Dynamic data race detection method based on static shared variable recognition

Abstract

The invention discloses a dynamic data race detection method based on static shared variable recognition. The method comprises the steps: S1, recognizing a shared variable of a program to be detected; S2, performing data race detection instrumenting and sampling on a basic block of the shared variable contained in the program to be detected, to obtain instrumented and sampled binary code of the program to be detected, wherein the basic block references to a continuous program statement sequence, a control flow starts entering from the continuous program sentence sequence and leaves from the end without interruption or branch; and S3, running the binary code to dynamically detect data race in spot detection. The method can be used for instrumenting and sampling the basic block containing the shared variable, thus avoiding the problem of code bloat, and greatly reducing the system overhead.

Description

technical field [0001] The invention relates to the technical field of computer software reliability, in particular to a dynamic data competition detection method based on static shared variable identification. Background technique [0002] With the development of the underlying multi-core processors, concurrent programs are becoming more and more popular with programmers. However, concurrent programs are not only difficult to write correctly, but also have various software defects that are difficult to debug and reproduce. Data race is one of the important causes of software defects in concurrent programs. A data race is when multiple threads access the same memory address without synchronization protection, and at least one of the accesses is a write operation. Because of its serious consequences, academia and industry have been trying to find an effective detection method. [0003] Existing data race detection methods are all based on software instrumentation, which ar...

AI Technical Summary

Problems solved by technology

This overhead can sometimes reach 20% if the code that checks the sampling is implemented poorly

[0006] 2. Code Expansion

If a function body is very large, but only a small part of the code area is really related to data races, sampling based on function granularity will copy the entire function and add instrumentation code to the copied function, which will inevitably cause program code bloat, interfere with the program

[0007] In summary, the existing sampling methods based on function granularity do not consider the characteristics of concurrent program defects or data races

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