No abstract

Distortion due to heat input from welding is well known, but difficult to predict. It causes rework, adds cost and may affect strength. The paper addresses the complexity of the problem using a neural network model as a predictor. This gave good agreement with experimental distortion data. The sensitivity of results to different variables, including chemical composition, is reported.

Buffer overflows are one of the most common software vulnerabilities that occur when more data is inserted into a buffer than it can hold. Various manual and automated techniques for detecting and fixing specific types of buffer overflow vulnerability have been proposed, but the solution to fix Unicode buffer overflow has not been proposed yet. Public security vulnerability repository e.g., Common Weakness Enumeration (CWE) holds useful articles about software security vulnerabilities. Mitigation strategies listed in CWE may be useful for fixing the specified software security vulnerabilities. This research contributes by developing a prototype that automatically fixes different types of buffer overflows by using the strategies suggested in CWE articles and existing research. A static analysis tool has been used to evaluate the performance of the developed prototype tools. The results suggest that the proposed approach can automatically fix buffer overflows without inducing errors.