Search

Several organizations are utilizing cloud technologies and resources to run a range of applications. These services help businesses save on hardware management, scalability and maintainability concerns of underlying infrastructure. Key cloud service providers (CSPs) like Amazon, Microsoft and Google offer Infrastructure as a Service (IaaS) to meet the growing demand of such enterprises. This increased utilization of cloud platforms has made it an attractive target to the attackers, thereby, making the security of cloud services a top priority for CSPs. In this respect, malware has been recognized as one of the most dangerous and destructive threats to cloud infrastructure (IaaS). In this paper, we study the effectiveness of Recurrent Neural Networks (RNNs) based deep learning techniques for detecting malware in cloud Virtual Machines (VMs). We focus on two major RNN architectures: Long Short Term Memory RNNs (LSTMs) and Bidirectional RNNs (BIDIs). These models learn the behavior of malware over time based on run-time fine-grained processes system features such as CPU, memory, and disk utilization. We evaluate our approach on a dataset of 40,680 malicious and benign samples. The process level features were collected using real malware running in an open online cloud environment with no restrictions, which is important to emulate practical cloud provider settings and also capture the true behaviour of stealth and sophisticated malware. Both our LSTM and BIDI models achieve high detection rates over 99% for different evaluation metrics. In addition, an analysis study is conducted to understand the significance of input data representations. Our results suggest that in particular cases, input ordering does have some affect on the performance of the trained RNN models.

Technical Debt (TD) is a software engineering metaphor that resembles the production of poor- quality code to going into debt. In particular, a development team that ‘‘saves’’ effort while developing by not removing inefficiencies, has to ‘‘pay-back’’ with interest, in the form of additional maintenance costs (i.e., fixing bugs, adding features, etc.) due to the poor maintainability of the developed code. Although maintainability assurance is an established practice in traditional software development (lately known as TD management), it has still not attracted the attention of scientific software developers; i.e., researchers writing code and developing tools for purely research purposes. Nevertheless, based on the literature and practice, maintainability seems to be ranked as an important key-driver for the development of such applications; since the effort needed to update the code before the experimentation (e.g., executing a simulation) is common and should not receive low priority. In this paper, we present the outcome of a 3-year research project on Technical Debt Management (TDM) for scientific software development. The outcome of the project is a framework (termed: EXA2PRO TDM framework) and an accompanying platform for assisting scientific software developers in managing the TD of their applications. The framework is a collection of methods tailored for the mainstream programming languages of scientific software development, which have been empirically validated through five pilot applications. The majority of the EXA2PRO framework suggestions have been applied by scientific software developers and eased future maintenance activities.

Building shared understanding of requirements is key to ensuring downstream software activities are efficient and effective. Non-functional requirements (NFR), which include performance, availability, and maintainability, are vitally important to overall software quality. Research has shown NFRs are, in practice, poorly defined and difficult to verify, especially in agile environments. Continuous software engineering (CSE) practices, which extend agile practices, emphasize fast paced, automated, and rapid release of software that poses additional challenges to NFRs. However, the level of shared understanding achieved across an organization is not well-understood. This dissertation will build the foundations towards a theory of the complex and intricate relationship between shared understanding of NFRs and CSE.

In this paper, we report on a case study in an industrial setting where code is generated from models, and, for various reasons, that generated code is then manually modified. To enhance the maintainability of both models and code, consistency between them is imperative. A first step towards establishing that consistency is to identify the manual changes that were made to the code after it was generated and deployed. Identifying the delta is not straightforward and requires pre-processing of the artifacts. The main mechanics driving our solution are higher-order transformations, which make the implementation scalable and robust to small changes in the modeling language. We describe the specific industrial setting of the problem, as well as the experiences and lessons learned from developing, implementing, and validating our solution together with our industrial partner.

Considerable studies have been carried out in recent years regarding fault diagnosis and prediction for the rotating machinery in industrial plants. However, few works present the use of clustering approaches applied to time series to diagnose machine faults. With the increasing practical requirement of safety, reliability, availability and maintainability of machinery running, predictive maintenance based on the technologies of fault diagnosis and prediction has also received significant attention in recent years. In the present study, under Cyber-physical systems (CPS) condition, k-means clustering analysis based on the fault case big data machine learning is applied to investigate the fault identification of the rotating machinery without external expert support. K-means cluster-based fault identification model, which includes the k-means cluster analysis module, fault mode – fault cluster centroid knowledge base module and fault identification module has been constructed. Moreover, the fault feature extraction and fault eigenvectors screening are studied in detail. The vibration data of surge, rubbing, misalignment and normal status of the centrifugal compressor in industrial plants are utilized to train and verify the effectiveness of the k-means cluster fault recognition model. The obtained result shows that recognition accuracy rates of the surge, rubbing and misalignment faults reach 94%, 100% and 80%, respectively. However, the effectiveness of the cluster analysis of vibration data for five or more operating states should be studied in the future.

Many different code snippets can implement the same software feature. However, a significant subset of these possible solutions contains difficult-to-understand code that harms the software's maintainability and evolution. Such low-quality code snippets directly harm profit, as frequent and fast code change enables businesses to seize new opportunities. Unfortunately, they are also prevalent in an industry that consists mostly of junior programmers. We developed a platform called Clean CaDET to tackle the prevalence of low-quality code from two angles. The Smell Detector module presents a framework for integrating AI-based code quality assessment algorithms to identify low-quality code as the programmer is writing it. The Smart Tutor module hosts a catalog of educational content that helps the programmer understand the identified issue and suggests possible solutions. By combining the quality assessment with the educational aspect, our integrated solution presents a novel approach for increasing the quality of code produced by our industry.

Over the last decade, migration methodologies from Legacy to SOA design have been broadly explored. Language migration, database migration, and platform migration were the primary issues with legacy systems. Legacy systems are a time-consuming and costly process for maintaining application and programming source code. It is difficult to change a single module of the software system without affecting other modules. SOA, on the other hand, enables organizations to manage business processes, business rules, enterprise content management, and management security. In this study A survey is carried out on the SOA maintainability challenges namely Analyzability, Changeability, Stability and Testability.

The paper discusses the problem of statistical characteristics substantiation during radio equipment diagnostics. To provide serviceability and maintainability of radio equipment is one of the tasks of operation system. The operation system contains the processes of intended use of equipment, maintenance, running repair, continuation of life service and others. During running repair, the procedures of diagnostics, operability recovery and parameters monitoring are performed. The diagnostics process regulates the sequence of searching the failed element in the radio equipment scheme. The decision-making during diagnostics is associated with the errors of the first and second kind. So for efficiency analysis, the probability of correct diagnostics can be used. Such efficiency measure has random nature, but in the literature, only expected value is used to describe it. This paper concentrates on the approach to calculation of the probability density function for efficiency measure with purpose of obtaining the complete information about random value.

Verification time is a major factor in determining the time to market of the product. In order to save time, reusable test benches are developed and are modified as and when the design undergoes changes. As new features are added in RTL, new configurations and functions are added in the test bench to enable verification of the new features. The test bench is nothing but a source code in itself. The coding style determines the amount of resources that will be used during simulation. Poorly coded logic results in increased consumption of memory and time leading to degraded simulation performance. Since a number of engineers work on a piece of code, it is essential to write readable and easily understandable code. Therefore, it is essential to keep in mind the best coding practices, readability and maintainability of the code while developing the test bench. This work proposes to enhance the coding style and improve the readability and maintainability of the test bench used to verify the L2 cache which is part of a larger design. Profiling and certain switches provided by the simulator were used to identify the performance issues pertaining to simulation. Assertions with large memory footprint were identified and the memory usage decreased by 8.675% after the changes. The address randomization logic was optimized post which the CPS improved by 26.38%. The code for preloading the cache was enhanced to improve the readability and maintainability.

In the automated Production Systems (aPS) domain, companies need to continuously decrease their systems’ development times while maintaining quality to stay globally competitive. Industry 4.0 imposes additional boundary conditions, e.g., a high degree of customization, that need to be met by aPS, which frequently have to be adapted to changing requirements during their long life cycles. Since control software implements an increasing share of aPS functionality, reusing well-tested software modules can significantly contribute to saving development time and increasing its comprehensibility and maintainability. However, planned reuse of control software modules still represents a major challenge in practice, e.g., the data exchange between software modules causes dependencies, which are often not directly visible and, thus, hinder reuse. This paper presents a metric-based concept for assessing the reusability of control software modules, focusing on different implementation types of indirect data exchange. Depending on company-specific boundary conditions, the approach can be integrated at various development process steps for continuous or one-time reuse assessment. The concept has been developed and evaluated with two industrial software projects and continuous feedback from domain experts.

Design patterns have a key role in software development process. However, their impact on software quality is not well investigated. There is no common agreement on how design patterns impact software quality. This paper aims to empirically investigate whether the Adapter design pattern impacts software maintainability positively or negatively. To achieve this aim, refactoring techniques have been applied to generate a non-pattern version for four subject systems. Then, software metrics have been calculated and compared for both versions; the pattern version and non-pattern version. The correlation of software metrics to software maintainability have been used and adopted from previous research studies. The empirical results show that the Adapter pattern versions have a better software metrics than do the non-Adapter pattern versions i.e. a sign that the Adapter design pattern impacts software maintainability positively.

Source code quality plays a key role in software quality mainly due to its impact on software maintainability. Software engineers have been using source code metrics to support them to assess source code quality. Source code metrics quantify different source code characteristics. However, source code metric analysis still involves subjectivity. For instance, it is not trivial to decide whether a metric value is high or low. To reduce the eventual subjectivity of source code metrics analysis, several researchers are using Machine Learning algorithms. Therefore, in this paper, we designed a Fuzzy-based approach to extract characteristics and patterns present in source code versioning repositories in order to: i) assist the specialist in the interpretation of releases, especially when working with large volumes of source code; ii) from the release interpretation, specialists can improve the quality of the source code; and iii) monitor the evolution of the software as new releases are submitted to the repositories. We evaluated the proposed approach with the Linux Test Project repository, emphasizing the interpretability of large source code versioning repositories.

E-AgriFinance mobile application is a digital platform that integrates activities related to farming which includes supply of input, production, marketing, distribution, and financial services. In this study, researchers focused on determining the relationship between system quality attributes and intention to use the e-AgriFinance mobile application from the perspective of suppliers. This study also examines the moderating effect of information and communication technology (ICT) readiness on the relationship between the system quality attributes and the intention of use of the mobile application by the suppliers of the agriculture sector. This study uses the TAM and ISO 25010 model, while the variable used are Perceived Usefulness, Perceived Ease of Use, Perceived Security, and Perceived Maintainability. A total of 276 questionnaires were collected in this study, and they were analysed using partial least squares structural equation modeling (PLS-SEM). Data were collected from the suppliers of the agriculture sector in Sarawak. The results of this study indicate that six of the eight research hypotheses are supported. This research contributes to the body of literature by providing empirical understanding and insights on system quality attributes of mobile application so that the agriculture sector suppliers will maximise the usage of the mobile application.

God class refers to a class that undertakes too many responsibilities for tasks that should more appropriately be handled by multiple classes. The existence of god classes seriously affects the maintainability and understandability of software. To eliminate god class, we first need to identify them. Researchers have proposed traditional methods using code metrics and deep learning methods using code metrics and text information to detect god classes. However, the relationship existing in metrics and text information is often ignored; moreover, deep learning methods require a large number of reliable datasets, while authentic god class datasets are scarce. To solve the above problems, we propose a novel god class detection method based on multi-aspect interactions and dataset fine-tuning. First, we use proposed model to extract multi-aspect interaction information, including three parts: (i) the interaction information existing in code metrics; (ii) the interaction information existing in texts; (iii) the interaction information existing in texts and code metrics. In this way, we can not only make use of code metrics and text information, but also fully exploit the multi-aspect interaction information. Second, we train with large-scale synthetic datasets to obtain a pre-trained model, then fine-tune the pre-trained model parameters with high-quality authentic datasets. Using the training method of pre-training and fine-tuning, we can solve the problem of low-reliability synthetic datasets and scarce authentic datasets. Finally, evaluation results on open-source applications suggest that the proposed approach improves on the state-of-the-art.

Quantum circuit simulators have a long tradition of exploiting massive hardware parallelism. Most of the times, parallelism has been supported by special purpose libraries tailored specifically for the quantum circuits. Quantum circuit simulators are integral part of quantum software stacks, which are mostly written in Python. Our focus has been on ease of use, implementation and maintainability within the Python ecosystem. We report the performance gains we obtained by using CuPy, a general purpose library (linear algebra) developed specifically for CUDA-based GPUs, to simulate quantum circuits. For supremacy circuits the speedup is around 2x, and for quantum multipliers almost 22x compared to state-of-the-art C++-based simulators., accepted at 2nd International Workshop on Quantum Computing: Circuits Systems Automation and Applications (QC-CSAA) in conjuction with the ISVLSI 2021

Microservices-based architectures gain more and more attention in industry and academia due to their tremendous advantages such as providing resiliency, scalability, composability, etc. To benefit from these advantages, a proper architectural design is very important. The decomposition model of services into microservices and the granularity of these microservices affect the different aspects of the system such as flexibility, maintainability, performance, and security. An inappropriate service decomposition into microservices (improper granularity) may increase the attack surface of the system and lower its security level. In this paper, first, we study the probability of compromising services before and after decomposition. Then we formulate the impacts of possible service decomposition models on confidentiality, integrity, and availability attributes of the system. To do so, we provide equations for measuring confidentiality, integrity, and availability risks of the decomposed services in the system. It is also shown that the number of entry points to the decomposed services and the size of the microservices affect the security attributes of the system. As a use case, we propose three different service decomposition models for the 5G NRF (Network Repository Function) and calculate the impacts of these decomposition models on the confidentiality, integrity, and availability of the system using the provided equations.

In situ food production capability is a key achievement required to enable long term human space exploration. Making food production sustainable in space is not only a matter of increasing crop yields while reducing necessary consumables, power consumption and buffers, coping with the microgravity or planetary space environment. Safety, reliability, repeatability, maintainability, logistics, failures management, biocontamination control are the key enabling aspects for sustainable food production technologies. Starting from the experience gained in Thales Alenia Space Italia from multiple studies in the ASI, ESA and EC framework, as well as from internal research activities, the lessons learnt concerning these key enabling aspects were analyzed. A path toward sustainable food production in space emerged from this analysis, impacting the currently ongoing ESA PFPU projects. This path runs along with analog applications for a sustainable future on Earth.

In recent years many companies shift from product-based to service-based business models. This includes aerospace and defense industries. This new trend of servitization moves the risk of system downtime and higher servicing costs from customers to contractors and increases the need for more precise estimations on in-service support of systems. The fundamentals of in-service support (ISS) planning is not a new concept. Aerospace and defense has set foundations with the introduction of Integrated Logistics Support (ILS), a discipline within Systems Engineering, in the 1960s. Nowadays, ILS (also Supportability Engineering or Logistics Engineering) anticipates servitization and other trends by transforming into Integrated Product Support (IPS). Decisions for ISS in utilization are taken in the early system life cycle phases. Uncertainty in these phases has a negative effect and represents a threat to ILS and system performance in utilization. The use of reliable measures for ILS is needed in early life cycle. However, no ILS specific measures for early life cycle phases have been established. Instead, system measures for reliability, maintainability, availability, affordability (or life cycle costing), etc. are also used for measuring ILS performance. Unfortunately, it is not documented if established system measures work for ILS. This paper is addressing the issue. Established system measures were qualitatively reviewed for their applicability in ILS in early life cycle phases and utilization. Found weaknesses are described. As a result, the need for ILS specific measures is identified.

We present the application of FAIR principles to photovoltaic time series data to increase their reusability within the photovoltaic research community. The main requirements for a "FAIRified" dataset is to have a clearly defined data format, and to make accessible all metadata for this dataset to humans and machines. To achieve FAIRification, we implement a data model that separates the photovoltaic data and its metadata. The metadata and their descriptions are registered on a data repository in a human and machine readable format, using JSON-LD. Also, secure APIs are developed to access photovoltaic data. This approach has long term scalability and maintainability.

In this paper, the MVC framework was first introduced and the common paper constructing algorithm was analyzed in detail. Then, the paper proposed the online exam system design idea and main functional module based on the MVC framework, as well as analyzed and designed the system. Specifically, the online exam system gets involved in test base management, examinee management, paper inspection management, random paper generation, exam management, paper inspection management, inquiry statistics, examination paper backup, score management, and system management. The system applies the MVC framework and also adopts the three-tier network structure application system based on B/S mode to enhance the system maintainability and practicability.

We propose a concept of the use of dynamic uncertainty to improve the accuracy and robustness of digital sensor networks. The digital smart transducer is integrated with a microcontroller that reads the binary data from the calibrated sensor, performs an analysis to determine the measurand and its instantaneous uncertainty, and communicates these values in SI units with a time stamp to the host computer. We postulate that communicating the measurement with its uncertainty from the sensor node could eliminate the need for the host computer to maintain a database of the sensor models and performance parameters while at the same time improving accuracy and maintainability. We demonstrate this protocol using calibrated triaxial accelerometers as a test case. This protocol could eliminate the need for the host computer(s) to keep track of individual sensor performance parameters and facilitate the functionality to seamlessly upgrade sensors in the network.

The unprecedented scale and ubiquity of the Internet of Things (IoT) introduce a maintainability challenge. IoT networks operate in diverse and harsh environments that impose nonnegligible thermal stress on the IoT devices with no active cooling. The lifetime of these networks can be limited by the exacerbated effects of hardware failure mechanisms at high temperatures, exponentially accelerating reliability degradation. In this paper, we propose a novel reliability-driven routing approach to mitigate the reliability degradation of IoT devices and improve the network Mean Time to Failure (MTTF). Through routing, we curb the utilization of highly degrading devices, which helps to lower the device power dissipation and temperature and reduce the effect of temperature-driven failure mechanisms. The routing protocol makes the decisions based on the current reliability of the devices, the amount of degradation they will experience due to communication activity, and networking performance. We enhance the ns-3 network simulator to support our reliability modeling and evaluate the routing performance by comparing with state-of-the-art approaches.

The front-end text processing module is considered as an essential part that influences the intelligibility and naturalness of a Mandarin text-to-speech system significantly. For commercial text-to-speech systems, the Mandarin front-end should meet the requirements of high accuracy and low time latency while also ensuring maintainability. In this paper, we propose a universal BERT-based model that can be used for various tasks in the Mandarin front-end without changing its architecture. The feature extractor and classifiers in the model are shared for several sub-tasks, which improves the expandability and maintainability. We trained and evaluated the model with polyphone disambiguation, text normalization, and prosodic boundary prediction for single task modules and multi-task learning. Results show that, the model maintains high performance for single task modules and shows higher accuracy and lower time latency for multi-task modules, indicating that the proposed universal front-end model is promising as a maintainable Mandarin front-end for commercial applications.

Known attempts to build autonomous robots rely on complex control architectures, often implemented with the Robot Operating System platform (ROS). The implementation of adaptable architectures is very often ad hoc, quickly gets cumbersome and expensive. Reusable solutions that support complex, runtime reasoning for robot adaptation have been seen in the adoption of ontologies. While the usage of ontologies significantly increases system reuse and maintainability, it requires additional effort from the application developers to translate requirements into formal rules that can be used by an ontological reasoner. In this paper, we present a design tool that facilitates the specification of reconfigurable robot skills. Based on the specified skills, we generate corresponding runtime models for self-adaptation that can be directly deployed to a running robot that uses a reasoning approach based on ontologies. We demonstrate the applicability of the tool in a real robot performing a patrolling mission at a university campus.

This paper presents a probabilistic model-checking tool for FIGARO, a multi-formalism modelling language that includes e.g., generalised stochastic Petri nets, Boolean-logic driven Markov processes, telecommunication networks, dynamic reliability block diagrams, process diagrams, and electric circuits. FIGARO has been developed and maintained by EDF for the analysis of system dependability such as reliability, availability and maintainability. We present a probabilistic model-checking tool for FIGARO models. It combines efficient, fully automated verification algorithms with numerical analysis techniques. Whereas the existing FIGARO tools, the Monte Carlo simulator YAMS and the most-probable-sequence explorer FiGSEQ, provide respectively statistical guarantees and upper bounds for unreliability and unavailability, our tool provides hard guarantees: its results are correct up to a given numerical accuracy. The key ingredient is the tool-component FiGAROAPI that enables the state-space generation for FIGARO models thus facilitating model checking. This paper describes the details of FiGAROAPI and empirically evaluates the feasibility and merits of the proposed framework. FiGAROAPI leverages upon the state-of-the-art STORM model checker as back-end, and it can model check various types of formalism in their FIGARO representation.

Supercapacitor battery stacks (SBS) are widely used in energy storage systems and electric vehicles. The stricter requirements for their reliability, safety and reduction of unexpected failures necessitate an analysis of their maintainability, availability and expected failures. An analysis of the maintainability and availability of supercapacitor battery stacks is particularly important when these devices are used in systems with uninterruptible processes and in electric vehicles. For the evaluation of the availability and expected failures of SBS it is necessary to apply three types of analysis – an analysis of failure rates of supercapacitors or SBSs, a probability analysis (also called Markov Analysis) and Theory for Preventive Maintenance to define the time to perform the maintenance, repair activities and probability for failure-free operation of the SBSs after performed activities. In combining these three types of analysis it is possible to determine the probability states, expected failures, effectiveness of possible protection circuits, maintenance activity and redundancy of SBS.

Microservices are a powerful new way of building, customizing, and deploying distributed services owing to their flexibility and maintainability. Several large-scale distributed platforms have emerged to serve the growing needs of data-centric workloads and services in commercial computing. Concurrently, high-performance computing (HPC) systems and software are rapidly evolving to meet the demands of diversified applications and heterogeneity. The interplay of hardware factors, software configuration parameters, and the flexibility offered with a microservice architecture makes it nontrivial to estimate the optimal service instantiation for a given application workload. Further, this problem is exacerbated when considering that these services operate in a dynamic and heterogeneous HPC environment. An optimally integrated service can be vastly more performant than a haphazardly integrated one. Existing performance tools for HPC either fail to understand the request-response model of communication inherent to microservices or they operate within a narrow scope, limiting the insight that can be gleaned from employing them in isolation.We propose a methodology for integrated performance analysis of HPC microservices frameworks and applications called SYMBIOSYS. We describe its design and implementation within the context of the Mochi framework. This integration is achieved by combining distributed callpath profiling and tracing with a performance data exchange strategy that collects fine-grained, low-level metrics from the RPC communication library and network layers. The result is a portable, low-overhead performance analysis setup that provides a holistic profile of the dependencies among microservices and how they interact with the Mochi RPC software stack. Using HEPnOS, a production-quality Mochi data service, we demonstrate the low-overhead operation of SYMBIOSYS at scale and use it to identify the root causes of poorly performing service configurations.

The cloud-native system is shifting from traditional monolithic architecture to microservice architecture because of loosely coupling, better maintainability and availability, faster deployment, and richer ecology brought by it. Except for these advantages, it still has an inevitable weakness–the communication over RPC (Remote Procedure Call) between services makes the system performance more unpredictable. Moreover, the complex interactions amongst services make it hard to reveal the root cause of performance issues. To address this challenge, we propose a lightweight spectrum-based performance diagnosis tool, named T-Rank. T-Rank provides the ranked suspicious score in a list of microservices to localize root causes with very few resources. We demonstrate the high accuracy and the low cost of T-Rank by conducting experiments with the data collected from a real-world production microservice system. Moreover, comparison results show that T-Rank outperforms other state-of-the-art approaches.

Extending the work of (Prasetyo &Rosita, 2020) in this study, the application of Reliability Centered Maintenance (RCM) to optimize equipment reliability is presented through a framework developed for ABC manufacturing company. ABC company is a well-established manufacturing company in the middle east that already has their current maintenance strategy to maximize their equipment reliability but based on the data collected from SAP on where they stored their historical data from maintenance and operation, their MTBF is low and their MTTR is quite high. This is because of their high machine downtime hours and repeated failures. Reliability Centered Maintenance (RCM) integrates Preventive Maintenance (Interval-based), Reactive Maintenance, Proactive Maintenance and Condition Based Maintenance to increase the machines performance and the component will function in the required manner over its design life-cycle with a very less amount of machine downtime and parts failure. To implement RCM in ABC manufacturing company and apply the developed framework, the failure modes collected from SAP was analyzed using Failure Mode and Effect Analysis (FMEA) with the help of RCM Logic tree analysis for decision making and establishing maintenance criticality criteria. The last part of this paper shows the possible maintenance selection task for each failure modes that the company can consider, challenges in implementing RCM are also explained which might influence the plan of adopting the RCM process in an already established organization. It is also worth mentioning that the developed framework can be apply also in different manufacturing industries that want to implement Reliability Centered Maintenance (RCM).

Distributed Denial-of-Service (DDoS) attacks can occur anytime, everywhere, and most normally occur with little or no warning. Most small and medium businesses (SMBs) usually are not prepared to deal with this type of attack. The companies must have at least a bandwidth higher than the attack, an infrastructure with redundant components, regular backups, and firewalls capable of monitoring the threats. Otherwise, the services provided by the companies’ support can be interrupted, increasing the chances of financial losses. Hierarchical modeling approaches are often used to evaluate the availability of such systems. It can represent different failures and repair events in distinct parts of the system. In this way, this paper proposes hierarchical models that describe the behavior of major IT systems and IoT device components and assess the DDoS effects on system availability. Therefore, we evaluate the impact of the DDoS attacks on computing systems using IoT devices in attack amplification. We assessed equations that estimate the attack feasibility, pain factor, attack propensity, attacker benefits, and technical ability. They enable a direct analytical solution for large systems. The attack tree indices show the impact of simultaneous attacks on a computer system and the several threats that will maximize the system downtime. The attack tree investigation results allow for planning and improving the system’s availability, maintainability, and reliability.

Automated Program Repair (APR) is an area of research focused on the automatic generation of bug-fixing patches. Current APR approaches present some limitations, namely overfitted patches and low maintainability of the generated code. Several works are tackling this problem by attempting to come up with algorithms producing higher quality fixes. In this experience paper, we explore an alternative. We believe that by using existing low-costAPR techniques, fast enough to provide real-time feedback, and encouraging the developer to work together with the APR inside the IDE, will allow them to immediately discard proposed fixes deemed inappropriate or prone to reduce maintainability. Most developers are familiar with real-time syntactic code suggestions, usually provided as code completion mechanisms. What we propose are semantic code suggestions, such as code fixes, which are seldom automatic and rarely real-time. To test our hypothesis, we implemented a Visual Studio Codeextension (named pAPRika), which leverages unit tests as specifications and generates code variations to repair bugs in JavaScript. We conducted a preliminary empirical study with 16 participants in a crossover design. Our results provide evidence that, although incorporating APR in the IDE improves the speed of repairing faulty programs, some developers are too eager to accept patches, disregarding maintenance concerns.