Combinatorial Interaction Testing (CIT) is an established software testing method having a wide application area. While many studies have been conducted to investigate the effectiveness of CIT through various simulations and mutation testing, there needs to be more evidence regarding its effectiveness in real industrial testing processes. In response to this certain gap, this work investigates the practical implications of CIT application in a real software project with real historical defects as well as artificial defects created to resemble historical defects. The evidence presented encompasses two distinct studies: firstly, the optimization of test data input into user interface forms, and secondly, the management of system configurations for testing purposes. These studies employ the Tricentis Test Automation for ServiceNow (TTA-SNOW) outcome as the system under evaluation. We cautiously evaluated the impact of CIT tool on various metrics, such as time spent by test design, test automation and test execution, test suite size, and defect detection compared to an intuitive approach taken by test engineers without proper application of CIT. Our findings demonstrate that CIT has the potential to reduce test suite size and execution time while maintaining or improving defect detection rates. Specifically, in Study 1, CIT reduced average defect detection time from 5.11 hours to 3.39 hours. In Study 2, focusing on system configurations, this time was reduced from 4.38 hours to 2.28 hours. These findings provide valuable insights into CIT’s practical benefits in industrial contexts.

In Wireless Sensor Networks (WSN) communication protocols, rule-based approaches have been traditionally used for managing caching and congestion control . These approaches rely on explicitly defined, unchanging models. Recently, a trend has been toward incorporating adaptive methods that leverage machine learning (ML), including its subset deep learning (DL), during network congestion conditions. However, an adaptive cache-aware congestion control mechanism using Deep Reinforcement Learning (DRL) in WSN has not yet been explored. Therefore, this study developed a DRL-based adaptive cache-aware congestion control mechanism called DRL-CaCC to alleviate WSN during congestion scenarios. The DRL-CaCC uses intermediate caching parameters as its state space and adaptively moves the congestion window as its action space through the Rapid Start and DRL algorithms . The mechanism aims to find the optimal congestion window movement to avoid further network congestion while ensuring maximum cache utilization. Results show that DRL-CaCC achieved an average improvement gain between 20% and 40% compared to its baseline protocol, RT-CaCC. Finally, DRL-CaCC outperformed other caching-based and DRL-based congestion control protocols in terms of cache utilization, throughput, end-to-end delay, and packet loss metrics, with improvement gains between 10% and 30% in various congestion scenarios in WSN.

Path-based testing is an established method for creating test cases comprising sequences of steps executed in a System Under Test (SUT). Several algorithms for generating the test sequences (paths) that satisfy various test coverage criteria determining their properties are published in the literature. However, existing path-based testing techniques have limited applicability in numerous practical cases, such as when executing a particular test step in the test further excludes the execution of another test step. More complex exclusion requirements (further denoted negative constraints) exist in real systems, depending on the number of executions of a particular test step. In the paper, we discuss two possible negative constraints, namely, (1) the complete exclusion of a step as a consequence of the execution of a particular previous step and (2) the requirement to include a particular step maximally once in one test path, when another step was executed previously. We present a novel ant-colony-optimization (ACO) principle-based algorithm, accepting a SUT model based and a set of negative constraints, and computing a set of test paths while maximizing edge coverage and satisfying the given set of negative constraints. We compare the results of the ACO-based algorithm with those returned by a baseline, an alternative algorithm that excludes specific test paths from a set of test paths satisfying edge coverage, and, for reference, with results returned by an algorithm that generates test paths that satisfy edge coverage. Evaluated on 152 problem instances, the presented ACO-based algorithm outperformed the baseline in the average length of test paths (representing the testing costs) lower by 32.62%. Also, the ratio of edge coverage satisfaction in the set of test paths computed by the ACO-based algorithm is better by 3.41% compared to the baseline.

Due to the emergence of sustainable smart homes, each smart device requires more bandwidth putting pressure on the existing home networks. A very good solution to ensure high-bandwidth home networks is the fiber-to-the-home (FTTH) technology. FTTH delivers high-speed Internet from a central point directly to the home through fiber optic cables. This fixed broadband network can transmit information at virtually unlimited speed and capacity enabling homes to be smarter. Hence, a well-monitored and well-maintained FTTH broadband network is necessary to obtain a high level of service availability and sustainability in smart homes. This study aims to develop a predictive model that will proactively monitor and maintain FTTH networks through the use of sophisticated modeling techniques such as machine learning (ML). The predictive model targets to classify the proposed technician resolution based on the historical FTTH field data set. The results show that the K-nearest neighbors (KNN)-based model obtained the highest accuracy of 89% followed by the feedforward artificial neural network (FF-ANN)-based model with 86%. In addition, the identified anomalies from the data set affecting service degradation and performance include FTTH access issues, optical network unit issues, and faults in customer premises equipment.

This paper adapts the Use Case Points method to estimate the size and development effort required for the Internet of Things systems. Despite the extensive use of UCP in software engineering, it has yet to be adapted for IoT systems, which is essential for project management and resource planning. Our proposed adaptation, UCP for IoT, is based on a four-layer IoT architecture and tailors the standard software UCP to the specifications of IoT systems. It was validated using a case study of three IoT systems, demonstrating its applicability and effectiveness in estimating the development effort required for IoT projects. However, the results also highlight the need for further improvements, particularly given the absence of historical data sets for IoT projects. Our future work will focus on gathering such datasets and further refining the proposed model.

Abstract The use of supervised Machine Learning (ML) to enhance Intrusion Detection Systems (IDS) has been the subject of significant research. Supervised ML is based upon learning by example, demanding significant volumes of representative instances for effective training and the need to retrain the model for every unseen cyber-attack class. However, retraining the models in-situ renders the network susceptible to attacks owing to the time-window required to acquire a sufficient volume of data. Although anomaly detection systems provide a coarse-grained defence against unseen attacks, these approaches are significantly less accurate and suffer from high false-positive rates. Here, a complementary approach referred to as “One-Shot Learning”, whereby a limited number of examples of a new attack-class is used to identify a new attack-class (out of many) is detailed. The model grants a new cyber-attack classification opportunity for classes that were not seen during training without retraining. A Siamese Network is trained to differentiate between classes based on pairs similarities, rather than features, allowing to identify new and previously unseen attacks. The performance of a pre-trained model to classify new attack-classes based only on one example is evaluated using three mainstream IDS datasets; CICIDS2017, NSL-KDD, and KDD Cup’99. The results confirm the adaptability of the model in classifying unseen attacks and the trade-off between performance and the need for distinctive class representations.

Finite State Machine is a popular modeling notation for various systems, especially software and electronic. Test paths (TPs) can be automatically generated from the system model to test such systems using a suitable algorithm. This paper presents a strategy that generates TPs and allows to start and end TPs only in defined states of the finite state machine. The strategy also simultaneously supports generating TPs only of length in a given range. For this purpose, alternative system models, test coverage criteria, and a set of algorithms are developed. The strategy is compared with the best alternative based on the reduction of the test set generated by the established N-switch coverage approach on a mix of 171 industrial and artificially generated problem instances. The proposed strategy outperforms the compared variant in a smaller number of TP steps. The extent varies with the used test coverage criterion and preferred TP length range from none to two and half fold difference. Moreover, the proposed technique detected up to 30% more simple artificial defects inserted into experimental SUT models per one test step than the compared alternative technique. The proposed strategy is well applicable in situations where a possible TP starts and ends in a state machine needs to be reflected and, concurrently, the length of the TPs has to be in a defined range.

A considerable number of patients with COVID-19 suffer from respiratory problems in the post-acute phase of the disease (the second-third month after disease onset). Individual telerehabilitation and telecoaching are viable, effective options for treating these patients. To treat patients individually, medical staff must have detailed knowledge of their physical activity and condition. A sensor network that utilizes medical-grade devices can be created to collect these data, but the price and availability of these devices might limit such a network's scalability to larger groups of patients. Hence, the use of low-cost commercial fitness wearables is an option worth exploring. This article presents the concept and technical infrastructure of such a telerehabilitation program that started in April 2021 in the Czech Republic. A pilot controlled study with 14 patients with COVID-19 indicated the program's potential to improve patients' physical activity, (85.7% of patients in telerehabilitation versus 41.9% educational group) and exercise tolerance (71.4% of patients in telerehabilitation versus 42.8% of the educational group). Regarding the accuracy of collected data, the used commercial wristband was compared with the medical-grade device in a separate test. Evaluating [Formula: see text]-scores of the intensity of participants' physical activity in this test, the difference in data is not statistically significant at level [Formula: see text]. Hence, the used infrastructure can be considered sufficiently accurate for the telerehabilitation program examined in this study. The technical and medical aspects of the problem are discussed, as well as the technical details of the solution and the lessons learned, regarding using this approach to treat COVID-19 patients in the post-acute phase.

Software module clustering is an unsupervised learning method used to cluster software entities (e.g., classes, modules, or files) with similar features. The obtained clusters may be used to study, analyze, and understand the software entities’ structure and behavior. Implementing software module clustering with optimal results is challenging. Accordingly, researchers have addressed many aspects of software module clustering in the past decade. Thus, it is essential to present the research evidence that has been published in this area. In this study, 143 research papers from well-known literature databases that examined software module clustering were reviewed to extract useful data. The obtained data were then used to answer several research questions regarding state-of-the-art clustering approaches, applications of clustering in software engineering, clustering processes, clustering algorithms, and evaluation methods. Several research gaps and challenges in software module clustering are discussed in this paper to provide a useful reference for researchers in this field.

With the rapid growth of the contemporary Internet of Things (IoT) market, the established systems raise a number of concerns regarding the reliability and the potential presence of critical integration defects. In this paper, we present a PatrIoT framework that aims to provide flexible support to construct an effective IoT system testbed to implement automated interoperability and integration testing. The framework allows scaling from a pure physical testbed to a simulated environment using a number of predefined modules and elements to simulate an IoT device or part of the tested infrastructure. PatrIoT also contains a set of reference example testbeds and several sets of example automated tests for a smart street use case.

A natural method to evaluate the effectiveness of a testing technique is to measure the defect detection rate when applying the created test cases. Here, real or artificial software defects can be injected into the source code of software. For a more extensive evaluation, injection of artificial defects is usually needed and can be performed via mutation testing using code mutation operators. However, to simulate complex defects arising from a misunderstanding of design specifications, mutation testing might reach its limit in some cases. In this paper, we present an open-source benchmark testbed application that employs a complement method of artificial defect injection. The application is compiled after artificial defects are injected into its source code from predefined building blocks. The majority of the functions and user interface elements are covered by creating front-end-based automated test cases that can be used in experiments.

Internet of Things (IoT) devices have been widely adopted in recent years. Unlike conventional information systems, IoT solutions have greater access to real-world contextual data and are typically deployed in an environment that cannot be fully controlled, and these circumstances create new challenges and opportunities. In this article, we leverage the knowledge that an IoT device has about its network context to provide an additional security factor. The device periodically scans a network and reports a list of all devices in the network. The server analyzes movements in the network and subsequently reacts to suspicious events. This article describes how our method can detect network changes, retrieved only from scanning devices in the network. To demonstrate the proposed solution, we perform a multiweek case study on a network with hundreds of active devices and confirm that our method can detect network anomalies or changes.

Combinatorial interaction testing (CIT) is a well-known technique, but the industrial experience is needed to determine its effectiveness in different application domains. We present a case study introducing a unified framework for generating, executing and verifying CIT test suites, based on the open-source Avocado test framework. In addition, we present a new industrial case study to demonstrate the effectiveness of the framework. This evaluation showed that the new framework can generate, execute, and verify effective combinatorial interaction test suites for detecting configuration failures (invalid configurations) in a virtualization system.

Combinatorial interaction testing (CIT) is a useful testing technique to address the interaction of input parameters in software systems. CIT has been used as a systematic technique to sample the enormous test possibilities. Most of the research activities focused on the generation of CIT test suites as a computationally complex problem. Less effort has been paid for the application of CIT. To apply CIT, practitioners must identify the input parameters for the Software‐under‐test (SUT), feed these parameters to the CIT test generation tool, and then run those tests on the application with some pass and fail criteria for verification. Using this approach, CIT is used as a black‐box testing technique without knowing the effect of the internal code. Although useful, practically, not all the parameters having the same impact on the SUT. This paper introduces a different approach to use the CIT as a gray‐box testing technique by considering the internal code structure of the SUT to know the impact of each input parameter and thus use this impact in the test generation stage. The case studies results showed that this approach would help to detect new faults as compared to the equal impact parameter approach.

Testing processes and workflows in information and Internet of Things systems is a major part of the typical software testing effort. Consistent and efficient path-based test cases are desired to support these tests. Because certain parts of software system workflows have a higher business priority than others, this fact has to be involved in the generation of test cases. In this paper, we propose a Prioritized Process Test (PPT), which is a model-based test case generation algorithm that represents an alternative to currently established algorithms that use directed graphs and test requirements to model the system under test. The PPT accepts a directed multigraph as a model to express priorities, and edge weights are used instead of test requirements. To determine the test-coverage level of test cases, a test-depth-level concept is used. We compared the presented PPT with five alternatives (i.e. the Process Cycle Test (PCT), a naive reduction of test set created by the PCT, Brute Force algorithm, Set-covering-Based Solution and Matching-based Prefix Graph Solution) for edge coverage and edge-pair coverage. To assess the optimality of the path-based test cases produced by these strategies, we used 14 metrics based on the properties of these test cases and 59 models that were created for three real-world systems. For all edge coverage, the PPT produced more optimal test cases than the alternatives in terms of the majority of the metrics. For edge-pair coverage, the PPT strategy yielded similar results to those of the alternatives. Thus, the PPT strategy is an applicable alternative as it reflects both the required test coverage level and the business priority in parallel.

In the automated testing based on actions in user interface of the tested application, one of the key challenges is maintenance of these tests. The maintenance overhead can be decreased by suitably structuring the test scripts, typically by employing reusable objects. To aid in the development, maintenance and refactoring of these test scripts, potentially reusable objects can be identified by a semi-automated process. In this paper, we propose a solution that identifies the potentially reusable objects in a set of automated test scripts and then provides developers with suggestions about these objects. During this process, we analyze the semantics of specific test steps using a system of abstract signatures. The solution can be used to identify the potentially reusable objects in both recorded automated test sets and tests programmed in an unstructured style. Moreover, compared to approaches that are based solely on searching for repetitive source code fragments, the proposed system identifies potentially reusable objects that are more relevant for test automation.

For a considerable number of software projects, the creation of effective test cases is hindered by design documentation that is either lacking, incomplete, or obsolete. The exploratory testing approach can serve as a sound method in such situations. However, the efficiency of this testing approach strongly depends on the method, the documentation of explored parts of a system, the organization and distribution of work among individual testers on a team, and the minimization of potential (very probable) duplicities in performed tests. In this paper, we present a framework for replacing and automating a portion of these tasks. A screen-flow-based model of the tested system is incrementally reconstructed during the exploratory testing process by tracking testers' activities. With additional metadata, the model serves for an automated navigation process for a tester. Compared with the exploratory testing approach, which is manually performed in two case studies, the proposed framework allows the testers to explore a greater extent of the tested system and enables greater detection of the defects present in the system. The results show that the time efficiency of the testing process improved with the framework support. This efficiency can be increased by team-based navigational strategies that are implemented within the proposed framework, which is documented by another case study presented in this paper.

Adaptive hypermedia system stores information about particular user and according to this information, it adapts its output in accord to this user information. This paper focuses on general reusability of user data in adaptive hypermedia system which are used as an input of the adaptation process. This proposal is based on the mapping language, which maps user parameters (atomic parts of user information in adaptive hypermedia system) between the systems. The essential point of this proposal is, that the proposed integration is done by the external way (by definition of the mapping and external data pump), thus any changes in existing adaptive hypermedia systems are not necessary.

Adaptive hypermedia systems represent one of promising future Internet concepts, especially in the area of e-learning. One of important contemporary claims is to create reusable adaptive hypermedia content, compatible with existing systems, to ease a widespread of this promising technology. In this paper we propose method of adaptive hypermedia e-learning courses construction using AICC communication standard. This proposal is based on formal description of adaptive hypermedia course topology and its mapping to AICC communication structure