Network attached storage (NAS) devices are widely deployed for personal data storage. However, their distributed architecture and limited inspection interfaces pose significant challenges for comprehensive performance, security, and privacy analysis. In this paper, we first establish a threat model for NAS ecosystems. Then, we present a systematic framework NASchecker for discovering performance optimization mechanisms, security threats, and privacy leakage in NAS devices. By analyzing traffic generated during varied file operations on crafted files, NASchecker infers implemented optimizations and identifies security flaws within the traffic (e.g., susceptibility to passive sniffing and replay attacks). NASchecker also integrates NAS-specific protocol fuzzing and firmware reverse engineering to uncover deep-seated command injection, memory corruption, and improper access control vulnerabilities. NASchecker compares personally identifiable information (PII) leaked in traffic against declarations in privacy policies to detect privacy compliance issues. We evaluated NASchecker on twelve commercial NAS devices. Our results reveal that none of the tested devices employ file compression or deduplication. From a security standpoint, ten devices are vulnerable to passive sniffing and seven to replay attacks. Moreover, seven devices are affected by command injection, four by memory corruption, and eleven by improper access control. From a privacy perspective, four devices leaked PIIs that were not disclosed in their respective privacy policies. After reporting the findings to the manufacturers, we have been acknowledged by several manufacturers, resulting in the assignment of 20 CVEs and 6 NVDB entries (16 of them are rated as high severity). These findings validate NASchecker’s effectiveness and underscore the urgent need for improved design and testing practices of NAS.

Abstract Cyber-physical systems (CPSs) blend digital and physical processes. CPS software is the key to realizing their functionalities. This software needs to evolve to deal with different aspects, such as the implementation of new functionalities or bug fixes. Because of this, design–operation methods, colloquially known as “DevOps,” are paramount to be adopted within these systems. During DevOps phases, automating test execution at design time is a key enabler of streamlined software development and software quality improvement. Likewise, monitoring whether a CPS is behaving as expected at operation is similarly important. In DevOps, test oracles play an important role in enabling automated testing, ensuring the reliability of software deployments, providing feedback to developers, etc. However, defining and generating test oracles in the context of DevOps practices in CPSs need to accommodate aspects specific to CPSs, such as their time-continuous behavior and inherent uncertainties. To this end, in this paper, we propose a domain-specific language (DSL) to ease the definition of test oracles and an automated solution for generating a microservice encapsulating the defined test oracles, which is compatible with a DevOps ecosystem for CPSs. We evaluated our DSL with two industrial case study systems and 9 open-source CPSs. Our evaluation results suggest that our DSL can model around 98% of the requirements of these systems through test oracles. Furthermore, it is possible to generate a microservice to be applicable at different test levels within less than 20 min, being fast enough to be adopted in practice.

Since multilanguage programming has the strength of interoperating languages with different features and paradigms, and it also enables the reuse of existing libraries, developers often use multilanguage interoperability in software systems of different application domains. Program analysis is an effective way to maintain the reliability and safety of software. However, numerous challenges appear when using program analysis to analyze multilanguage interoperability. However, there still lacks a systematic overview of the multilanguage interoperability and the corresponding program analysis (e.g., what are the research trends, how existing works solve them). To bridge this gap, we conducted a comprehensive investigation of the 195 research works related to multilanguage interoperability and its program analysis in the past 38 years (1987–2024). In this article, we classify these works into three categories with 10 research perspectives, including foreign interface design, interface definition and generation, intermediate representation (IR), semantics, static analysis of memory management, type system, exception handling and concurrency, dynamic analysis, and others. Then, we evaluate research trends that affect multilanguage interoperability and key static/dynamic language futures of multilanguage program analysis. Finally, we discuss the open challenges and future research directions of multilanguage interoperability.

Industrial elevator systems are commonly used software systems in our daily lives, which operate in uncertain environments such as unpredictable passenger traffic, uncertain passenger attributes and behaviors, and hardware delays. Understanding and assessing the robustness of such systems under various uncertainties enable system designers to reason about uncertainties, especially those leading to low system robustness, and consequently improve their designs and implementations in terms of handling uncertainties. To this end, we present a comprehensive empirical study conducted with industrial elevator systems provided by our industrial partner Orona, which focuses on assessing the robustness of a dispatcher—that is, a software component responsible for elevators’ optimal scheduling. In total, we studied 90 industrial dispatchers in our empirical study. Based on the experience gained from the study, we derived an uncertainty-aware robustness assessment method (named UncerRobua ) comprising a set of guidelines on how to conduct the robustness assessment and a newly proposed ranking algorithm, for supporting the robustness assessment of industrial elevator systems against uncertainties.

Industrial elevator systems are complex Cyber-Physical Systems operating in uncertain environments and experiencing uncertain passenger behaviors, hardware delays, and software errors. Identifying, understanding, and classifying such uncertainties are essential to enable system designers to reason about uncertainties and subsequently develop solutions for empowering elevator systems to deal with uncertainties systematically. To this end, we present a method, called RuCynefin, based on the Cynefin framework to classify uncertainties in industrial elevator systems from our industrial partner (Orona, Spain), results of which can then be used for assessing their robustness. RuCynefin is equipped with a novel classification algorithm to identify the Cynefin contexts for a variety of uncertainties in industrial elevator systems, and a novel metric for measuring the robustness using the uncertainty classification. We evaluated RuCynefin with an industrial case study of 90 dispatchers from Orona to assess their robustness against uncertainties. Results show that RuCynefin could effectively identify several situations for which certain dispatchers were not robust. Specifically, 93% of such versions showed some degree of low robustness against uncertainties. We also provide insights on the potential practical usages of RuCynefin, which are useful for practitioners in this field.

Orona is a world-renowned elevators developer. During elevators' lives, their software continues to evolve, e.g., due to hardware obsolescence, requirements changes, vulnerabilities, and bug corrections. Such continuous evolution demands the continuous testing of industrial elevators with the minimum manual effort possible. To this end, we present a tool, whose core component is a domain-specific language (DSL) with which a user can specify test oracles at a higher level of abstraction and independent of a testing level. The DSL also supports specifying uncertainty-aware test oracles to test elevators under various uncertainties inherent in them. Finally, the DSL is also equipped with test oracle generation that generates test oracle code automatically at the different DevOps testing levels (i.e., Software and Hardware-in-the-Loop test levels, and in operation) to enable reuse of test oracles across these levels. We evaluated this DSL with an industrial elevators case study at Orona's site to specify and generate test oracles. The evaluation showed that the high expressiveness of the DSL permits the high-level definition of test oracles in our industrial context. Based on the industrial application, we discuss our experiences and lessons learned.