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AI-based code assistants are increasingly popular as a means to enhance productivity and improve code quality. This study compares four AI-based code assistants, GitHub Copilot, Tabnine, ChatGPT, and Google Bard, in method generation tasks, assessing their ability to produce accurate, correct, and efficient code. Results show that code assistants are useful, with complementary capabilities, although they rarely generate ready-to-use correct code.

Reusing test cases across apps that share similar functionalities reduces both the effort required to produce useful test cases and the time to offer reliable apps to the market. The main approaches to reuse test cases across apps combine different semantic matching and test generation algorithms to migrate test cases across Android apps. In this paper we define a general framework to evaluate the impact and effectiveness of different choices of semantic matching with Test Reuse approaches on migrating test cases across Android apps. We offer a thorough comparative evaluation of the many possible choices for the components of test migration processes. We propose an approach that combines the most effective choices for each component of the test migration process to obtain an effective approach. We report the results of an experimental evaluation on 8,099 GUI events from 337 test configurations. The results attest the prominent impact of semantic matching on test reuse. They indicate that sentence level perform better than word level embedding techniques. They surprisingly suggest a negligible impact of the corpus of documents used for building the word embedding model for the Semantic Matching Algorithm. They provide evidence that semantic matching of events of selected types perform better than semantic matching of events of all types. They show that the effectiveness of overall Test Reuse approach depends on the characteristics of the test suites and apps.

Users regularly use apps to access services in a range of domains, such as health, productivity, entertainment, and business. The safety and correctness of the runtime behavior of these apps is thus a key concern for users. Indeed, unreliable apps may generate dissatisfaction, frustration and issues to users. Runtime enforcement techniques can be used to implement software enforcers that monitor executions and apply corrective actions when needed, potentially preventing misbehaviors and failures. However, enforcers might be faulty themselves, applying the wrong actions or missing to apply the right actions. To address this problem, this paper presents Test4Enforcers, an approach to automatically test software enforces. Test4Enforcers relies on an enforcement model describing the strategy that shall be applied at runtime to correct misbehaviors. Test4Enforcers first uses the enforcement model to derive a specification of the test cases that shall be executed to validate any software enforcer implemented from the given model. Then, it automatically turns the test specification into a set of concrete test cases that can be executed against apps augmented with the enforcers. We evaluated Test4Enforces with a set of 3,135 faults injected in the enforcers derived from 13 enforcement models. Results show that Test4Enforcers can automatically reveal 64% of the faults, while existing approaches relying on crash detection can only reveal 6% of the faults. Test4Enforcers is also practical since testing an enforcer required 9 min, in the worst case.

Monitoring is a key feature to enhance systems with the capability to anticipate, detect, predict, and mitigate failures, while providing Quality of Service (QoS) monitoring and Service Level Agreements (SLAs) guarantee. Monitoring frameworks can serve these purposes by deploying probes according to many possible patterns that have different features, for instance in terms of efficiency and privacy. So far, these probe deployment patterns have not been systematically defined, analyzed and assessed. Thus, engineers who design and configure their monitoring systems have to take decisions only based on partial knowledge and personal experience. This paper addresses this knowledge gap, by presenting a systematic analysis of 11 probe deployment patterns, their known uses, and implementations. We assess these patterns qualitatively, and quantitatively using both VMs and containers. Results show the targets have negligible resource consumption (e.g., less than 1% CPU usage), while the probe holder consumption is mainly significant in relation to memory consumption, reaching up to 10 GiB in our experiments. Our findings suggest that reusing probes and holders among users can generally enhance efficiency and scalability when direct access to the monitored target is not an option. We generate a set of best practices that can assist engineers in configuring their monitoring systems. Finally, we showcase the application of certain patterns through three practical usage scenarios, which feature diverse technologies and requirements.

Many automatic Web testing techniques generate test cases by analyzing the GUI of the Web applications under test, aiming to exercise sequences of actions that are similar to the ones that testers could manually execute. However, the efficiency of the test generation process is severely limited by the cost of analyzing the content of the GUI screens after executing each action. In this paper, we introduce an inference component, S, which accumulates knowledge about the behavior of the GUI after each action. S enables the test generators to reuse the results computed for GUI screens that recur multiple times during the test generation process, thus improving the efficiency of Web testing techniques. We experimented S with Web testing techniques based on three different GUI exploration strategies (Random, Depth-first, and Q-learning) and nine target systems, observing reductions from 22% to 96% of the test generation time

Generative AI is changing the way developers interact with software systems, providing services that can produce and deliver new content, crafted to satisfy the actual needs of developers. For instance, developers can ask for new code dtrectly from within their IDEs by writing natural language prompts, and integrated services based on generative AI, such as Copilot, immediately respond to prompts by providing ready-to-use code snippets. Formulating the prompt appropriately, and incorporating the useful information while avoiding any information overload, can be an important factor in obtaining the right piece of code. The task of designing good prompts is known as prompt engineering. In this paper, we systematically investigate the influence of eight prompt features on the style and the content of prompts, on the level of correctness, complexity, size, and similarity to the developers' code of the generated code. We specifically consider the task of using Copilot with 124,800 prompts obtained by systematically combining the eight considered prompt features to generate the implementation of 200 Java methods. Results show how some prompt features, such as the presence of examples and the summary of the purpose of the method, can significantly influence the quality of the result. Ccs Concepts • Software and its engineering →Integrated and visual development environments.

AI-based code assistants are promising tools that can facilitate and speed up code development. They exploit machine learning algorithms and natural language processing to interact with developers, suggesting code snippets (e.g., method implementations) that can be incorporated into projects. Recent studies empirically investigated the effectiveness of code assistants using simple exemplary problems (e.g., the re-implementation of well-known algorithms), which fail to capture the spectrum and nature of the tasks actually faced by developers. In this paper, we expand the knowledge in the area by comparatively assessing four popular AI-based code assistants, namely GitHub Copilot, Tabnine, ChatGPT, and Google Bard, with a dataset of 100 methods that we constructed from real-life open-source Java projects, considering a variety of cases for complexity and dependency from contextual elements. Results show that Copilot is often more accurate than other techniques, yet none of the assistants is completely subsumed by the rest of the approaches. Interestingly, the effectiveness of these solutions dramatically decreases when dealing with dependencies outside the boundaries of single classes.

Estimating software testability can crucially assist software managers to optimize test budgets and software quality. In this paper, we propose a new approach that radically differs from the traditional approach of pursuing testability measurements based on software metrics, e.g., the size of the code or the complexity of the designs. Our approach exploits automatic test generation and mutation analysis to quantify the evidence about the relative hardness of developing effective test cases. In the paper, we elaborate on the intuitions and the methodological choices that underlie our proposal for estimating testability, introduce a technique and a prototype that allows for concretely estimating testability accordingly, and discuss our findings out of a set of experiments in which we compare the performance of our estimations both against and in combination with traditional software metrics. The results show that our testability estimates capture a complementary dimension of testability that can be synergistically combined with approaches based on software metrics to improve the accuracy of predictions.

Phosphorite deposits form through phosphogenesis, a mechanism of diffusion and concentration of interstitial phosphate in sediments leading to Ca-phosphate mineral precipitation in a favorable sedimentary regime at the seafloor. Stretching along the Peruvian coast, the East Pisco Basin is known to host phosphorite beds of Miocene age. In this study, we investigate the petrographic, mineralogical and chemical composition of the Miocene phosphorite layers of the East Pisco Basin, and discuss the phosphogenetic mechanism(s) and sedimentary dynamics that led to the genesis and accumulation of P-rich deposits. Phosphorite samples were collected from layers overlaying stratigraphic unconformities within the Pisco Formation and subsequently analyzed through multiple methods: optical microscopy, XRD, XRF, SEM-EDS, EPMA, ICP-MS and LA-ICP-MS. Our results indicate that these layers consist of phosphatic clasts and nodules along with dolomite clasts, basement boulders, shark teeth, mollusk molds and vertebrate bone fragments. The P-rich clasts are mainly composed of phosphatic intraclasts and small phosphatic nodules. They occur associated with minor amounts of dolomite clasts coated by Fe-oxyhydroxides and laminites. Clasts and nodules are cemented by both early and late diagenetic cements, such as dolomite, calcite, silica, gypsum/anhydrite and baryte. Our observations suggest that the Pisco phosphorite deposits are transgressive lags laying on ravinement surfaces. They formed just below the sediment-water boundary during the early transgression phases, when the porewaters were enriched in phosphorus due to the abundance of organic-rich sediments and the microbial activity, as typical of upwelling settings. These deposits formed in shallow marine conditions, characterized by high biological productivity and low net sedimentation rates, with suboxic conditions at and beneath the sediment-water interface. The concentration of phosphate-coated clasts was then favored by a sedim

Software architectures are pivotal in the success of software-intensive systems and serve as foundational elements that significantly impact the overall software quality. Reference architectures abstract software elements, define main responsibilities and interactions within a domain, and guide the architectural design of new systems. Using reference architectures offers advantages like enhanced interoperability, cost reduction through reusability, decreased project risks, improved communication, and adherence to best practices. However, these benefits are most pronounced when software architectures align with reference architectures. Deviations from prescribed reference architectures can nullify these benefits. Uncontrolled misalignment can become prohibitively expensive, necessitating costly redevelopments, with maintenance costs reaching up to 90% of development costs. Conformance-checking processes and identifying and resolving violations in the software architecture are essential to mitigate misalignment. To address these challenges, we introduce the concept of continuous conformance that is expressed as a distance function, together with a process supporting it. Continuous conformance quantifies the degree to which a software architecture adheres to a designated reference architecture. The conformance concept enables multi-level, incremental, and non-blocking checking and restoration tasks and allows the check of partial architectures without obstructing the design process. We operationalize this process through an assistive modeling tool to architect an Internet of things-based system.

L'argomento che collega i lavori esposti in questa tesi è lo studio dei foraminiferi bentonici, uno strumento affidabile per le ricostruzioni paleoambientali e stratigrafiche. Inizialmente vengono discussi due articoli di revisione che offrono una panoramica generale sui produttori di carbonato nel Cenozoico. I risultati di questi lavori indicano l'importanza di un'analisi globale, quantitativa e standardizzata delle associazioni scheletriche nei contesti di carbonati di acque basse. Combinando i dati di letteratura sulle composizioni delle associazioni scheletriche nei sedimenti carbonatici moderni con i dati satellitari su vari fattori abiotici, sono stati identificati schemi nella distribuzione dei produttori di carbonato che seguono gradienti di energia solare e chimica. Questo suggerisce che associazioni scheletriche simili possono risultare da varie combinazioni di fattori. Pertanto, microfacies simili possono essere il risultato di diverse condizioni ambientali. Sulla base di un esteso set di dati bibliografici, abbiamo dimostrato che i grandi foraminiferi bentonici sono i produttori di carbonato più importanti volumetricamente lungo il Cenozoico, con un picco nell'Eocene. Ciò implica che lo studio di questi organismi è di primaria importanza per i progressi nella ricerca paleontologica e sedimentologica. Concentrandoci sui foraminiferi bentonici, è stato osservato come questi siano indicatori paleoecologici affidabili. È stata sviluppata una linea di ricerca sull'uso dei foraminiferi bentonici come indicatori di praterie fossili di fanerogame. Dato che questi ecosistemi sono notevolmente importanti, lo studio della loro evoluzione alla luce dei cambiamenti climatici attuali non può trascurare l'analisi del loro record fossile. Lo studio dei foraminiferi epifiti si è dimostrato uno strumento potente per queste analisi, come confermato dagli studi condotti nei depositi pleistocenici di Fauglia e del torrente Stirone. È stato possibile osservare come l, The topic connecting all the projects exposed in this thesis consists of the study and application of benthic foraminifera, which have proven to be a reliable tool for paleoenvironmental and stratigraphic reconstructions. Firstly, two review papers offering a general and quantitative analysis of different carbonate producers within the Cenozoic era, are discussed. The results of these works underscores the significance of a global, quantitative, and standardized analysis of skeletal assemblage compositions in shallow-water carbonate contexts. By combining literature data on skeletal assemblage compositions in modern carbonate sediments with satellite data on various abiotic factors, patterns in the distribution of carbonate producers relative to solar and chemical energy gradients were identified. These findings suggest that similar skeletal assemblages may result from various combinations of factors. Thus, similar microfacies may be the result of diverse environmental conditions. Based on an extensive literature dataset, we demonstrated that large benthic foraminifera are the most voluminous carbonate producers during the Cenozoic, with a peak in abundance during the Eocene. This implies that the study of these organisms is of paramount importance for advances in paleontological and sedimentological research. Focusing on benthic foraminifera, it has been observed how they can be reliably used as paleoenvironmental and paleoecological proxies. Consequently, a research line on the use of benthic foraminifera as proxies for paleo-seagrass has been developed. Since seagrass meadows are considerably important ecosystems, studying the evolution of these in light of current climate changes cannot disregard the analysis of their geological record. The study of epiphytic foraminifera has proven to be a powerful a tool for these analyses. This has been confirmed by studies conducted in the Pleistocene Fauglia and Stirone River deposits. It has been possible to observe how th

Collecting and analyzing data about developers working on their development tasks can help improve development practices, finally increasing the productivity of teams. Indeed, monitoring and analysis tools have already been used to collect data from productivity tools. Monitoring inevitably consumes resources and, depending on their extensiveness, may significantly slow down software systems, interfering with developers’ activity. There is thus a challenging trade-off between monitoring and validating applications in their operational environment and preventing the degradation of the user experience. The lack of studies about when developers perceive an overhead introduced in an application makes it extremely difficult to fine-tune techniques working in the field. In this paper, we address this challenge by presenting an empirical study that quantifies how developers perceive overhead. The study consists of three replications of an experiment that involved 99 computer science students in total, followed by a small-scale experimental assessment of the key findings with 12 professional developers. Results show that non-negligible overhead can be introduced for a short period into applications without developers perceiving it and that the sequence in which complex operations are executed influences the perception of the system response time. This information can be exploited to design better monitoring techniques.

We investigate the Palaeocene succession of the Hazara Basin (Northern Pakistan) to better understand the impact of climate change on marine carbonate-producing organisms. These shallow-water carbonates, deposited during the Late Palaeocene, before the onset of the Palaeocene-Eocene Thermal Maximum, were studied using a quantitative approach to highlight changes in the skeletal assemblage. We recognise a decrease in the abundance of colonial corals and green calcareous algae and an increase in larger benthic foraminifera and red calcareous algae from the early Thanetian to the late Thanetian. Increasing temperatures may represent a plausible cause for the decline of the more sensitive colonial corals in favor of the more tolerant larger benthic foraminifera. A similar pattern is observed in most successions deposited along the margins of the Neotethys Ocean, suggesting a connection with the Late Palaeocene environmental changes that heralded the PETM hyperthermal event. Our stratigraphic analysis of the Hazara Basin strata suggests that the biotic turnovers occurred during the Palaeocene – Eocene transition started already before the onset of the Palaeocene Eocene Thermal Maximum as recorded by the geochemical proxies.

The Oligocene-Miocene boundary marks a critical juncture characterized by remarkable alterations in the global climate. The transition from the relatively warm conditions of the late Oligocene to the onset of the Mi-1 glaciation, heralds changes in the composition of neritic carbonates and in the distribution patterns of zooxanthellate reef corals. In fact, while numerous Oligocene neritic carbonate successions worldwide feature abundant reef corals, our preliminary investigations, on a limited dataset primarily sourced from Europe, Iran, the Indo-Pacific, and the Caribbean, indicate a temporary decrease in their significance as carbonate producers after the Oligocene, reaching a minimum around the middle Miocene. Herein, we aim to elucidate the dynamics of the upper Oligocene coral-bearing units of the Southern Khirthar fold and thrust belt (S Pakistan), focusing on the understudied Jhill Limestone Unit (Gaj Formation). Through the analysis of four selected sections, the skeletal assemblages reveal a dominance of coralline algae, large benthic foraminifera (including Miogypsinoides, Miogypsina, Spiroclypeus, Nephrolepidina, and Archaias, alongside Operculina, Sorites, and sporadic Eulepidina), and reef corals (e.g., Hydnophora, Acropora, Porites). Reef corals are represented by colonies that form small patches in three out of four investigated sections, locally dominating the assemblages. Large benthic foraminifera suggest deposition during the Late Oligocene, potentially Shallow Benthic Zone 23, indicative of the Late Oligocene Warming Event, thus challenging previous assertions indicating a Burdigalian age for the Gaj Formation in the study area. These refined stratigraphic constraints facilitate comparative analyses of these coral-bearing shallow-water carbonates with analogous units from European and Indo-Pacific regions, providing a bridge between the two bio-provinces and a better understanding of the response of reef coral carbonates to past warming events.

Q-learning is an attractive option for GUI testing, allowing for sophisticated test generation strategies that learn and exploit effective GUI interactions. However, learning comprehensive models requires long test sessions. This issue is exacerbated by the needs of both testers, who might want to run multiple testing sessions to fine-tune the test strategy to their applications under test, and researchers, who might want to experiment with multiple alternative approaches. To address these concerns, this paper presents $GT^{PQL}$, a testing tool that supports GUI testing with a parallel deployment Q-learning, and that can be flexibly configured and extended with multiple state-space abstractions and Q-leaning variants.

Mutation testing is an established software quality assurance technique for the assessment of test suites. While it is well-suited to estimate the general fault-revealing capability of a test suite, it is not practical and informative when the software under test must be validated against specific requirements. This is often the case for embedded software, where the software is typically validated against rigorously-specified safety properties. In such a scenario (i) a mutant is relevant only if it can impact the satisfaction of the tested properties, and (ii) a mutant is meaningfully-killed with respect to a property only if it causes the violation of that property. To address these limitations of mutation testing, we introduce property-based mutation testing, a method for assessing the capability of a test suite to exercise the software with respect to a given property. We evaluate our property-based mutation testing framework on Simulink models of safety-critical Cyber-Physical Systems (CPS) from the automotive and avionic domains and demonstrate how property-based mutation testing is more informative than regular mutation testing. These results open new perspectives in both mutation testing and test case generation of CPS.

The advent of edge devices dedicated to machine learning tasks enabled the execution of AI-based applications that efficiently process and classify the data acquired by the resource-constrained devices populating the Internet of Things. The proliferation of such applications (e.g., critical monitoring in smart cities) demands new strategies to make these systems also sustainable from an energetic point of view. In this paper, we present an energy-aware approach for the design and deployment of self-adaptive AI-based applications that can balance application objectives (e.g., accuracy in object detection and frames processing rate) with energy consumption. We address the problem of determining the set of configurations that can be used to self-adapt the system with a meta-heuristic search procedure that only needs a small number of empirical samples. The final set of configurations are selected using weighted gray relational analysis, and mapped to the operation modes of the self-adaptive application. We validate our approach on an AI-based application for pedestrian detection. Results show that our self-adaptive application can outperform non-adaptive baseline configurations by saving up to 81% of energy while loosing only between 2% and 6 % in accuracy.

We present this paper as a broad group of Early Career Researchers (ECRs) active in various disciplines of the Geosciences in the Italian Academia (from palaeontology to underwater geomorphology and climate sciences). We offer some shared points of view on the Italian Academia and specifically on the Geosciences. We highlight the current precarious conditions of non-permanent workers within the Italian Academic system, so as to highlight the situation of Early Career Researchers (ECRs) in the Geosciences, and their general lack of perspective towards a permanent form of employment. Ultimately, this precariousness is one of the main obstacles impacting upon the quality of research. We tackle this aspect by analysing both the persisting gender gap and some perspectives on the future of the precarious figures currently working inside the Italian Academic system. We believe that the modern academic structure is generally unwelcoming to ECRs, to the detriment of high-quality research. Possible solutions, such as changing the current recruitment system, should include a better stabilisation mechanism for ECRs, which would also be essential to gaining a safe work-life balance.

The study of carbonate rocks is primarily reliant on microfacies analysis, which is strongly based on the comparison with modern allochem assemblages. Despite the existence of several models aimed at comprehensively explaining, on the bases of abiotic factors, the distribution of carbonate-producing organisms, a global, quantitative and standardized overview of the composition of shallow-water carbonate sediments is still missing. Aiming to address this gap in knowledge, the current study provides a global database of the available quantitative data on neritic carbonate sediments. This is paired with satellite-based observations for the abiotic parameters. The results highlight a non-linear, multi-variable, dependence in the distribution of allochems and suggest that depth, temperature, and trophic state are, to a certain extent, interchangeable. The implication of which is a level of non-uniqueness for paleoenvironmental interpretation. The resulting distribution is rather continuous and stretches along an energy gradient. A gradient extending from solar energy, with autotrophs and symbiont-bearing organisms to chemical energy with heterotrophs. Further, quantitative data from modern oceans are still required to disentangle the remaining elements of uncertainty.

There are several tunnelling projects underway in Europe and worldwide to facilitate connections between distant areas or ones with geographical constraints (e.g. mountains). As a result, the tunnel industry has been producing huge amounts of excavated material (spoil material: SM), which can be re-used for different purposes (including green areas). Adding lime can be a practical procedure at a construction site for the stabilization of SM and makes it possible to manage it better at its final destination site. This work reports a nature-based solution (NBS) for decreasing the highly basic pH (12.53 ± 0.01) of the SM after adding lime, using three different organic fertilizers which are by-products of local olive oil (pomace), compost and digestate production. For this purpose, microcosm experiments were performed with SM amended with compost, pomace or digestate. Half of the microcosms were also seeded with Medicago sativa to evaluate its growth in terms of biomass. The changes in pH and in the structure and functional potential of the microbial community were evaluated in the amended SM at the start and end (4 months) of the experiment. Aerial and root plant biomass and concentrations of leaf chlorophyll and total phenolic compounds were also measured. The microbial biodiversity was evaluated using amplicon sequencing (16S rRNA gene) and a predictive functional analysis was performed by processing sequencing data with the PICRUSt2 tool. Adding the amendments to SM significantly decreased the pH values by 4 units and introduced organic carbon, nitrogen, and active microbial populations. Although pomace promoted the lowest pH decrease (7.98 ± 0.18) and the highest microbial abundance and activity, it did not favour the highest plant growth. Adding compost and digestate to SM can be considered the best NBS for decreasing pH and supporting plant development. M. sativa did not have any direct effect on lowering pH, demonstrating in this study the key role of microorga

This special section contains the extended version of four papers from the 32th IEEE International Symposium on Software Reliability Engineering (ISSRE 2021).

Cloud services must be continuously monitored to guarantee that misbehaviors can be timely revealed, compensated, and fixed. While simple applications can be easily monitored and controlled, monitoring non-trivial cloud systems with dynamic behavior requires the operators to be able to rapidly adapt the set of collected indicators. Although the currently available monitoring frameworks are equipped with a rich set of probes to virtually collect any indicator, they do not provide the automation capabilities required to quickly and easily change (i.e., deploy and undeploy) the probes used to monitor a target system. Indeed, changing the collected indicators beyond standard platform-level indicators can be an error-prone and expensive process, which often requires manual intervention. This article presents a Monitoring-as-a-Service framework that provides the capability to automatically deploy and undeploy arbitrary probes based on a user-provided set of indicators to be collected. The life-cycle of the probes is fully governed by the framework, including the detection and resolution of the erroneous states at deployment time. The framework can be used jointly with existing monitoring technologies, without requiring the adoption of a specific probing technology. We experimented our framework with cloud systems based on containers and virtual machines, obtaining evidence of the efficiency and effectiveness of the proposed solution.

Microservice-based applications consist of multiple services that can evolve independently. When a service must be updated, it is first tested with in-house regression test suites. However, the test suites that are executed are usually designed without the exact knowledge about how the services will be accessed and used in the field; therefore, they may easily miss relevant test scenarios, failing to prevent the deployment of faulty services. To address this problem, we introduce ExVivoMicroTest, an approach that analyzes the execution of deployed services at run-time in the field, in order to generate test cases for future versions of the same services. ExVivoMicroTest implements lightweight monitoring and tracing capabilities, to inexpensively record executions that can be later turned into regression test cases that capture how services are used in the field. To prevent accumulating an excessive number of test cases, ExVivoMicroTest uses a test coverage model that can discriminate the recorded executions between the ones that are worth to be turned into test cases and the ones that should be discarded. The resulting test cases use a mocked environment that fully isolates the service under test from the rest of the system to faithfully reply interactions. We assessed ExVivoMicroTest with the PiggyMetrics and Train Ticket open source microservice applications and studied how different configurations of the monitoring and tracing logic impact on the capability to generate test cases.

Monitoring is a critical component in fog environments: it promptly provides insights about the behavior of systems, reveals Service Level Agreements (SLAs) violations, enables the autonomous orchestration of services and platforms, calls for the intervention of operators, and triggers self-healing actions. In such environments, monitoring solutions have to cope with the heterogeneity of the devices and platforms present in the Fog, the limited resources available at the edge of the network, and the high dynamism of the whole Cloud-To-Thing continuum. This paper addresses the challenge of accurately and efficiently monitoring the Fog with a self-Adaptive peer-To-peer (P2P) monitoring solution that can opportunistically adjust its behavior according to the collected data exploiting a lightweight rule-based expert system.Empirical results show that adaptation can improve monitoring accuracy, while reducing network and power consumption at the cost of higher memory consumption.

We introduce FIM, an open-source toolkit for automated fault injection and mutant generation in Simulink models. FIM allows the injection of faults into specific parts, supporting common types of faults and mutation operators whose parameters can be customized to control the time of fault actuation and persistence. Additional flags allow the user to activate the individual fault blocks during testing to observe their effects on the overall system reliability. We provide insights into the design and architecture of FIM, and evaluate its performance on a case study from the avionics domain.