My doubt : 1.Does the compilation option only need to support fortran versions above 9, o5r does it also need to support fortran 77? 2.Regarding parameter checking, *my idea is that after the user creates an array of a specified size, it is passed into the function as a parameter*. However, the array size required by the function does not match the size specified by the user. However, this idea seems to be impossible to implement in fortran77. *In addition, in addition to implicit and explicit size arrays, are there other data structures that require parameter type checking?* 3. I found out that "-fcheck=*" is an option for runtime checking, but the relevant options are commented out. OPT_fcheck_ = 1070, /* -fcheck= */ /* OPT_fcheck_assert = 1071, */ /* -fcheck=assert */ /* OPT_fcheck_bounds = 1072, */ /* -fcheck=bounds */ /* OPT_fcheck_in = 1073, */ /* -fcheck=in */ /* OPT_fcheck_invariant = 1074, */ /* -fcheck=invariant */ /* OPT_fcheck_out = 1075, */ /* -fcheck=out */ /* OPT_fcheck_switch = 1076, */ /* -fcheck=switch */ OPT_fcheckaction_ = 1077, /* -fcheckaction= */ OPT_fchecking = 1078, /* -fchecking */
And I tried : $ gfortran -o fibonacci fabonaqi.f90 -fcheck=in f951: Warning: command-line option ‘-fpreconditions’ is valid for D but not for Fortran $ gfortran --help=check cc1: warning: unrecognized argument to ‘--help=’ option: ‘check’ So Is this related to the project ? Here is my proposal draft (importran part ) Parameter Mismatch Implicit Declaration Features of Fortran 77 I think this is the reason why parameter mismatch may occur In *Fortran 77*, *Implicit Typing* is a variable type automatic inference mechanism, that is, *the compiler automatically determines its data type based on the first letter of the variable name* without explicit declaration. Variable first characterDefault data type A-H O-Z AH OZ REAL (single-precision floating point number) I-N INTEGER (integer) *This will lead* - *Error-prone*: If you make a typo (e.g., TOTAL is mistakenly written as TOAL), the compiler will not report an error, but the variable type may change. - *Poor code readability*: Implicit rules are not intuitive, increasing maintenance difficulty. Parameter mismatch of implicit-size or explicit-size array *1. Assumed-size Array* *Syntax example:* SUBROUTINE SUB(A) DIMENSION A(*) ! Assumed-size array INTEGER A ! ... END *Potential issues:* - *No bounds checking*: The compiler does not know the actual size of the array passed in, and relies entirely on the programmer to ensure correct access. - *Out-of-bounds risk*: If A(i) is accessed inside the function but i exceeds the actual array range, undefined behavior (such as memory corruption, program crash) will occur. - *No shape checking*: Even if the dimensions of the array passed in do not match (such as A(10) but the actual passed in A(5)), the compiler will not report an error. *2. Explicit-size Array * *Syntax example:* SUBROUTINE SUB(A, N) DIMENSION A(N) ! Explicit-size Array INTEGER A, N ! ... END *Potential Issues:* - *Manual Size Passing*: The array size N must be passed additionally, which is prone to errors (e.g. passing the wrong N). - *Mismatch Risk*: If N is larger than the actual array size, the function may access illegal memory. - *No Dimension Check*: The compiler will not warn even if the array dimensions passed in are different (e.g. A(10) vs A(5,2)). Technology stack mastered - Familiar with C++/C language - Familiar with Linux environment programming, proficient in three IO multiplexing technologies: epoll, select, and poll, and their underlying principles - Proficient in debugging tools such as gdb valgrind - Understand Fortran language Projects participated in - Personal project Lightweight HTTP server based on Reactor mode <https://github.com/gggggwen/HTTPWebServer> This project is developed in C++, including log output module, connection management module, business module, and I/O processing connection request processing module - OSPP-2024 Kytunig-Client data output function module development <https://gitee.com/openeuler/kytuning-client> This project is developed in Python, and its core functions include deserialization of JSON data and conversion of JSON data into Excel files.
