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In our fast-paced modern world, software systems have become indispensable in both personal and professional spheres. With increasing reliance on software products, the demand for reliable and high-quality software has intensified, placing significant pressure on developers to stay competitive. Software reliability growth models (SRGMs) play a vital role in assessing the dependability of software systems during their development. These models mathematically analyze the relationship between detected faults, testing time, and failures, enabling the prediction of software failures. This paper introduces an approach to software reliability evaluation, considering the dynamic nature of fault removal efficiency (FRE) during development. Additionally, the model accounts for the change rate of residual errors, considering both error introduction and correction processes. Moreover, the adoption of S-shaped curves captures the learning process of software developers, enhancing the model's accuracy. This approach guides software developers to make informed decisions, leading to improved software reliability and performance, meeting escalating demands. [ABSTRACT FROM AUTHOR]

Software is used in diverse settings and depends on development and testing environments. Software development should improve the reliability, quality, cost, and stability of software, making the software testing period crucial. We proposed a software reliability model (SRM) that considers the uncertainty of software environments and the fault detection rate function as a Rayleigh distribution, with an explicit mean value function solution in the model. The goodness-of-fit of the proposed model relative to several existing nonhomogeneous Poisson process (NHPP) SRMs is presented based on three software application failure datasets. Further, a cost model is also presented that addresses the error-removal risk level and required time. The optimal testing release policy for minimizing the expected total cost (ETC) is also determined for NHPP SRMs. The impact of the software environment is studied by varying it, and the optimal release times and minimum ETCs are compared. The goodness-of-fit comparison confirmed that the proposed model has more accurate prediction values than other models. Further, whereas the existing models applied to the cost model do not change after a certain operation period, the proposed model yields changes in release time even for long operating periods. [ABSTRACT FROM AUTHOR]

For highly reliable software systems, it is expensive, time consuming, or even infeasible to perform reliability testing via a conventional software reliability demonstration testing (SRDT) plan. Moreover, in the traditional SRDT approach, the various characteristics of the software system or test sets are not considered when making the testing schemes. Some studies have focused on the effect of software testability on SRDT, but only limited situations were discussed, and many theoretical and practical problems have been left unresolved. In this paper, an extended study on the quantitative relation between test effectiveness (TE) and test effort for SRDT is proposed. Theoretical derivation is put forward by performing statistical analysis for the test suite according to TE. The combinations of all the cases of zero-failure and revealed nonzero failure, as well as discrete-type software and continuous-type software, are studied with the corresponding failure probability models constructed. That is, zero-failure and nonzero failure, as well as discrete-type software and continuous-type software, respectively, constitute the symmetry and asymmetry of SRDT. Finally, we illustrated all the models and performed applications on the Siemens program suite. The experimental results show that within the same limitation of requirements and confidence levels, this approach can effectively reduce the number of test cases and the test duration, i.e., accelerate the test process and improve the efficiency of the SRDT. [ABSTRACT FROM AUTHOR]

Code smells indicate potential symptoms or problems in software due to inefficient design or incomplete implementation. These problems can affect software quality in the long-term. Code smell detection is fundamental to improving software quality and maintainability, reducing software failure risk, and helping to refactor the code. Previous works have applied several prediction methods for code smell detection. However, many of them show that machine learning (ML) and deep learning (DL) techniques are not always suitable for code smell detection due to the problem of imbalanced data. So, data imbalance is the main challenge for ML and DL techniques in detecting code smells. To overcome these challenges, this study aims to present a method for detecting code smell based on DL algorithms (Bidirectional Long Short-Term Memory (Bi-LSTM) and Gated Recurrent Unit (GRU)) combined with data balancing techniques (random oversampling and Tomek links) to mitigate data imbalance issue. To establish the effectiveness of the proposed models, the experiments were conducted on four code smells datasets (God class, data Class, feature envy, and long method) extracted from 74 open-source systems. We compare and evaluate the performance of the models according to seven different performance measures accuracy, precision, recall, f-measure, Matthew's correlation coefficient (MCC), the area under a receiver operating characteristic curve (AUC), the area under the precision–recall curve (AUCPR) and mean square error (MSE). After comparing the results obtained by the proposed models on the original and balanced data sets, we found out that the best accuracy of 98% was obtained for the Long method by using both models (Bi-LSTM and GRU) on the original datasets, the best accuracy of 100% was obtained for the long method by using both models (Bi-LSTM and GRU) on the balanced datasets (using random oversampling), and the best accuracy 99% was obtained for the long method by using Bi-LSTM model and 99% was obtained for the data class and Feature envy by using GRU model on the balanced datasets (using Tomek links). The results indicate that the use of data balancing techniques had a positive effect on the predictive accuracy of the models presented. The results show that the proposed models can detect the code smells more accurately and effectively. [ABSTRACT FROM AUTHOR]

In cloud-edge-end scenarios, how to achieve rational resource allocation, implement effective service deployment, and ensure high service quality has become a hot research topic in academic domains. Service providers usually deploy services by considering the characteristics of different geographical regions, which helps to meet the diverse needs of users in different regions and optimize resource allocation and utilization. However, due to the widespread distribution of users and limited server resources, providing all types of services to users in every geographical region is not feasible. In addition, edge servers are prone to operational failures caused by software anomalies, hardware malfunctions, and malicious attacks, which will decrease service reliability. To address the problems above, this paper proposes a metric for service priorities based on user demands and regional characteristics for different geographical regions. Building upon this foundation, a Multi-Service Geographic region Deployment based on Priority (MS-GD-P) is proposed. This method takes user coverage and service reliability into consideration, which facilitates users' needs for multiple services in different geographical regions. Experimental results on real datasets demonstrate that MS-GD-P outperforms baseline methods in user coverage and service reliability. [ABSTRACT FROM AUTHOR]

This document offers guidance to medical students on how to prepare for virtual residency interviews. It emphasizes the importance of creating a professional environment, including having a neat background and reliable internet. The document also provides advice on personal appearance, backup devices, and researching the interviewers. It suggests practicing mock interviews and provides a template for structuring answers. The document encourages authenticity and highlighting strengths and experiences. Additionally, it provides guidance on asking relevant questions, handling uncomfortable experiences, and making the most of downtime during the interview day. Thorough preparation and follow-up are also emphasized. [Extracted from the article]

In the realm of cloud computing, ensuring the dependability and robustness of software systems is paramount. The intricate and evolving nature of cloud infrastructures, however, presents substantial obstacles in the pre-emptive identification and rectification of software anomalies. This study introduces an innovative methodology that amalgamates hybrid optimization algorithms with Neural Networks (NN) to refine the prediction of software malfunctions. The core objective is to augment the purity metric of our method across diverse operational conditions. This is accomplished through the utilization of two distinct optimization algorithms: the Yellow Saddle Goat Fish Algorithm (YSGA), which is instrumental in the discernment of pivotal features linked to software failures, and the Grasshopper Optimization Algorithm (GOA), which further polishes the feature compilation. These features are then processed by Neural Networks (NN), capitalizing on their proficiency in deciphering intricate data patterns and interconnections. The NNs are integral to the classification of instances predicated on the ascertained features. Our evaluation, conducted using the Failure-Dataset-OpenStack database and MATLAB Software, demonstrates that the hybrid optimization strategy employed for feature selection significantly curtails complexity and expedites processing [ABSTRACT FROM AUTHOR]

The crucial part of the software development cycle is software maintenance. The demands included in the software management are fault fixes and request to change or bring a new feature. If priority is not given to these demands, then it may lead to customer dissatisfaction, inefficient planning, and software failure as well. Therefore, it is important to study the severity of the bug reports to maintain the efficiency of the software. Various research has been conducted in the past to predict the severity of the paper using text mining focusing only on the content of the bug reports. The sentiment of the user while reporting a bug also plays a vital role. In this study, we will be focusing on two aspects, that is, sentiment and content to improve the prediction. We propose a prediction model based on LDA to study the content aspect and emotion analysis to study the sentiment aspect. The model is validated on the datasets collected from the Eclipse project using Convolutional Neural Network (CNN). The results show that the CNN model effectively utilizes the content and sentiment aspect of the data to handle the severity prediction. CNN has weight sharing feature that decreases the number of parameters used for training. It also improves generalization and overfitting is avoided The Accuracy, Precision, Recall, and F‐measure are improved when both aspects are taken into account rather than considering only content. [ABSTRACT FROM AUTHOR]

To better address mechanical behavior, it is necessary to make use of modern tools through which it is possible to run predictions, simulate scenarios, and optimize decisions. sources integration. This will increase the capability of detecting material modifications that forerun damage and/or to forecast the stage in the future when very likely fatigue is initiating and propagating cracks. Early warning outcomes obtained by the synergetic implementation of NDE-based protocols for studying mechanical and fatigue and fracture behavior will enhance the preparedness toward economically sustainable future damage control scenarios. Specifically, these early warning outcomes will be developed in the form of retopologized models to be used coupled with FEA. This paper presents the first stage of calibration and the combination of a system of different sensors (photogrammetry, laser scanning and strain gages) for the creation of volumetric models suitable for the prediction of failure of FEA software. The test objects were two components of car suspension to which strain gauges were attached to measure its deformation under cyclic loading. The calibration of the methodology was carried out using models obtained from photogrammetry and experimental strain gauge measurements. [ABSTRACT FROM AUTHOR]

Project risk management which has been rarely considered, especially in software projects, is a crucial process to complete projects successfully. This paper aims to propose a novel project risk management framework both to evaluate the project risks and to predict the success or failure of software projects based on their risk. This new framework uses Machine Learning (ML) and Multi-Attribute Decision-Making under a cloud model to effectively manage uncertainty. Based on the proposed framework, in the first stage, the important risks of the software projects are identified by an organized approach. Then, the risks are evaluated based on their probability and impacts on time, cost, and quality. In the second stage, the obtained results of the previous stage are entered into a new COPRAS method under the cloud model to rank the risks. Then, the risks are classified into various groups according to their rank. It helps project managers to gain a profound awareness of their high-priority project risks. In this paper, data on risks for fifty software projects has been collected. All the steps of the second stage are implemented on these projects in order to assess their risks. As a result, a data set whose features are nine types of software project risks and the label of success or failure of the projects is created. To recognize the pattern between risks' values and the success or failure of the projects, various efficient ML algorithms such as Naive Bayes, Logistic regression, Decision Tree, Bagging, Random Forest, and AdaBoost are applied. This framework can predict the success or failure of software projects based on their risks with good accuracy. The results depict that the Naive Bayes algorithm has the best results compared to others. [ABSTRACT FROM AUTHOR]

Definition: Space exploration has become an integral part of modern society, and since its early days in the 1960s, software has grown in importance, becoming indispensable for spaceflight. However, software is boon and bane: while it enables unprecedented functionality and cost reductions and can even save spacecraft, its importance and fragility also make it a potential Achilles heel for critical systems. Throughout the history of spaceflight, numerous accidents with significant detrimental impacts on mission objectives and safety have been attributed to software, although unequivocal attribution is sometimes difficult. In this Entry, we examine over two dozen software-related mishaps in spaceflight from a software engineering perspective, focusing on major incidents and not claiming completeness. This Entry article contextualizes the role of software in space exploration and aims to preserve the lessons learned from these mishaps. Such knowledge is crucial for ensuring future success in space endeavors. Finally, we explore prospects for the increasingly software-dependent future of spaceflight. [ABSTRACT FROM AUTHOR]

It is becoming increasingly common for software to operate in various environments. However, even if the software performs well in the test phase, uncertain operating environments may cause new software failures. Traditional proposed software reliability models under uncertain operating environments suffer from the problem of being well-suited to special cases due to the large number of assumptions involved. To improve these problems, this study proposes a new software reliability model that assumes an uncertain operating environment. The new software reliability model is a model that minimizes assumptions and minimizes the number of parameters that make up the model, so that the model can be applied to general situations better than the traditional proposed software reliability models. In addition, various criteria based on the difference between the predicted and estimated values have been used in the past to demonstrate the superiority of the software reliability models. Also, we propose a new multi-criteria decision method that can simultaneously consider multiple goodness-of-fit criteria. The multi-criteria decision method using ranking is useful for comprehensive evaluation because it does not rely on individual criteria alone by ranking and weighting multiple criteria for the model. Based on this, 21 existing models are compared with the proposed model using two datasets, and the proposed model is found to be superior for both datasets using 15 criteria and the multi-criteria decision method using ranking. [ABSTRACT FROM AUTHOR]

Flying Ad hoc Networks have emerged as a promising technology for number of real-time applications. However, the flexible and unstructured characteristics of these networks make them vulnerable to security threats posed by malicious nodes, such as denial of service attacks, node impersonation, and information breaches. Another major issue is the consideration of those nodes being unable to prove their trustworthiness due to factors like hardware or software failure, or by link interruptions, during the processing of detection of false nodes in the network. The existing mechanisms encompassing encryption, authentication, and intrusion detection highlight limitations to secure real-time applications and services due to the high speed of flying nodes and the absence of fixed network structures. To overcome these constraints, this research paper incorporates a novel framework for evaluating and improving the security of network by introducing an innovative cluster-based approach. Moreover, it presents a fuzzy model that dynamically estimates the trust levels of both individual nodes and clusters, by assigning weight to the parameters to address vulnerabilities. Additionally, a trust reconfiguration mechanism is further proposed to address the issue of nodes unable to substantiate their trust by providing them with additional chances based on the collective trust from previous evaluations. Further, the paper incorporates a dynamic reputation system to proficiently identify and separate malicious and selfish nodes from the network. Simulation results indicate a significant improvement in performance metrics, with a considerable reduction in delay and drop ratio by 41.46% and 36.37%, respectively, while the sufficient rise of 54.71% and 46.05% in throughput and coverage, respectively, comparing with the considered state-of-art. [ABSTRACT FROM AUTHOR]

In this paper, we propose stochastic scheme models with two kinds of errors for a computer system. A highly reliable computer system needs greatly to be backed up successfully in case of computer system crashes due to hardware failures and malicious software infections. We aim to determine appropriate backup times in computer processing of constant and random tasks. For this purpose, the trade-off between backup times and data losses due to errors is considered. The total execution times until the computing processing completes are obtained, and optimal backup policies to minimize them are derived analytically. Furthermore, we take up extended models of random tasks with n sub-tasks and analyze them. [ABSTRACT FROM AUTHOR]

In risk and safety assessments of aviation systems, engineers generally pay more attention to the risks of hardware or software failure and focus less on the risks caused by human errors. In this paper, a (FRAHE) method is proposed for identifying this critical error type and determining the risk severity of human errors. This method accounts for the human error probability as well as the impacts of human errors on the system. The fuzzy inference approach is employed in this paper to address the uncertainty and issues of imprecision that arise from insufficient information and scarce error data and a risk assessment model of human error is developed. The model can be used to precisely describe the relationship between the output risk severity and the input risk indicators, including the human error probability, the error impact probability, and the human error consequence. A case study of the approach task is presented to demonstrate the availability and reasonability of the model. The risk-based modeling method can not only provide valuable information for reducing the occurrence of critical errors but also be used to conduct prospective analyses to prevent unsafe incidents or aviation accidents. [ABSTRACT FROM AUTHOR]

In the current technological era, the reliability growth model depicts the failure in software that is employed for estimating the reliability of software. Conventional software reliability growth model (SRGMs) usually presume that faults in software are immediately corrected once a software fault is found and no new faults are introduced. This consideration can be impractical. However, introduction of several new faults is possible throughout debugging, which is called imperfect debugging. During such a process of debugging and introduction of new faults, consumption of testing effort also plays an important role. Hence, in this paper, two models are proposed that incorporate testing effort along with imperfect debugging during fault correction. Additionally, the combined testing effort is the addition of two efforts, specifically detection effort as well as correction effort. The formulated models are validated for real data set and contrasted with additional famous SRGMs. These inferences indicate that Model 2 performs best in terms of fitting and prediction capability. [ABSTRACT FROM AUTHOR]

Software reliability engineering has been developed to examine software systems over the years. Non-homogeneous Poisson process (NHPP) models are widely employed for analyzing software reliability data, as they have the ability to describe the expected number of failures over time using the mean value function. In addition, these models have an intensity function representing the failure rate over time. This article proposes a new non-homogeneous Poisson process model based on extended log-logistic (NHPP ELL) distribution with three parameters. The model properties were derived and represented graphically. Furthermore, the maximum likelihood estimation method was utilized to estimate the model parameters, which were computed numerically using the Newton–Raphson method. Three real software reliability datasets were utilized to evaluate the performance of the new NHPP ELL model based on three distinct criteria: the mean square error, the coefficient of determination (R2), the Theil statistic, the predictive-ratio risk, and the predictive power. Moreover, a comparative study was conducted between the proposed model and several well-known NHPP models regarding goodness-of-fit and predictive performance. All the evaluation measures indicate that the proposed model outperforms the other models across all the considered datasets. In addition, the graphical plots depicting the actual data and the prediction results for all the fitted models show that the proposed model performs well in all three considered software reliability datasets compared to other models. [ABSTRACT FROM AUTHOR]

Software bugs are a noteworthy concern for developers and maintainers. When a failure is detected late, it costs more to be fixed. To repair the bug that caused the software failure, the location of the bug must first be known. The process of finding the defective source code elements that led to the failure of the software is called bug localization. Effective approaches for automatically locating bugs using bug reports are highly desirable, as they would reduce bug-fixing time, consequently lowering software maintenance costs. With the increasing size and complexity of software projects, manual bug localization methods have become complex, challenging, and time-consuming tasks, which motivates research on automated bug localization techniques. This paper introduces a novel bug localization model, which works on two levels. The first level localizes the buggy classes using an information retrieval approach and it has two additional sub-phases, namely the class-level feature scoring phase and the class-level final score and ranking phase, which ranks the top buggy classes. The second level localizes the buggy methods inside these classes using an information retrieval approach and it has two sub-phases, which are the method-level feature scoring phase and the method-level final score and ranking phase, which ranks the top buggy methods inside the localized classes. A model is evaluated using an AspectJ dataset, and it can correctly localize and rank more than 350 classes and more than 136 methods. The evaluation results show that the proposed model outperforms several state-of-the-art approaches in terms of the mean reciprocal rank (MRR) metrics and the mean average precision (MAP) in class-level bug localization. [ABSTRACT FROM AUTHOR]

Numerous sensor nodes make up a wireless sensor network. Sensor nodes work together to send information about the environment to the base station. Nodes are susceptible to failure during these operations due to power failure, hardware or software failure, etc.. Consequently, delivering dependable packages necessitates achieving two important objectives: energy efficiency and minimized error rate. A novel approach is proposed to distribute the workload among each sensor node to accomplish these objectives. When estimating the scope of communication between nodes, uncertainties are dealt with using the fuzzy logic method. During some weather hazards, the features and cables are particularly challenging to monitor with simple devices in the WAN area. Remote sensing units are used to check and display the feature conditions to resolve this issue. Using machine learning algorithms, the collected data will be analyzed and processed to create a prediction horizon that will be useful for environmental studies and early warning systems. The MatLab2020b simulation program, which has useful tools and learning applications, will be used to carry out this task. The FIS algorithm obtained a cost distance of just 629 m out of the total WSN sensor lengths of 2600 m, which is the best cost among all implemented routing protocol techniques in this study. [ABSTRACT FROM AUTHOR]

Defective modules that cause software execution failures are common in large software projects. Source code for a significant number of modules may be found in several software repositories. This software repository includes each module's software metrics and the module's faulty status. Software companies face a considerable problem detecting defects in sizeable and complex programming code. In addition, many international reports, such as the comprehensive human appraisal for originating (CHAOS) report, have mentioned that there are countless reasons for the failure of software projects, including the inability to detect errors and defects in the programming code of those projects at an early stage. This research employs a statistical analysis technique to reveal the characteristics that indicate the faulty status of software modules. It is recommended that statistical analysis models derived from the retrieved information be merged with existing project metrics and bug data to improve prediction. When all algorithms are merged with weighted votes, the results indicate enhanced prediction abilities. The proposed statistical analysis outperforms the state-of-the-art method (association rule, decision tree, Naive Bayes, and neural network) in terms of accuracy by 9.1%, 10.3%, 13.1%, and 13.1%, respectively. [ABSTRACT FROM AUTHOR]

Software development encompasses various factors beyond technical considerations. Neglecting non-technical elements like individuals, processes, culture, and social and organizational aspects can lead to debt-like characteristics that demand attention. Therefore, we introduce the non-technical debt (NTD) concept to encompass and explore these aspects. This indicates the applicability of the debt analogy to non-technical facets of software development. Technical debt (TD) and NTD share similarities and often arise from risky decision-making processes, impacting both software development professionals and software quality. Overlooking either type of debt can lead to significant implications for software development success. The current study conducts a comprehensive multivocal literature review (MLR) to explore the most recent research on NTD, its causes, and potential mitigation strategies. For analysis, we carefully selected 40 primary studies among 110 records published until October 1, 2022. The study investigates the factors contributing to the accumulation of NTD in software development and proposes strategies to alleviate the adverse effects associated with it. This MLR offers a contemporary overview and identifies prospects for further investigation, making a valuable contribution to the field. The findings of this research highlight that NTD’s impacts extend beyond monetary aspects, setting it apart from TD. Furthermore, the findings reveal that rectifying NTD is more challenging than addressing TD, and its consequences contribute to the accumulation of TD. To avert software project failures, a comprehensive approach that addresses NTD and TD concurrently is crucial. Effective communication and coordination play a vital role in mitigating NTD, and the study proposes utilizing the 3C model as a recommended framework to tackle NTD concerns. [ABSTRACT FROM AUTHOR]

The foundation of a large river crossing bridge is often located on high and steep slopes in mountainous area, and the stability of the slope has a significant impact on the safety of the bridge. Not only the bridge load, but also the hydro-dynamical action in the reservoir area has a significant impact on the stability of the bank slope where the bridge foundation is located, especially for the toppling bank slope. This paper takes the stability of the toppling bank slope where the one major bridge foundation is located at on the Lancang River in China as an example. Through on-site exploration, drilling data and core conditions, and television images of the borehole, the geological structure of the on-site bank slope were conducted. Based on the development of the dumping body obtained from on-site exploration, corresponding indicators have been proposed from the perspectives of rock inclination, deformation, and rock quality to clarify the degree of dumping along the depth of the bank slope. The failure mechanism of the overturned bank slope under the action of a bridge was analyzed from a mechanical perspective. Numerical simulations were conducted using GeoStudio 2018:SEEP/W and FLAC3D 6.0 software to analyze the failure modes of bridge loads and hydrodynamic forces under different water levels and rainfall conditions. The seepage field characteristics, failure modes, and stability characteristics were analyzed from a two-dimensional perspective, while the displacement characteristics, plastic zone, and stress–strain characteristics were explored from a three-dimensional perspective, which revealed the evolution mode of overturned deformation under the action of bridge foundation loads. Finally, the stability of the wide slope was numerically calculated using the strength reduction method, and the stability calculation data was combined with the numerical simulation results to determine the optimal location of the bridge foundation. [ABSTRACT FROM AUTHOR]

Software-Defined Networking (SDN), which is used in Industrial Internet of Things, uses a controller as its "network brain" located at the control plane. This uniquely distinguishes it from the traditional networking paradigms because it provides a global view of the entire network. In SDN, the controller can become a single point of failure, which may cause the whole network service to be compromised. Also, data packet transmission between controllers and switches could be impaired by natural disasters, causing hardware malfunctioning or Distributed Denial of Service (DDoS) attacks. Thus, SDN controllers are vulnerable to both hardware and software failures. To overcome this single point of failure in SDN, this paper proposes an attack-aware logical link assignment (AALLA) mathematical model with the ultimate aim of restoring the SDN network by using logical link assignment from switches to the cluster (backup) controllers. We formulate the AALLA model in integer linear programming (ILP), which restores the disrupted SDN network availability by assigning the logical links to the cluster (backup) controllers. More precisely, given a set of switches that are managed by the controller(s), this model simultaneously determines the optimal cost for controllers, links, and switches. [ABSTRACT FROM AUTHOR]

Malaysian public sectors have invested billions in digitizing systems. Electronic government efforts created much software. Our informal interview taught us that many software projects encountered delays, and several failed. One of the main contributions of software failure is ambiguity in requirements specification (RS). Ambiguity is a familiar requirement smell that causes misinterpretation. Thus, we seek to devise a technique for detecting and improving ambiguous RS in the Malaysian public sector. One of our challenges is that the Malaysian public sector RS is developed in Malay, and most available techniques support English and other major languages. Hence, this paper investigates the automated and semi-automated techniques to detect and improve ambiguous RS. Following the standard guidelines for systematic mapping, review, snowballing, and quality assessment, we studied works from 2010 to 2022 on ambiguity detection and improvement techniques. We chose 42 articles as primary studies from 2,549. As a result, Natural Language Processing (NLP) and machine learning (ML) are the most promising techniques for automated and semi-automated ambiguous detection models. Furthermore, the ambiguous improvement technique began using deep learning (DL) in 2019. However, most proposed tools are still in the validation phase and are not widely employed, implying that tool development and validation research are progressing slowly. Apart from the generic linguistic context of RS, some research focuses on industrial domain-based RS. Our study shows that additional strategies have been developed to overcome RS-related issues. [ABSTRACT FROM AUTHOR]

The coronavirus disease (COVID-19) outbreak has prompted various industries to embark on digital transformation efforts, with software playing a critical role. Ensuring the reliability of software is of the utmost importance given its widespread use across multiple industries. For example, software has extensive applications in areas such as transportation, aviation, and military systems, where reliability problems can result in personal injuries and significant financial losses. Numerous studies have focused on software reliability. In particular, the software reliability growth model has served as a prominent tool for measuring software reliability. Previous studies have often assumed that the testing environment is representative of the operating environment and that software failures occur independently. However, the testing and operating environments can differ, and software failures can sometimes occur dependently. In this study, we propose a new model that assumes uncertain operating environments and dependent failures. In other words, the model proposed in this study takes into account a wider range of environments. The numerical examples in this study demonstrate that the goodness of fit of the new model is significantly better than that of the existing SRGM. Additionally, we show the utilization of the sequential probability ratio test (SPRT) based on the new model to assess the reliability of the dataset. [ABSTRACT FROM AUTHOR]

The development of most modern software systems is accompanied by a significant level of uncertainty, which can be attributed to the unanticipated activities that may occur throughout the software development process. As these modern software systems become more complex and drawn out, escalating software project failure rates have become a critical concern. These unforeseeable uncertainties are known as software risks, and they emerge from many risk factors inherent to the numerous activities comprising the software development lifecycle (SDLC). Consequently, these software risks have resulted in massive revenue losses for software organizations. Hence, it is imperative to address these software risks, to curb future software system failures. The subjective risk assessment (SRM) method is regarded as a viable solution to software risk problems. However, it is inherently reliant on humans and, therefore, in certain situations, imprecise, due to its dependence on an expert's knowledge and experience. In addition, the SRM does not allow repeatability, as expertise is not easily exchanged across the different units working on a software project. Developing intelligent modelling methods that may offer more unbiased, reproducible, and explainable decision-making assistance in risk management is crucial. Hence, this research proposes enhanced fuzzy induction models for software requirement risk prediction. Specifically, the fuzzy unordered rule induction algorithm (FURIA), and its enhanced variants based on nested subset selection dichotomies, are developed for software requirement risk prediction. The suggested fuzzy induction models are based on the use of effective rule-stretching methods for the prediction process. Additionally, the proposed FURIA method is enhanced through the introduction of nested subset selection dichotomy concepts into its prediction process. The prediction performances of the proposed models are evaluated using a benchmark dataset, and are then compared with existing machine learning (ML)-based and rule-based software risk prediction models. From the experimental results, it was observed that the FURIA performed comparably, in most cases, to the rule-based and ML-based models. However, the FURIA nested dichotomy variants were superior in performance to the conventional FURIA method, and rule-based and ML-based methods, with the least accuracy, area under the curve (AUC), and Mathew's correlation coefficient (MCC), with values of approximately 98%. [ABSTRACT FROM AUTHOR]

The number of software failures, software reliability, and failure rates can be measured and predicted by the software reliability growth model (SRGM). SRGM is developed and tested in a controlled environment where the operating environment is different. Many SRGMs have developed, assuming that the working and developing environments are the same. In this paper, we have developed a new SRGM incorporating the imperfect debugging and testing coverage function. The proposed model's parameters are estimated from two real datasets and compared with some existing SRGMs based on five goodness-of-fit criteria. The results show that the proposed model gives better descriptive and predictive performance than the existing selected models. [ABSTRACT FROM AUTHOR]

Requirement prioritization (RP) is a crucial task in managing requirements as it determines the order of implementation and, thus, the delivery of a software system. Improper RP may cause software project failures due to over budget and schedule as well as a low-quality product. Several factors influence RP. One of which is requirements dependency. Handling inappropriate handling of requirements dependencies can lead to software development failures. If a requirement that serves as a prerequisite for other requirements is given low priority, it affects the overall project completion time. Despite its importance, little is known about requirements dependency in RP, particularly its impacts, types, and techniques. This study, therefore, aims to understand the phenomenon by analyzing the existing literature. It addresses three objectives, namely, to investigate the impacts of requirements dependency on RP, to identify different types of requirements dependency, and to discover the techniques used for requirements dependency problems in RP. To fulfill the objectives, this study adopts the Systematic Literature Review (SLR) method. Applying the SLR protocol, this study selected forty primary articles, which comprise 58% journal papers, 32% conference proceedings, and 10% book sections. The results of data synthesis indicate that requirements dependency has significant impacts on RP, and there are a number of requirements dependency types as well as techniques for addressing requirements dependency problems in RP. This research discovered various techniques employed, including the use of Graphs for RD visualization, Machine Learning for handling large-scale RP, decision making for multi-criteria handling, and optimization techniques utilizing evolutionary algorithms. The study also reveals that the existing techniques have encountered serious limitations in terms of scalability, time consumption, interdependencies of requirements, and limited types of requirement dependencies. [ABSTRACT FROM AUTHOR]

To guarantee that software does not fail, software quality assurance (SQA) teams play a critical part in the software development procedure. As a result, prioritizing SQA activities is a crucial stage in SQA. Software defect prediction (SDP) is a procedure for recognizing high-risk software components and determining the influence of software measurements on the likelihood of software modules failure. There is a continuous need for sophisticated and better SDP models. Therefore, this study proposed the use of dagging-based and baseline classifiers to predict software defects. The efficacy of the dagging-based SDP model for forecasting software defects was examined in this study. The models employed were naïve Bayes (NB), decision tree (DT), and k-nearest neighbor (kNN), and these models were used on nine NASA datasets. Findings from the experimental results indicated the superiority of SDP models based on dagging meta-learner. Dagging-based models significantly outperformed experimented baseline classifiers built on accuracy, the area under the curve (AUC), F-measure, and precision-recall curve (PRC) values. Specifically, dagging-based NB, DT, and kNN models had +6.62%, +3.26%, and +4.14% increments in average accuracy value over baseline NB, DT, and kNN models. Therefore, it can be concluded that the dagging meta-learner can advance the recognition performances of SDP methods and should be considered for SDP processes. [ABSTRACT FROM AUTHOR]

The transition from conventionally manned to autonomous ships is accompanied by the development of enhanced Decision Support Systems (DSS) for navigators. Such systems need to consider interactions among hardware, software, and humans and their potential effects on system performance, which require rigorous testing to verify the system's safe decision-making ability and operational limits. Testing requirements for verification are aimed at 1) assessing the system's reliability and failure handling performance, and 2) integration testing. This work uses the System-Theoretic Process Analysis (STPA) to develop integration tests for a novel DSS. STPA is a structured methodology to identify hazards from multiple sources, including hardware or software failures, system interactions, and human errors. The objectives of the study are to develop and assess the feasibility of integration test procedures based on STPA. The stability monitoring subsystem from the DSS is analyzed as a case study. The results are used to suggest functional and performance integration test procedures. [ABSTRACT FROM AUTHOR]

The use of automated methods for log analysis is unavoidable in any large company; therefore, it has attracted attention from engineers and researchers. As a result, the number of articles in the field grows yearly and new approaches are frequently proposed. Unfortunately, published research works only sometimes meet the needs of engineers wishing to apply the methods in real-life systems. A common issue is that the method's benefits often do not compensate for the effort required for its implementation and maintenance. Therefore, engineers must understand the pros and cons of full-scale applications, including the implementation details and the required effort. This work provides a comprehensive review of automated log analysis methods and aims to offer a guide for software engineers who fix integration and production failures. The article categorizes and provides an overview of existing methods and assesses their implementation and maintenance costs, as well as the feasibility of the methods. The article also identifies and describes the shortcomings of existing methods, including concept drift, which is not addressed with sufficient attention, as well as the lack of online benchmarks and the interpretation of the log sequence as a language, without an in-depth analysis of its properties. Despite growing efforts to provide feasible and widely adopted solutions, many reference implementations are unavailable. Consequently, the time and computation complexities differ between various implementations of the same approaches, making the results of research work difficult to replicate in real-life scenarios. [ABSTRACT FROM AUTHOR]

The more complex the software system, the greater the number of possible combinations of defects that can cause errors, resulting in software defects that are difficult to isolate and expensive to correct in the development stage. An essential feature of such defects is a gradual deterioration in software performance finishing with software failure—software aging. Mobile devices are particularly vulnerable to software aging. Thus, there is a constant need for methods and tools to eliminate the effects of aging in mobile systems based on modeling the aging process, including the study of metrics and aging factors and the development of more reliable and adequate aging and rejuvenation models. This paper summarizes the previously developed Android software aging and rejuvenation models and presents a comprehensive model of aging and rejuvenation for the Android operating system. The comprehensive model is based on continuous-time Markov Chains and considers different aging levels, mobile device activity, and battery status. The aging and rejuvenation model can be used to assess the software quality, allows obtaining expressions for indicators of software rejuvenation efficiency, and can be used to design and select parameters of the software rejuvenation method considering battery saving. [ABSTRACT FROM AUTHOR]

Featured Application: Software runtime anomaly detection is a critical component in AIOps. The proposed method can detect both functional failures and performance failures in software systems, with a particular focus on database systems. This technique can help long-running service systems identify internal partial failures so that they can take action between the partial failures becoming service failures and ensuring the runtime reliability of systems. Software plays a critical role in the infrastructure of modern society. Due to the increasing complexity, it suffers runtime reliability issues. Online anomaly detection can detect partial failures within the program based on manifestations exhibited internally or externally before serious failures occur in the software system, thus enabling timely intervention by operation and maintenance staff to avoid serious losses. This paper introduces CL-MMAD, a novel anomaly detection method based on contrastive learning using multimodal data sources. CL-MMAD uses ResNet-18 to learn the comprehensive feature spaces of software running status. MSE loss is used as the objective to guide the training process and is taken as the anomaly score. Empirical results highlight the superiority of MSE loss over InfoNCE loss and demonstrate CL-MMAD's effectiveness in detecting both functional failures and performance issues, with a greater ability to detect the latter. [ABSTRACT FROM AUTHOR]

Cloud storage has been widely used to team work or cooperation development. Data owners set up groups, generating and uploading their data to cloud storage, while other users in the groups download and make use of it, which is called group data sharing. As all kinds of cloud service, data group sharing also suffers from hardware/software failures and human errors. Provable Data Possession (PDP) schemes are proposed to check the integrity of data stored in cloud without downloading. However, there are still some unmet needs lying in auditing group shared data. Researchers propose four issues necessary for a secure group shared data auditing: public verification, identity privacy, collusion attack resistance and traceability. However, none of the published work has succeeded in achieving all of these properties so far. In this paper, we propose a novel blockchain-based ring signature PDP scheme for group shared data, with an instance deployed on a cloud server. We design a linkable ring signature method called Linkable Homomorphic Authenticable Ring Signature (LHARS) to implement public anonymous auditing for group data. We also build smart contracts to resist collusion attack in group auditing. The security analysis and performance evaluation prove that our scheme is both secure and efficient. [ABSTRACT FROM AUTHOR]

Requirements elicitation is a fundamental step in a software development process since it is at this stage that the software begins to be designed. In some situations, the problems related to the failure of the software development project are due to an incomplete requirements elicitation, resulting in solutions that do not understand all the necessary functionalities or do not incorporate innovation. Despite the various techniques offered by Requirements Engineering, situations such as the growing application market and the need for innovation further increase the importance of understanding the user's different needs. In this paper, we investigated how the journey map and personas techniques are being used in requirements elicitation in both the literature and the industry, along with the advantages, disadvantages and challenges of using these techniques. We conducted systematic literature review to identify the personas and journey map techniques used in requirements elicitation in the literature and industry. In addition, we conducted a survey with 52 practitioners (software developers, users and managers) to investigate their perceptions of the use of journey maps and personas techniques in the requirements elicitation phase. Twenty-four primary studies were identified that address journey map and personas techniques in software requirements elicitation. In addition, most respondents stated that using these techniques facilitates understanding the requirements, providing better integration, collaboration and leveling of knowledge among the members of the software development teams. Our findings allow us to conclude that most of the software developers, users and managers that participated in the survey consider that the journey map and personas techniques are effective in helping understand the software requirements to be developed by the development teams. [ABSTRACT FROM AUTHOR]

The design and organization of complex robotic systems traditionally requires laborious trial-and-error processes to ensure both hardware and software components are correctly connected with the resources necessary for computation. This paper presents a novel generalization of the quadratic assignment and routing problem, introducing formalisms for selecting components and interconnections to synthesize a complete system capable of providing some user-defined functionality. By introducing mission context, functional requirements, and modularity directly into the assignment problem, we derive a solution where components are automatically selected and placed into an optimal organization of hardware and software, all while respecting restrictions on component viability and required functionality. The ability to generate complete functional systems directly from individual components reduces manual design effort by allowing for a guided exploration of the design space. Additionally, our formulation increases resiliency by quantifying resource margins and enabling adaptation of system structure in response to changing environments, hardware or software failure. The proposed formulation is cast as an integer linear program which is provably N P -hard. Two case studies are analyzed to highlight the expressiveness and complexity of problems that are addressable by this approach: the first explores the iterative development of a ground-based search-and-rescue robot in varying mission contexts, while the second compares an automated humanoid design against a manual design that was finalist in the DARPA Robotics Challenge. Numerical simulations quantify real-world performance and demonstrate tractable time complexity for the scale of problems encountered in many modern robotic systems. [ABSTRACT FROM AUTHOR]

Software is used in various industries, and its reliability has become an extremely important issue. For example, in the medical industry, software is used to provide medical services to underprivi-leged individuals. If a problem occurs with the software reliability, incorrect medical information may be provided. The software reliability is estimated using a software reliability growth model. However, most software reliability growth models assume that the failures are independent. In addition, it is assumed that the test and operating environments are the same. In this study, we propose a new software reliability growth model that assumes that software failures are dependent and uncertain operating environments. A comparison of the proposed model against existing NHPP SRMEs using actual datasets shows that the proposed model achieves the best fit. [ABSTRACT FROM AUTHOR]

Configuration errors are some of the main reasons for software failures. Some configuration options may even negatively impact the software's security, so that if a user sets the options inappropriately, there may be a huge security risk for the software. Recent studies have proposed mapping option read points to configuration options as the first step in alleviating the occurrence of configuration errors. Sadly, most available techniques use humans, and the rest require additional input, like an operation manual. Unfortunately, not all software is standardized and friendly. We propose a technique based on program and static analysis that can automatically map all the configuration options of a program just by reading the source code. Our evaluation shows that this technique achieves 88.6%, 97.7%, 94.6%, 94.8%, and 92.6% success rates with the Hadoop modules Common, Hadoop distributed file system, MapReduce, and YARN, and also PX4, when extracting configuration options. We found 53 configuration options in PX4 that were not documented and submitted these to the developers. Compared with published work, our technique is more effective in mapping options, and it may lay the foundation for subsequent research on software configuration security. [ABSTRACT FROM AUTHOR]

Computing software plays an essential role in almost every sector of the digital age, but the process of efficient software development still faces several challenges. Effective software development methodology can be the difference between the success and failure of a software project. This research aims to evaluate the overall impact of Agile Software Development (ASD) on the individual, organizational, software development, and project management dimensions. For this purpose, we surveyed several software development professionals from a variety of backgrounds (experience, location, and job ranks) to explore the impact of ASD on the IT industry of Pakistan. Our analysis of the collected information is two folds. First, we summarized the findings from our surveys graphically clearly show the opinions of our survey respondents regarding the effectiveness of the Agile methodology for software development. Secondly, we utilized quantitative measures to analyze the same data statistically. A comparison is drawn between the graphical and statistical analysis to verify the reliability of our findings. Our findings suggest the existence of a strong relationship between effective software development and the use of Agile processes. Our analysis shows that the job fit of software development professionals and ASD are critical factors for software development project success in terms of cost, quality, stakeholders satisfaction, and time. Although the study focuses on the IT industry of Pakistan, the findings can be generalized easily to other developing IT industries worldwide. [ABSTRACT FROM AUTHOR]

The cars of my childhood were unsafe at any speed. American automobiles were designed for looks, not safety. Automobiles of the 1950s and 1960s were sold without padded dashboards, antilock brakes, airbags, traction control, or even seatbelts. The first federal motor vehicle safety standards led to collapsible steering columns by the late 1960s. Today 's automobiles are safer than ever, yet 40,000 Americans are killed annually in automobile accidents. Ninety-four percent of all automobile accidents are caused by human error. America's automobile industry is in the process of making a paradigmatic shift to connected vehicles controlled by software, not humans. Key enablers of fully autonomous vehicles are software and advances in machine learning. Autonomous vehicles ("AVs") travel .from their point of departure to their point of destination without human intervention controlled by software, cameras, and Light Detection and Ranging ("LiDAR"). This Article proposes extending products liability to defective software components in AVs. Under this proposed reform, software creators that supply components to driverless vehicles or AVs will be strictly liable for injuries or deaths attributable to dangerously defective code. The software industry currently deploys contract law to disclaim all meaningful warranties and cap damages in their licensing agreements. Under a product liability regime, software warranties can no longer be disclaimed nor liability limited. Extending products liability to defective software incorporated within AVs will ensure that manufacturers, distributors, suppliers, retailers, and other parties involved in the production of AVs Will be held accountable for the costs of injuries and deaths caused by defective AV software components. Product liability must evolve to address software failure caused by errors in algorithms to protect the occupants of AVs and others using the roadway, including bicyclists and pedestrians. [ABSTRACT FROM AUTHOR]

This article presents the design and implementation of a fault tolerant architecture for sensor fusion that tolerates faults on a quadrotor unmanned aerial vehicle (UAV). It aims to tolerate both hardware sensors faults (GPS jamming, IMU lock or freezing, magnetometer sensitivity to high power magnetic fields...) and software faults (faults in the Kalman filter, bad parameters initialization....). The proposed architecture uses data fusion with Kalman filters in order to estimate the states (position and orientation) of the UAV. It includes an analytical redundancy using the dynamic model of the system. The estimations of the defined Kalman filters and the dynamic model feed a weighted average voter, which increases the accuracy of the outputs and the error detection process. The proposed architecture allows multiple recovery solutions to a faulty system and thus increasing its flexibility. The architecture is validated using numerical simulations and experimental flights in real outdoor environment using a quadrotor. • The strategy provides fault tolerance to successive hardware and software failures. • Analytical redundancy is added using the dynamic model. • Several solutions to recover a faulty system are identified. • Real experimental flights are conducted to validate our strategy. [ABSTRACT FROM AUTHOR]

Software failure such as software defect causes billion of dollar loss every year. Software failure also affects billion of people worldwide. Inadequate software testing can cause software failure. To predict the software defect, this study proposed a model consisting of feature selection and classifications. The correlation base method was used for feature selection, and radial base function neural network (RBF) was used for classification. Also, for testing the proposed system, fourteen NASA data sets were used including CM1, JM1, KC1, KC2, KC3, KC4, MC1, MC2, MW1, PC1, PC2, PC3, PC4, and PC5. The data set was divided using the well-known K-cross-validation methods which were performed to divide the data set for training and testing the RBF. The RBF were trained and tested before and after feature selections. Precision, recall, F-measure, and accuracy are four methods used to evaluate the performance of the proposed methods. The precision obtained for the fourteen data sets was CM1, 94.01%; JM1, 85.18%; KC1, 83.24%; KC2, 81.27%; KC3, 79.30%; KC4, 85.29%; MC1, 99.89%; MC2, 73.27%; MW1, 90.90%; PC1, 98.79%; PC2, 100%; PC3, 95.67%; PC4, 95.12%; and PC5, 80.89%. Recall was as follows: CM1, 95.78%; JM1, 87.89%; KC1, 86.24%; KC2, 83.82%; KC3, 82.10%; KC4, 86.28%; MC1, 100%; MC2, 76.67%; MW1, 92.09%; PC1, 99.98%; PC2, 100%; PC3, 96.23%; PC4, 95.17%; and PC5, 81.80%. F-measure was as follows: CM1, 0.95; JM1, 0.87; KC1, 0.83; KC2, 0.82; KC3, 0.85; KC4, 0.86; MC1, 0.99; MC2, 0.76; MW1, 0.95; PC1, 0.99; PC2, 0.99; PC3, 0.97; PC4, 0.95; and PC5, 0.80. The accuracy obtained was as follows: CM1, 93.99%; JM1, 84.87%; KC1, 83.25%; KC2, 79.11%; KC3, 78.25%; KC4, 83.18%; MC1, 99.01%; MC2, 70.18%; MW1, 88.90%; PC1, 98.99%; PC2, 99.80%; PC3, 94.11%; PC4, 94.4%; and PC5, 79.02%. The proposed method results were compared with the result obtained from different methods. The proposed model obtained better results than other methods for data set CM1, KC4, MC1, PC1, PC2, PC3, PC4, and PC5. [ABSTRACT FROM AUTHOR]

Traffic forecasting is an important part in realising intelligent traffic management, which helps traffic controllers and travellers make effective decisions. However, traffic forecasting accuracy is often affected by missing traffic data due to hardware and software failure. Therefore, accurate prediction based on incomplete traffic data is an important problem as well as a challenge. Though many approaches recover the missing values before prediction, the errors from the data‐filling step are likely to cause additional bias to the prediction result. Besides, this tactic is difficult to guarantee the timeliness and may impede real‐time prediction. In this case, a traffic forecasting model is proposed to directly predict traffic data with missing values. This model develops tensor formed dynamic mode decomposition, recording the dynamic information of traffic data into a state transition tensor. In addition, the model takes low rank property of the dynamic tensor and the similarity of temporal variation trend into consideration. In order to verify the effectiveness and the robustness of the proposed model, experiments were performed on two real‐world time series datasets. The results demonstrate that the model achieves better performance on forecasting than other baseline approaches under the impact of missing data. [ABSTRACT FROM AUTHOR]

The software development industry or organizations increasingly emerging day by day have adopted global software development (GSD) practices due to the large significance of outsourcing. These industries face many challenges due to a lack of understanding customer perspective in the GSD environment. For any organization, the customer is the major stakeholder, and customer relationship management (CRM) plays a vital role in customer satisfaction with software development projects. These challenges create serious risks for any software development project's success. Thus, CRM is a crucial challenge in the success of software projects in the GSD environment. This research study aims to address the factors that negatively influence CRM implementation in the global context and proposes a conceptual model based on the identified factors for enhancing software product quality. The systematic literature review (SLR) phase investigates the potential barriers to CRM implementation in GSD. Based on identified barriers, an initial conceptual model is developed. The proposed conceptual model is validated using a questionnaire survey of the GSD industry and CRM practitioners of Pakistan. Statistical analysis and several suitable tests are also performed to develop the final conceptual model for CRM implementation in the GSD environment. This research is performed from the client's perspective. The results are promising and accommodating to avoid any software project failure due to customer-related issues in a GSD environment. [ABSTRACT FROM AUTHOR]

Long-running software, such as cloud computing services, is now widely used in modern applications. As a result, the demand for high availability and performance has grown. However, these applications are more vulnerable to software aging issues and are more likely to fail due to the accumulation of mistakes in the system. One popular strategy for dealing with such aging-related problems is to plan prediction-based software rejuvenation activities based on previously obtained data from long-running software. Prediction algorithms enable the activation of a mitigation mechanism before the problem occurs. The long short-term memory (LSTM) neural network, the present state of the art in temporal series prediction, has demonstrated promising results when applied to software aging concerns. This study aims to anticipate software aging failures using a hybrid prediction model integrating long short-term memory models and statistical approaches. We emphasize the capabilities of each strategy in various long-running software scenarios and provide an untried hybrid model (hLSTM-aging) based on the union of Conv-LSTM networks and probabilistic methodologies, attempting to combine the strengths of both approaches for software aging forecasts. The hLSTM-aging prediction results revealed how hybrid models are a compelling solution for software-aging prediction. Experiments showed that hLSTM-aging increased MSE criteria by 8.54% to 50% and MAE criteria by 3.53% to 14.29% when compared to Conv-LSTM, boosting the model's initial performance. [ABSTRACT FROM AUTHOR]