The quality of software projects is measured by different attributes such as efficiency, security, robustness, or understandability, among others. In this paper, we focus on maintainability by studying the optimization of software modularity, which is one of the most important aspects in this regard. Specifically, we study two well-known and closely related multi-objective optimization problems: the Equal-size Cluster Approach Problem (ECA) and the Maximizing Cluster Approach Problem (MCA). Each of these two problems looks for the optimization of several conflicting and desirable objectives in terms of modularity. To this end, we propose a method based on the Multi-Objective Variable Neighborhood Search (MO-VNS) methodology in combination with a constructive procedure based on Path-Relinking. As far as we know, this is the first time that a method based on MO-VNS is proposed for the MCA and ECA problems. To enhance the performance of the proposed algorithm, we present three advanced strategies: an incremental evaluation of the objective functions, an efficient exploration of promising areas in the search space, and an analysis of the objectives that better serve as guiding functions during the search phase. Our proposal has been validated by experimentally comparing the performance of our algorithm with the best previous state-of-the-art method for the problem and three reference methods for multi-objective optimization. The experiments have been performed on a set of 124 real software instances previously reported in the literature.

Machine Learning (ML) techniques, especially Artificial Neural Networks, have been widely adopted as a tool for malware detection due to their high accuracy when classifying programs as benign or malicious. However, these techniques are vulnerable to Adversarial Examples (AEs), i.e., carefully crafted samples designed by an attacker to be misclassified by the target model. In this work, we propose a general method to produce AEs from existing malware, which is useful to increase the robustness of ML-based models. Our method dynamically introduces unused blocks (caves) in malware binaries, preserving their original functionality. Then, by using optimization techniques based on Genetic Algorithms, we determine the most adequate content to place in such code caves to achieve misclassification. We evaluate our model in a black-box setting with a well-known state-of-the-art architecture (MalConv), resulting in a successful evasion rate of 97.99 % from the 2k tested malware samples. Additionally, we successfully test the transferability of our proposal to commercial AV engines available at VirusTotal, showing a reduction in the detection rate for the crafted AEs. Finally, the obtained AEs are used to retrain the ML-based malware detector previously evaluated, showing an improve on its robustness.