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Security is a requirement of utmost importance to produce high-quality software. However, there is still a considerable amount of vulnerabilities being discovered and fixed almost weekly. We hypothesize that developers affect the maintainability of their codebases when patching vulnerabilities. This paper evaluates the impact of patches to improve security on the maintainability of open-source software. Maintainability is measured based on the Better Code Hubs model of 10 guidelines on a dataset, including 1300 security-related commits. Results show evidence of a trade-off between security and maintainability for 41.90% of the cases, i.e., developers may hinder software maintainability. Our analysis shows that 38.29% of patches increased software complexity and 37.87% of patches increased the percentage of LOCs per unit. The implications of our study are that changes to codebases while patching vulnerabilities need to be performed with extra care; tools for patch risk assessment should be integrated into the CI/CD pipeline; computer science curricula needs to be updated; and, more secure programming languages are necessary.
Energy efficiency is a crucial quality requirement for mobile applications. However, improving energy efficiency is far from trivial as developers lack the knowledge and tools to aid in this activity. In this paper we study the impact of changes to i
The recent Spectre attacks has demonstrated the fundamental insecurity of current computer microarchitecture. The attacks use features like pipelining, out-of-order and speculation to extract arbitrary information about the memory contents of a proce
The joint task of bug localization and program repair is an integral part of the software development process. In this work we present DeepDebug, an approach to automated debugging using large, pretrained transformers. We begin by training a bug-crea
Data-driven research on the automated discovery and repair of security vulnerabilities in source code requires comprehensive datasets of real-life vulnerable code and their fixes. To assist in such research, we propose a method to automatically colle
Recent research finds CNN models for image classification demonstrate overlapped adversarial vulnerabilities: adversarial attacks can mislead CNN models with small perturbations, which can effectively transfer between different models trained on the