Arnaldo Sgueglia, Andrea Di Sorbo, Corrado Aaron Visaggio, Gerardo Canfora. A Systematic Literature Review of IoT Time Series Anomaly Detection Solutions. Future Generation Computer Systems (to appear) - preprint
The rapid spread of the Internet of Things (IoT) devices has prompted many people and companies to adopt the IoT paradigm, as this paradigm allows the automation of several processes related to data collection and monitoring. In this context, the sensors (or other devices) generate huge amounts of data while monitoring physical spaces and objects. Therefore, the problem of managing and analyzing these huge amounts of data has stimulated researchers and practitioners to adopt anomaly detection techniques, which are automated solutions to enable the recognition of abnormal behaviors occurring in complex systems. In particular, in IoT environments, anomaly detection very often involves the analysis of time series data and this analysis should be accomplished under specific time or resource constraints. In this systematic literature review, we focus on the IoT time series anomaly detection problem by analyzing 62 articles written from 2014 to 2021. Specifically, we explore the methods and techniques adopted by researchers to deal with the issues related to dimensionality reduction, anomaly localization, and real-time monitoring, also discussing the datasets used, and the real-case scenarios tested. For each of these topics, we highlight potential limitations and open issues that need to be addressed in future work
Andrea Di Sorbo, Sonia Laudanna, Anna Vacca, Corrado A. Visaggio, Gerardo Canfora. Profiling Gas Consumption in Solidity Smart Contracts, Journal of Systems and Software, Vol. 186, April 2022 - preprint
Nowadays, more and more applications are developed for running on a distributed ledger technology, namely dApps. The business logic of dApps is usually implemented within smart contracts developed through Solidity, a programming language for writing smart contracts on different blockchain platforms, including the popular Ethereum. In Ethereum, the smart contracts run on the machines of miners and the gas corresponds to the execution fee compensating such computing resources. However, the deployment and execution costs of a smart contract depend on the implementation choices done by developers. Unappropriated design choices could lead to higher gas consumption than necessary. In this paper, we (i) identify a set of 19 Solidity code smells affecting the deployment and transaction costs of a smart contract, and (ii) assess the relevance of such smells through a survey involving 34 participants. On top of these smells, we propose GasMet, a suite of metrics for statically evaluating the code quality of a smart contract from the gas consumption perspective. An experiment involving 2,186 smart contracts demonstrates that the proposed metrics have direct associations with deployment costs. The metrics in our suite can be used for more easily identifying source code segments that need optimizations.
Gerardo Canfora, Andrea Di Sorbo, Sara Forootani, Matias Martinez, Corrado A. Visaggio. Patchworking: Exploring the Code Changes induced by Vulnerability Fixing Activities, Information and Software Technology, vol. 142, February 2022 - preprint
Context: Identifying and repairing vulnerable code is a critical software maintenance task. Change impact analysis plays an important role during software maintenance, as it helps software maintainers to figure out the potential effects of a change before it is applied. However, while the software engineering community has extensively studied techniques and tools for performing impact analysis of change requests, there are no approaches for estimating the impact when the change involves the resolution of a vulnerability bug. Objective: We hypothesize that similar vulnerabilities may present similar strategies for patching. More specifically, our work aims at understanding whether the class of the vulnerability to fix may determine the type of impact on the system to repair. Method: To verify our conjecture, in this paper, we examine 524 security patches applied to vulnerabilities belonging to ten different weakness categories and extracted from 98 different open-source projects written in Java. Results: We obtain empirical evidence that vulnerabilities of the same types are often resolved by applying similar code transformations, and, thus, produce almost the same impact on the codebase. Conclusion: On the one hand, our findings open the way to better management of software maintenance activities when dealing with software vulnerabilities. Indeed, vulnerability class information could be exploited to better predict how much code will be affected by the fixing, how the structural properties of the code (i.e., complexity, coupling, cohesion, size) will change, and the effort required for the fix. On the other hand, our results can be leveraged for improving automated strategies supporting developers when they have to deal with security flaws.