnosis.The system is a comprehensive diagnosis system relying on hardware collection and software analysis.The system comprises a fault information collection module, a data preprocessing unit, a multi-dimension feature extraction module, a fusion diagnosis engine and a diagnosis result output module.The application has the following innovative points: for the first time, five types of sensors, i.e.temperature sensors, partial discharge sensors, arc light sensors, electrical parameter sensors and vibration sensors, are integrated;the arc light sensors adopt a triangular redundant monitoring network;the vibration sensors compensate for the blank of mechanical fault monitoring;the system covers all types of faults of a switch cabinet, such as thermal faults, insulation faults, arc faults and mechanical faults, and greatly reduces the false negative rate.

The application discloses a kind of dynamic depth analysis engine construction methods based on genetic evolution algorithm, belong to software analysis technical field, main in solving the problem of low code coverage and malicious behavior trigger rate of existing dynamic analysis technology, main scheme includes step 1: the UI level capture of being tested Android application is carried out to obtain UI component information in the tested Android application, obtain UI level information.Step 2: specified UI event trigger is realized by injecting event function.Step 3: capture software dynamic behavior characteristic data and realize malicious behavior dynamic detection using neural network model.Step 4: event function sequence is generated and screened using genetic evolution algorithm, and the injected event function is changed by feedback regulation.The application finally realizes the dynamic optimization of UI event sequence by combining with feedback regulation while detecting malicious behavior, and realizes the identification of Android malicious software based on dynamic analysis with high trigger rate.

The invention discloses a compiled binary code recognition method based on a graph network. The method comprises the following steps: acquiring a compiled binary code to be identified in a control system, performing decompilation to obtain a decompiled pseudo code and inheritance relationships of various types in the decompiled pseudo code, and constructing a member function table; constructing a software variable relation table based on the relation between the software variables of the classes; based on the software variable relation table, constructing a directed subgraph of the member function; aggregating the directed sub-graphs into a class level general graph according to the calling relationship; and performing variable semantic inference based on the class hierarchy general graph, and identifying the type and semantics of the compiled binary code so as to determine the function of the compiled binary code in the control system. According to the method, the directed subgraph and the class level general graph of the decompilation pseudo-code can be automatically constructed, then variable type semantic inference is carried out based on a graph network structure, the decompilation pseudo-code recognition problem can be systematically solved, and therefore more efficient and accurate technical support is provided for robot reverse engineering and software analysis.

The present application provides a kind of malware analysis system and method, belong to malware analysis field.The system includes: sample acquisition module, for utilizing multiple acquisition methods to collect malware samples, and the malware samples are stored;Threat intelligence integration module, for utilizing multiple platforms to collect threat intelligence, and the threat intelligence is integrated and stored;Automated analysis module, utilizes automated analysis tool, combines analysis process and automated process, generates the automated analysis result of malware;Result verification module, utilizes selected verification method, combines multiple verification indexes, and the credibility of automated analysis result is checked;And report generation module, generates corresponding defense measures, generates malware analysis report.The present application improves the update speed of threat intelligence and the detection efficiency of new malware, reduces the false alarm rate, improves the determination accuracy, shortens the deployment time of defense measures, and reduces the amount of manual operation.

A software analysis system collects a first set of time series counter values from an original version of software instrumented with software telemetry counters, and collects a second set of time series counter values from a modified version of the software that also is instrumented with the software telemetry counters. The first time series become dimensions input to train a weight matrix of a first self-organizing map, to cause the first self-organizing map to cluster the time series into a group of first clusters describing execution of the original software. The second time series become dimensions input to train a weight matrix of a second self-organizing map, to cause the second self-organizing map to cluster the time series into a group of second clusters describing execution of the modified software. Deviation analysis between the first and second groups of clusters is used to identify execution differences between the software versions.

The present application relates to a kind of large-span super heavy plate girder overturning hoisting method, comprising the following steps: one hoisting point position determination, according to specification and actual situation in site determines the range of two hoisting point distance, the stress distribution of plate girder is calculated using finite element software analysis different hoisting point position, select the best hoisting point position of two large hoist;Two small hoist use determination, whether need to set up small hoist in the middle during overturning process is calculated using finite element software analysis;Three determine small hoist connection position, the stress distribution of different position and different stress conditions of small hoist is calculated using finite element software analysis, determine the best connection position;Four overturning hoisting, two end position large hoist first force, small hoist in the middle according to the result follow two end large hoist behind force, overturning is completed, small hoist track field, two end large hoist plate girder is hoisted in place after hook loosening, complete hoisting.The present application solves the problem that plate girder shakes in the overturning process of conventional hoisting method and has potential safety hazard.

This application provides an anomaly log analysis method and apparatus, relating to the field of software analysis technology. The method includes: acquiring an Android device log information set, the log information set including multiple raw log messages; performing format standardization processing on each raw log message to obtain a first log set including multiple log messages with uniform format; filtering and obtaining a second log set based on the first log set, the second log set not including redundant log messages in the first log set, and including missing fields of log messages in the first log set; classifying the multiple log messages in the second log set to obtain a third log set; organizing each log message in the third log set according to time sequence and category label to generate multiple log message sequences; sending the multiple log message sequences to a local server; and obtaining an anomaly log report and corresponding repair suggestions based on the multiple log message sequences.

The invention relates to a cutting system and a cutting state judgment method, and the system comprises a hardware detection module which is used for collecting a multi-source physical signal in a cutting process and carrying out the preliminary preprocessing; the software analysis module is in communication connection with the hardware detection module and is used for carrying out secondary optimization, feature extraction and intelligent identification on the preprocessed multi-source physical signals and outputting a cutting state judgment result and a hierarchical control instruction; and the linkage control module is in communication connection with the software analysis module, is in linkage with a machine tool control system, and executes cutting parameter adjustment, abnormity early warning and emergency shutdown operation after receiving the hierarchical control instruction. Multiple parameters are collected through the multi-source sensor, so that multi-dimensional physical feature collection of the cutting state is achieved, the comprehensiveness and accuracy of detection are improved, meanwhile, the intelligent recognition efficiency is improved through preliminary recognition and secondary recognition, and then the efficiency and accuracy are improved.

The invention relates to the field of software testing, in particular to a computer software testing and analyzing device which comprises a mounting box, first sliding grooves are symmetrically formed in the inner box walls of the two sides, close to the lower end, of the mounting box, and second sliding grooves are symmetrically formed in the inner box walls of the sides, away from the first sliding grooves, of the mounting box. An inlet and outlet is formed in the box wall of the top end of the mounting box in a penetrating mode, moving holes which are symmetrically formed are formed in the box wall of the bottom end of the mounting box in a penetrating mode, and a driving rotating hole internally provided with a bearing is formed in the side, away from the second sliding groove, of the mounting box in a penetrating mode; a driving motor, a screw rod and a screw sleeve can be matched with a first sliding groove and a sliding block to drive a moving wheel to move internally, carrying and moving are convenient, moving transmission connection can drive a worm and a turbine to rotate, a gear and a rack plate can be conveniently matched for moving and lifting, the software analyzer is lifted, carrying and moving in the testing process are convenient, and the testing efficiency is improved. Meanwhile, when not used, the device can enter the interior for limiting protection, the safety is high, and testing is convenient.

The invention discloses an ultra-low permeability reservoir well test dynamic early warning method based on a water injection well and a related device. The method comprises the following steps: acquiring pressure drop speed data of the water injection well; detecting the ultra-low permeability reservoir water drive radius and judging stratum seepage characteristics and reservoir dominant channels by utilizing the obtained water injection well pressure drop speed data and well testing software; and presetting an oil production well exploitation safety standard, and based on the model change in the well testing software, pre-warning whether the stratum flow conductivity meets the oil production well exploitation safety standard or not. The water injection strategy and parameters can be adjusted in time by monitoring the pressure drop speed of the water injection well in real time and analyzing the water drive radius and the stratum seepage characteristics in combination with well testing software; secondly, the mining safety standard of the oil production well is preset, and based on the early warning function of well testing software, potential safety hazards such as insufficient stratum flow conductivity can be found in time.

This invention relates to the field of software testing technology, specifically to an adaptive fuzz testing method and system based on deep learning, comprising the following steps: injecting software analysis instrumentation probes into the binary conditional jump instruction locations of the target test program; loading an initial test case set to perform branch execution operations of the program logic; generating instrumentation execution trajectory data; and inputting the instrumentation execution trajectory data into a multivariate fitting calculation structure. In this invention, a high-priority scheduling mechanism triggered by sparsity is used to dynamically adjust the execution order of the test sequence, ensuring that computing resources are concentrated on test cases located at the exploration boundary or uncovered logic regions. This mechanism breaks the traditional testing's tendency to fall into local optima, guides the testing direction from the novelty dimension of data distribution, enhances the ability to mine deep program paths, and improves the coverage and overall efficiency of vulnerability detection.

The embodiment of the invention provides a software vulnerability determination method based on static analysis, which belongs to the technical field of data processing, and specifically comprises the following steps of: writing all basic statements into a function form, unifying the formats of functions and function variables in a source code, adding parameters related to risks, and determining the risk of the source code; a software source code is divided into N layers based on a preset division rule according to a function call graph of the software source code, each layer comprises a plurality of functions, the code layers have a function call relationship, the risk of the source code of each layer is determined based on risk transfer characteristics of variables and the functions, and then the vulnerability of a software system can be determined. According to the scheme, the software source codes serve as the N independent code blocks to be processed respectively to determine the risk, and good universality is achieved for software analysis of a large code quantity.

The application relates to a software dynamic analysis method and device, computer equipment, a readable storage medium and a program product. The method comprises the following steps: performing online dynamic analysis on a real running state of software to be analyzed; the online dynamic analysis at least comprises performance analysis of hardware resources of the software to be analyzed; in response to the end of running of the software to be analyzed, performing offline dynamic analysis on behaviors of running of the software to be analyzed; the offline dynamic analysis at least comprises analysis on resource dumping of different levels of the software to be analyzed, breakpoint-based debugging tracking and bottleneck analysis based on data flow. The method can improve software analysis accuracy.

The application provides an application processing method and device, a storage medium and an electronic device, relates to the technical field of data processing, and specifically can acquire a binary program package of a first version and a second version of an application; extracts original code structures in the binary program packages of the first version and the second version; based on the original code structures, identifies seed element pairs between the first version and the second version, two seed elements in each seed element pair satisfying a preset mapping condition; based on the seed element pairs, constructs a code element relationship mapping table between the first version and the second version; and based on the code element relationship mapping table, migrates a semantic derivation result for the first version to corresponding code elements of the second version, the semantic derivation result including a mapping relationship between code elements in the original code structure of the first version and naming with business semantics. The software analysis efficiency can be effectively improved.

A test case generation method comprises the steps that a tested object is obtained, and the tested object is tested software or a code snippet in the tested software; software analysis is conducted on the tested object, engineering dependency information and contexts of the tested object are obtained, and the contexts comprise at least one of file-level contexts or project-level contexts; then, according to the engineering dependency information and the context, a test head of a test case of the tested object is generated, the test head is an initial part of the test case, and the initial part comprises information except the test method; then, a test head of the test case is continuously written through the language model, and the test case is obtained; and presenting the test case to the user. According to the method, software analysis and prompt engineering are combined, the test head is provided for the language model to generate the test case, the test head is continuously written through the language model instead of generating the test case from the beginning of the language model, and the quality and effectiveness of the test case generated by the language model are improved.

A test case generation method. The method comprises: acquiring an object under test, said object being software under test or a code snippet within said software; performing software analysis on said object to obtain engineering dependency information and context of said object, the context comprising at least one of a file-level context or a project-level context; then generating a test header of a test case of said object on the basis of the engineering dependency information and the context, the test header being an initial part of the test case, and the initial part comprising information other than a test method; next, performing continuation writing on the test header of the test case by means of a language model to obtain the test case; and presenting the test case to a user. In the method, by combining software analysis and prompt engineering, a test header is provided for a language model to generate a test case, and continuation writing is performed on the test header by means of the language model instead of generating a test case by the language model from scratch, thereby improving the quality and effectiveness of test cases generated by the language model.

As impact quotient for a test automate can be determined and assigned based on factors such as static rules, results of a software analysis of the test automate, results of an analysis of historical data regarding the test automate, and a functional expert opinion. The factors used in the impact quotient determination can be assigned different weights which are adjustable based on the functional expert opinion. A test automate whose assigned impact quotient is below a threshold can be disabled and thus excluded from continuous executions. A test automate whose assigned impact quotient is not below the threshold can maintain an enabled status; updated impact quotients can later be determined for the test automate in response to prompts, in response to which the test automate can either maintain its enabled status or be disabled. Accordingly, low impact test automates can be removed from the pool of continuously executed test automates.