CN110162480B - Automatic analysis method for structured diagnosis object - Google Patents
Automatic analysis method for structured diagnosis object Download PDFInfo
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- CN110162480B CN110162480B CN201910468391.2A CN201910468391A CN110162480B CN 110162480 B CN110162480 B CN 110162480B CN 201910468391 A CN201910468391 A CN 201910468391A CN 110162480 B CN110162480 B CN 110162480B
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Abstract
An automatic analysis method of a structured diagnosis object, which defines a diagnosis resource definition macro and a diagnosis object generation API; the script reads in a resource description file of a diagnosis object, generates description information of a diagnosis object ID and a diagnosis data structure name, and inserts the description information into a diagnosis database file corresponding to a diagnosed executable program; carrying out syntactic analysis on the structural body header file of the diagnosis object, extracting structural body information, and inserting the structural body information into a diagnosis database file corresponding to the diagnosed executable program; when the diagnosed program runs, calling a diagnosis object to generate an API (application program interface), and outputting the API to a diagnosis tool; when the diagnosis tool is started, a diagnosis database file of the diagnosed executable program is imported, when a diagnosis object needing structural analysis is received, the diagnosis ID of the diagnosis object and the binary memory of the diagnosis object are extracted, the structural description of the diagnosis object is found from the diagnosis database according to the diagnosis object ID, and the binary memory of the diagnosis object is formatted according to the structural description information.
Description
Technical Field
The invention relates to the field of computer programs, in particular to an automatic analysis method for a structured diagnosis object.
Background
Computer programs are typically diagnosed by printing functions, tracing the execution of functions, the state machine of the program, and state variables. The program consists of a process, an algorithm and a data structure, wherein the data structure is a format for processing and exchanging data by the program. In practice, a developer often needs a diagnosis and debugging tool to structurally display a diagnosis data structure customized by the developer, and the general method is as follows:
the method also has a large workload, all diagnostic objects cannot be analyzed in a normalized mode, and once an original diagnostic data structure changes, a text analysis program on the tool side needs to be modified in a linkage mode and is highly coupled.
This method is inefficient in diagnosis and requires a large amount of effort by developers to manually parse the binary memory.
Disclosure of Invention
Aiming at the problems, the invention provides an automatic analysis method of a structured diagnosis object, which does not need to manually analyze the structured diagnosis memory information, developers can call the diagnosis API in a normalized mode, the structured diagnosis data object is packaged and sent to diagnosis tool software, and the diagnosis tool can automatically analyze the structured diagnosis memory information and displays the information in a text mode.
A method for automated interpretation of a structured diagnostic subject, comprising the steps of:
step 1, defining a diagnosis resource definition macro and a diagnosis object to generate an API;
step 2, self-defining the structured diagnostic information objects of each program module by a diagnostic resource definition macro, and adding the objects into one or more specific header files H;
step 3, the script reads in the resource description file of the diagnostic object, generates a diagnostic object ID and the description information of the diagnostic data structure name, and inserts the diagnostic object ID and the description information into a diagnostic database file corresponding to the diagnosed executable program;
step 4, carrying out syntactic analysis on the structural body header file of the diagnosis object through a clone compiler and the attached plug-ins thereof, extracting structural body information, and inserting the structural body information into a diagnosis database file corresponding to the diagnosed executable program;
step 5, when the diagnosed program runs, calling the diagnosis object generation API in the step one, packaging according to the indicated frame format, and outputting to a diagnosis tool;
and 6, importing a diagnosis database file of the diagnosed executable program when the diagnosis tool is started, extracting the diagnosis ID of the diagnosis object and the binary memory of the diagnosis object when the diagnosis object needing structural analysis is received, searching the structural description of the diagnosis object from the diagnosis database according to the diagnosis object ID, and formatting the binary memory of the diagnosis object according to the structural description information.
Further, in step 1, the diagnosis resource definition macro is:
DIAG_PAYLOAD_STRUCT_DEF(PAYLOAD_ID,PAYLOAD_STRUCT_NAME)
wherein the parameters are as follows:
PYALOAD _ ID: an unsigned shaping ID, which is a unique ID within the application and refers to the identity of the structured diagnostic payload to be parsed;
PAYLOAD _ STRUCT _ NAME: a structure name string corresponding to PAYLOAD _ ID.
Further, in step 1, the diagnostic object generates an API:
DIAG_PAYLOAD_DUMP(PAYLOAD_ID,MOD_ID,FILTER_MASK,Ppayload,PAYLOAD_SZ)
the function defines a diagnostic API with which to pack the data structure object to be structurally parsed into a diagnostic frame, where the parameters are specified as follows:
PYALOAD _ ID: an unsigned shaping ID, which is a unique ID within the application and refers to the identity of the structured diagnostic payload to be parsed;
MOD _ ID: an ID identifying the program module;
FILTER _ MASK: a 32bit shaping for diagnostic filtering, the filter type of the diagnostic item being formulated by the program developer;
ppayload: a memory pointer to a structured object;
PAYLOAD _ SZ: the memory size of the structured object pointed to by Ppayload is in bytes.
Further, in the step 3, specifically, the python script reads in an H file, performs regularization matching according to the DIAG _ PAYLOAD _ stuct _ DEF substring to obtain a string beginning with "DIAG _ PAYLOAD _ stuct _ DEF" for each action, establishes a mapping relationship (one object for each row) between all PAYLOAD _ IDs and PAYLOAD _ stuct _ NAME, and finally redirects to a diagnostic database file DIAG _ PAYLOAD _ object _ map.db, where the database file is used for a diagnostic tool to perform structured analysis to provide information retrieval.
Further, in step 4, specifically, a blank C file for extracting the diagnostic data structure is created, the C file includes all other header files required for defining the diagnostic data structure, a compiling script of the source compiler front-end software clone is written, a "-fsyntax-only" compiling flag is opened, the printfunc names in the clone example directory are compiled into a library file, the library file is loaded by the compiling script, for example, CFLAGS + = -fsyntax-only-load printfunc names.
Further, in step 6, specifically, the upper computer software receives the data stream received by the IO, identifies the boundary of each diagnostic load, sorts out a complete diagnostic load packet, extracts PYALOAD _ ID according to the diagnostic frame format, finds a struct NAME ST _ NAME character string corresponding to the PAYLOAD _ ID in the DIAG _ PAYLOAD _ object _ map.db, finds a specific struct object description in struct xmldef.xml according to the ST _ NAME, and performs normalized text parsing on the Ppayload binary memory according to the description.
Further, in step 6, the information describing the structure to be diagnosed includes: the total number of data fields of the structure; the size of the structure; the type, domain name and offset of each data field are described, if the type of the data field is not the basic data type, the diagnostic tool needs to be expanded iteratively and analyzed in a text mode.
The invention has the beneficial effects that: the development or work of manually analyzing the structured memory is reduced to 0, and the diagnosis tool is loosely coupled with the diagnosed program, so that the automatic analysis of the structured diagnosis information is realized through automation and normalization. The diagnosis efficiency and the efficiency of diagnosis development are greatly improved.
Drawings
FIG. 1 is a diagram illustrating the definition of a function prototype in step 1 according to the present invention.
FIG. 2 is a diagram illustrating step 3 in the present invention.
Fig. 3 is how to generate a structured text description file of all the diagnosis objects in step 4 of the present invention.
FIG. 4 is a frame format illustration of the diagnostic API organized into a binary diagnostic package in step 5 of the present invention.
Detailed Description
The technical scheme of the invention is further explained in detail by combining the drawings in the specification.
A method for automated interpretation of a structured diagnostic subject, comprising the steps of:
step 1, defining a diagnosis resource definition macro and a diagnosis object generation API.
In step 1, the diagnostic resource definition macro is:
DIAG_PAYLOAD_STRUCT_DEF(PAYLOAD_ID,PAYLOAD_STRUCT_NAME)
wherein the parameters are as follows:
PYALOAD _ ID: an unsigned shaping ID, which is a unique ID within the application and refers to the identity of the structured diagnostic payload to be parsed;
PAYLOAD _ STRUCT _ NAME: a structure name string corresponding to PAYLOAD _ ID.
In the step 1, the diagnostic object generation API:
DIAG_PAYLOAD_DUMP(PAYLOAD_ID,MOD_ID,FILTER_MASK,Ppayload,PAYLOAD_SZ)
the function defines a diagnostic API with which to package the data structure object to be structurally parsed into a diagnostic frame, where the parameters are specified as follows:
PYALOAD _ ID: an unsigned shaping ID, which is a unique ID within the application and refers to the identity of the structured diagnostic payload to be parsed;
MOD _ ID: an ID identifying the program module;
FILTER _ MASK: a 32bit shaping for diagnostic filtering, the filter type of the diagnostic item being specified by the program developer;
ppayload: a memory pointer to a structured object;
PAYLOAD _ SZ: the memory size of the structured object pointed to by Ppayload is in bytes.
And 2, self-defining the structural diagnosis information objects of all program modules through the diagnosis resource definition macro, and adding the structural diagnosis information objects into one or more specific header files H.
And 3, reading in a resource description file of the diagnostic object by the script, generating description information of the diagnostic object ID and the diagnostic data structure name, and inserting the description information into a diagnostic database file corresponding to the diagnosed executable program.
In the step 3, specifically, the python script reads in an H file, performs regularization matching according to the DIAG _ PAYLOAD _ stuct _ DEF substring to obtain a character string beginning with "DIAG _ PAYLOAD _ stuct _ DEF" for each behavior, establishes a mapping relationship between all PAYLOAD _ IDs and PAYLOAD _ stuct _ NAMEs, and finally redirects to a diagnostic database file DIAG _ PAYLOAD _ object _ map.db, which is used for a diagnostic tool to perform structured parsing to provide information retrieval.
And 4, carrying out syntactic analysis on the structural body header file of the diagnosis object through the clone compiler and the attached plug-in thereof, extracting structural body information, and inserting the structural body information into the diagnosis database file corresponding to the diagnosed executable program.
In step 4, specifically, a blank C file for extracting the diagnostic data structure is created, the C file includes other header files required by all the diagnostic data structure definitions, a compiling script of the source compiler front-end software clone is written, a "-fsyntax-only" compiling flag is opened, printfuncationnames in the clone example directory are compiled into library files, the library files are loaded by the compiling script, for example, CFLAGS + = -fsyntax-only-load printfuncationnames.
And 5, when the diagnostic program runs, calling the diagnostic object generation API in the first step, packaging according to the indicated frame format, and outputting to a diagnostic tool.
And 6, importing a diagnosis database file of the diagnosed executable program when the diagnosis tool is started, extracting the diagnosis ID of the diagnosis object and the binary memory of the diagnosis object when the diagnosis object needing structural analysis is received, searching the structural description of the diagnosis object from the diagnosis database according to the diagnosis object ID, and formatting the binary memory of the diagnosis object according to the structural description information.
In step 6, specifically, the upper computer software receives the data stream received by the IO, identifies the boundary of each diagnostic load, sorts out a complete diagnostic load packet, extracts PYALOAD _ ID according to the diagnostic frame format, finds a struct NAME ST _ NAME character string corresponding to the PAYLOAD _ ID in the DIAG _ PAYLOAD _ object _ map.db, finds a specific struct object description in struct xmldef.xml according to the ST _ NAME, and performs normalized text parsing on the Ppayload binary memory according to the description.
In the step 6, the information describing the structure to be diagnosed includes: the total number of data fields of the structure; the size of the structure; the type, domain name and offset of each data field are described, if the type of the data field is not the basic data type, the diagnostic tool needs to be expanded iteratively and analyzed in a text mode.
The above description is only a preferred embodiment of the present invention, and the scope of the present invention is not limited to the above embodiment, but equivalent modifications or changes made by those skilled in the art according to the disclosure of the present invention should be included in the scope of the present invention as set forth in the appended claims.
Claims (6)
1. A method for automated interpretation of a structured diagnostic object, comprising: the method comprises the following steps:
step 1, defining a diagnosis resource definition macro and a diagnosis object generation API;
step 2, self-defining the structured diagnostic information objects of each program module by a diagnostic resource definition macro, and adding the objects into one or more specific header files H;
step 3, the script reads in the resource description file of the diagnosis object, generates a description information of the diagnosis object ID and the diagnosis data structure name, and inserts the description information into the diagnosis database file corresponding to the diagnosed executable program;
in the step 3, specifically, a python script reads in an H file, performs regularization matching according to a DIAG _ PAYLOAD _ stuct _ DEF substring to obtain a character string beginning with "DIAG _ PAYLOAD _ stuct _ DEF" for each behavior, establishes a mapping relationship between all PAYLOAD _ IDs and PAYLOAD _ stuct _ NAMEs, that is, an object in each row, and finally redirects to a diagnostic database file DIAG _ PAYLOAD _ object _ map.db for a diagnostic tool to perform structured analysis to provide information retrieval;
step 4, carrying out syntactic analysis on the structural body header file of the diagnosis object through a clone compiler and the attached plug-ins thereof, extracting structural body information, and inserting the structural body information into a diagnosis database file corresponding to the diagnosed executable program;
step 5, when the diagnosed program runs, calling the diagnosis object generation API in the step one, packaging according to the indicated frame format, and outputting to a diagnosis tool;
and 6, importing a diagnosis database file of the diagnosed executable program when the diagnosis tool is started, extracting the diagnosis ID of the diagnosis object and the binary memory of the diagnosis object when the diagnosis object needing structural analysis is received, searching the structural description of the diagnosis object from the diagnosis database according to the diagnosis object ID, and formatting the binary memory of the diagnosis object according to the structural description information.
2. The method of claim 1, wherein the automated analysis of the structured diagnostic object comprises: in step 1, the diagnostic resource definition macro is:
DIAG_PAYLOAD_STRUCT_DEF(PAYLOAD_ID,PAYLOAD_STRUCT_NAME)
wherein the parameters are as follows:
PYALOAD _ ID: an unsigned integer ID, which is a unique ID within the application and refers to the identity of the structured diagnostic payload to be resolved;
PAYLOAD _ STRUCT _ NAME: a structure name string corresponding to PAYLOAD _ ID.
3. The automated method of structured diagnosis of an object of claim 1, wherein: in the step 1, the diagnosis object generation API:
DIAG_PAYLOAD_DUMP(PAYLOAD_ID,MOD_ID,FILTER_MASK,Ppayload,PAYLOAD_SZ)
the function defines a diagnostic API with which to package the data structure object to be structurally parsed into a diagnostic frame, where the parameters are specified as follows:
PYALOAD _ ID: an unsigned integer ID, which is a unique ID within the application and refers to the identity of the structured diagnostic payload to be resolved;
MOD _ ID: an ID identifying the program module;
FILTER _ MASK: a 32bit integer for diagnostic filtering, the filter type of the diagnostic item being formulated by the program developer;
ppayload: a memory pointer to a structured object;
PAYLOAD _ SZ: the memory size of the structured object pointed to by Ppayload is in bytes.
4. The automated method of structured diagnosis of an object of claim 1, wherein: in step 4, specifically, a blank C file for extracting the diagnostic data structure is created, the C file includes other header files required by all the diagnostic data structure definitions, a compiling script of the source compiler front-end software clone is written, a "-fsyntax-only" compiling flag is opened, printfuncationnames in the clone example directory are compiled into library files, the library files are loaded by the compiling script, for example, CFLAGS + = -fsyntax-only-load printfuncationnames.
5. The method of claim 1, wherein the automated analysis of the structured diagnostic object comprises: in step 6, specifically, the upper computer software receives the data stream received by the IO, identifies the boundary of each diagnostic load, sorts out a complete diagnostic load packet, extracts the paralad _ ID according to the diagnostic frame format, finds a specific structure object description in structxmldef.xml in the DIAG _ PAYLOAD _ object _ map.db corresponding to the paralad _ ID, and then performs normalized text parsing on the ppaoad binary memory according to the description.
6. The automated method of structured diagnosis of an object of claim 1, wherein: in the step 6, the information describing the structure to be diagnosed includes: the total number of data fields of the structure; the size of the structure; the type, domain name and offset of each data field are described, if the type of the data field is not the basic data type, the diagnostic tool needs to be expanded iteratively and analyzed in a text mode.
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