With the proliferation of the Internet of Things devices and cyber-physical systems, there is a growing demand for highly functional and high-quality software. To address this demand, it is crucial to employ effective software verification methods. The proposed method is based on the use of physical quantities defined by the International System of Units, which have specific physical dimensions. Additionally, a transformation of the physical value orientation introduced by Siano is utilized. To evaluate the effectiveness of this method, specialized software defect models have been developed. These models are based on the statistical characteristics of the open-source C/C++ code used in drone applications. The advantages of the proposed method include early detection of software defects during compile-time, reduced testing duration, cost savings by identifying a significant portion of latent defects, improved software quality by enhancing reliability, robustness, and performance, as well as complementing existing verification techniques by focusing on latent defects based on software characteristics. By implementing this method, significant reductions in testing time and improvements in both reliability and software quality can be achieved. The method aims to detect 90% of incorrect uses of software variables and over 50% of incorrect uses of operations at both compile-time and run-time.

Misuse of measurement units and orientations leads to errors in scientific applications, Cyber Physical Systems (CPS), and IoT C/C++ programs. Standard type system are inadequate in preventing such errors. Although dimensional and orientational analysis in physics can manually detect these errors in equations, analyzing complex code with intricate physical computations is impractical. To overcome this challenge, we propose an advanced type system that incorporates units and orientations as integral components within a specialized type library. Our enhanced type system automatically detects potential errors during compile time by representing physical quantities as types and utilizing dimensional analysis, orientational analysis, and metaprogramming techniques. Our improved type system enables formal verification of C++ software, successfully verifying programs with extensive codebases. We also employ it for runtime verification of dynamic linking and pointer operations in C++ programs. The integration of compile-time verification, dimensional analysis, orientational analysis, and advanced type system enhances the robustness and accuracy of scientific applications, CPS, and IoT C/C++ programs. By leveraging these approaches, we ensure precise calculations and prevent errors related to measurement units and orientations, resulting in substantial improvements in reliability and accuracy.

With the rise of Internet of Things (IoT) devices and Cyber-Physical Systems, the demand for more functional and high-quality software has increased tremendously. To meet this need, we must reengineer and reuse existing software, as well as develop new software formal verification methods. One such method is based on physical quantities defined by the System International, which have physical dimensions. The homogeneity of physical dimensions in software code enables us to check the software code in the space of base units, making it the first basis of the new software verification method. However, this method cannot check expressions with angles, angle speed, and other similar features. To address this, a transformation for physical value orientation introduced by Siano allows us to check software code for orientational, stabilization, and other related branches. This makes the orientational homogeneity the second basis of the new software verification method. To assess the effectiveness of the proposed method, we developed special software defect models based on the statistical characteristics of software code. We used a special statistical analysis tool to define the statistical characteristics of modern software and analyzed over 2 GB of C++ GITHUB code for drones. Based on the actual distribution of software characteristics, the proposed method can detect over 60% of latent software defects. Implementing this method can significantly reduce testing time, improve reliability, and enhance overall software quality.