Alejandro Mazuera-Rozo

Alejandro Mazuera-Rozo, Camilo Escobar-Velásquez, Juan Espitia-Acero et al.

Android is nowadays the most popular operating system in the world, not only in the realm of mobile devices, but also when considering desktop and laptop computers. Such a popularity makes it an attractive target for security attacks, also due to the sensitive information often manipulated by mobile apps. The latter are going through a transition in which the Android ecosystem is moving from the usage of Java as the official language for developing apps, to the adoption of Kotlin as the first choice supported by Google. While previous studies have partially studied security weaknesses affecting Java Android apps, there is no comprehensive empirical investigation studying software security weaknesses affecting Android apps considering (and comparing) the two main languages used for their development, namely Java and Kotlin. We present an empirical study in which we: (i) manually analyze 681 commits including security weaknesses fixed by developers in Java and Kotlin apps, with the goal of defining a taxonomy highlighting the types of software security weaknesses affecting Java and Kotlin Android apps; (ii) survey 43 Android developers to validate and complement our taxonomy. Based on our findings, we propose a list of future actions that could be performed by researchers and practitioners to improve the security of Android apps.

Alejandro Mazuera-Rozo, Anamaria Mojica-Hanke, Mario Linares-Vásquez et al.

Deep learning (DL) techniques are on the rise in the software engineering research community. More and more approaches have been developed on top of DL models, also due to the unprecedented amount of software-related data that can be used to train these models. One of the recent applications of DL in the software engineering domain concerns the automatic detection of software vulnerabilities. While several DL models have been developed to approach this problem, there is still limited empirical evidence concerning their actual effectiveness especially when compared with shallow machine learning techniques. In this paper, we partially fill this gap by presenting a large-scale empirical study using three vulnerability datasets and five different source code representations (i.e., the format in which the code is provided to the classifiers to assess whether it is vulnerable or not) to compare the effectiveness of two widely used DL-based models and of one shallow machine learning model in (i) classifying code functions as vulnerable or non-vulnerable (i.e., binary classification), and (ii) classifying code functions based on the specific type of vulnerability they contain (or "clean", if no vulnerability is there). As a baseline we include in our study the AutoML utility provided by the Google Cloud Platform. Our results show that the experimented models are still far from ensuring reliable vulnerability detection, and that a shallow learning classifier represents a competitive baseline for the newest DL-based models.