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Software reuse is an essential practice to increase efficiency and reduce costs in software production. Software reuse practices range from reusing artifacts, libraries, components, packages, and APIs. Identifying suitable software for reuse requires pinpointing potential candidates. However, there are no objective methods in place to measure software reuse. This makes it challenging to identify highly reusable software. Software reuse research mainly addresses two hurdles: 1) identifying reusable candidates effectively and efficiently, and 2) selecting high-quality software components that improve maintainability and extensibility. This paper proposes automating software reuse prediction by leveraging machine learning (ML) algorithms, enabling future research and practitioners to better identify highly reusable software. Our approach uses cross-project code clone detection to establish the ground truth for software reuse, identifying code clones across popular GitHub projects as indicators of potential reuse candidates. Software metrics were extracted from Maven artifacts and used to train classification and regression models to predict and estimate software reuse. The average F1-score of the ML classification models is 77.19%. The best-performing model, Ridge Regression, achieved an F1-score of 79.17%. Additionally, this research aims to assist developers by identifying key metrics that significantly impact software reuse. Our findings suggest that the file-level PUA (Public Undocumented API) metric is the most important factor influencing software reuse. We also present suitable value ranges for the top five important metrics that developers can follow to create highly reusable software. Furthermore, we developed a tool that utilizes the trained models to predict the reuse potential of existing GitHub projects and rank Maven artifacts by their domain. [ABSTRACT FROM AUTHOR]

Project-based learning (PBL) is a learning technology praised for its ability to grow domain-specific and domain-general skills and related knowledge and attitudes. However, consistently designing effective PBL experiences is challenging, primarily due to the lack of instructor support and guidance for designing PBL experiences aligned with learning principles. This study designs a framework that aids software engineering instructors in designing, administering, and refining structured PBL experiences. We follow design science research to iteratively understand the problem of effective PBL experiences and design solutions for consistently creating them. We validate our intermediary designs by instantiating PBL experiences for a fourth-year undergraduate course on software design, running the experience, and reflecting on the gathered data. We repeat this process four times. Through the lens of pragmatism, we validate our final design by interviewing five software engineering instructors to examine the applicability of our framework to their courses. The resulting framework is based on the four-component instructional design model, where each instantiated PBL experience is a sequence of learning tasks. The framework is divided into four composite activities to reduce the cost of authoring PBL experiences. The paper includes heuristics and examples to aid instructors in using our framework. The designed framework has successfully created four large-scale PBL experiences, each lasting three months and including teams of 16 students. The case study results and the interviewees' perceptions indicate that the framework is useful for higher education study programs, coding boot camps, and onboarding corporate training programs. [ABSTRACT FROM AUTHOR]

The increased demand for high-quality software makes software reliability modeling an important research area. The number of failures that occur during the testing phase over a specified period of time is usually used to determine how reliable a software system is. There are numerous reliability models suggested, addressing the various aspects of testing and debugging processes. The issue with those models is that they assume testing operations are conducted under controlled circumstances. The software testing environment, however, is not always consistent or ideal and its efficacy can be influenced by many uncertain factors such as testing effort, testing coverage, testing skills, hardware and software configuration differences, time and budget constraints, and incomplete or inaccurate requirements. The proposed model incorporates a special parameter that represents the effectiveness of the testing environment. The model was validated on two distinct datasets and it was compared to six well known models using four different goodness-of-fit criteria. Both datasets revealed that the suggested model outperforms the other models in prediction accuracy. [ABSTRACT FROM AUTHOR]

The article explores the use of contralateral nasofrontal trephination (CNT) and posterior palatectomy (PP) to enhance the inferior access provided by the endoscopic endonasal approach (EEA) to the craniovertebral junction (CVJ). Surgical dissections on cadaveric specimens showed that EEA combined with CNT or PP offered greater target exposure, surgical freedom, reach, and angles of attack compared to the traditional EEA corridor. The study suggests that EEA + CNT may be a superior technique for minimally invasive access to the CVJ region. [Extracted from the article]

Software defect prediction (SDP) is a technique that uses known software features and defect information to predict target software defects. It helps reduce software development costs, save development time, and improve software quality. The performance of SDP has always been the goal of this field. Many researchers have found that software features and their processing have a significant impact on the results of SDP. However, few reviews have summarized the features types, use, utility, processing and effect. So, we searched the six databases for SDP empirical papers. One hundred and forty-eight researches were obtained by using the inclusion and exclusion strategies. The information of feature types, processing methods, prediction schemes, modeling techniques, data sets, and performance evaluation metrics in these papers was collected and analyzed. We find that the rise of semantic and network features has broken the situation where only software metrics were used for SDP. These features demonstrate significant potential in prediction, especially semantic features. Deep learning is used increasingly in SDP, and deep learning feature extraction replaces the original feature selection based on machine learning. Although SDP performance continues to improve, issues such as data quality, feature stability, and scenes selection still deserve future research attention. [ABSTRACT FROM AUTHOR]

Sheet metal parts are the fundamental components for realizing the structural characteristics of aircraft, and their design efficiency and quality directly influence the cost and performance of products. Currently, many aviation enterprises rely on experiential methods to inspect the design quality of parts, leading to challenges such as inconsistent inspection specifications, low efficiency and a high likelihood of errors. To address these issues, a manufacturability analysis method for sheet metal part is proposed based on rule reasoning. Firstly, the topological information of sheet metal part is classified and combined according to geometric characteristics and its topological relationships, and the topological face set of the features is constructed. Secondly, the manufacturing feature recognition methods based on rule reasoning is proposed, and standardized information models are established based on these manufacturing features. Subsequently, the manufacturability rules are established based on the process criteria in the actual processing requirements. The attribute information contained in the design model is compared with the process rule information to identify the attributes that do not comply with the processing requirements. Finally, a manufacturability evaluation software system for aircraft sheet metal part is developed, and several typical parts containing various concave features are successfully tested. [ABSTRACT FROM AUTHOR]

This article represents the Replicated Computational Results (RCR) related to our TOSEM paper "A Machine Learning Approach for Automated Filling of Categorical Fields in Data Entry Forms," where we proposed LAFF, an approach to automatically suggest possible values of categorical fields in data entry forms, which is a common user interface feature in many software systems. In this RCR report, we provide details about our replication package. We make available the different scripts needed to fully replicate the results obtained in our paper. [ABSTRACT FROM AUTHOR]

Modern video games are extremely complex software systems and, as such, they might suffer from several types of post-release issues. A particularly insidious issue is constituted by drops in the frame rate (i.e., stuttering events), which might have a negative impact on the user experience. Stuttering events are frequently documented in the million of hours of gameplay videos shared by players on platforms such as Twitch or YouTube. From the developers' perspective, these videos represent a free source of documented "testing activities." However, especially for popular games, the quantity and length of these videos make impractical their manual inspection. We introduce HASTE, an approach for the automatic detection of stuttering events in gameplay videos that can be exploited to generate candidate bug reports. HASTE firstly splits a given video into visually coherent slices, with the goal of filtering-out those that not representing actual gameplay (e.g., navigating the game settings). Then, it identifies the subset of pixels in the video frames which actually show the game in action excluding additional elements on screen such as the logo of the YouTube channel, on-screen chats, and so forth. In this way, HASTE can exploit state-of-the-art image similarity metrics to identify candidate stuttering events, namely subsequent frames being almost identical in the pixels depicting the game. We evaluate the different steps behind HASTE on a total of 105 videos showing that it can correctly extract video slices with a 76% precision, and can correctly identify the slices related to gameplay with a recall and precision higher than 77%. Overall, HASTE achieves 71% recall and 89% precision for the identification of stuttering events in gameplay videos. [ABSTRACT FROM AUTHOR]

The fractal property has been regarded as a fundamental property of complex networks, characterizing the self-similarity of a network. Such a property is usually numerically characterized by the fractal dimension metric, and it not only helps the understanding of the relationship between the structure and function of complex networks but also finds a wide range of applications in complex systems. The existing literature shows that class-level software networks (i.e., class dependency networks) are complex networks with the fractal property. However, the fractal property at the feature (i.e., methods and fields) level has never been investigated, although it is useful for measuring class complexity and predicting bugs in classes. Furthermore, existing studies on the fractal property of software systems were all performed on un-weighted software networks and have not been used in any practical quality assurance tasks such as bug prediction. Generally, considering the weights on edges can give us more accurate representations of the software structure and thus help us obtain more accurate results. The illustration of an approach's practical use can promote its adoption in practice. In this article, we examine the fractal property of classes by proposing a new metric. Specifically, we build a Feature-Level Software Network (FLSN) for each class to represent the methods/fields and their couplings (including coupling frequencies) within the class and propose a new metric, Fractal Dimension for Classes (FDC), to numerically describe the fractal property of classes using FLSNs, which captures class complexity. We evaluate FDC theoretically against Weyuker's nine properties, and the results show that FDC adheres to eight of the nine properties. Empirical experiments performed on a set of 12 large open source Java systems show that (i) for most classes (larger than \(96\%\)), there exists the fractal property in their FLSNs, (ii) FDC is capable of capturing additional aspects of class complexity that have not been addressed by existing complexity metrics, (iii) FDC significantly correlates with both the existing class-level complexity metrics and the number of bugs in classes, and (iv) FDC, when used together with existing class-level complexity metrics, can significantly improve bug prediction in classes in three scenarios (i.e., bug-count, bug-classification, and effort-aware) of the cross-project context, but in the within-project context, it cannot. [ABSTRACT FROM AUTHOR]

Objective: The complexity of software systems, with their multifaceted functionalities and intricate source code structures, poses significant challenges for developers in identifying and resolving bugs. This study aims to address these challenges by proposing an efficient bug localization method that improves the accuracy and effectiveness of identifying faulty code based on bug reports. Method: We introduce a novel bug localization approach that integrates a Long Short-Term Memory (LSTM) attention mechanism with top-K code similarity learning. The proposed method preprocesses bug reports and source code files, calculates top-K code similarities using the BM25 algorithm, and trains an LSTM-Attention model to predict the most relevant buggy source code files. Results: The model was evaluated on six open-source projects (Tomcat, AspectJ, Birt, Eclipse, JDT, SWT) and demonstrated significant improvements over the baseline method, DNNLoc. Notably, the proposed approach improved accuracy across all projects, with average gains of 18% in prediction accuracy compared to the baseline. Conclusion: This study highlights the efficacy of combining similarity learning with attention mechanisms for bug localization. By streamlining debugging workflows and enhancing predictive accuracy, the proposed method offers a practical solution for improving software quality and reducing development costs. [ABSTRACT FROM AUTHOR]

Copyright of HMD: Praxis der Wirtschaftsinformatik is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The composition of web service is the best chance provided by Service-Oriented Computing and Service-Oriented Architecture as it gives real competitive benefits for some industrial and technological actors via presenting them with the probability to guarantee fast and inexpensive improvement of collaborative and distributed software applications. Here, a novel technique is introduced, which contains several phases for better web services. At first, the requirements specification phase is enabled with a set of requirements such as non-functional and functional requirements. Next to the requirements specification stage, the discovery stage is enabled to choose the suitable web services that have high-matching profiles with the developer's requirement set. Here, for a semantic matching algorithm, a new hybrid similarity measure is developed. Additionally, among the group of candidate services that the discovery phase returned, the best service is selected during the selection step. Then, hybrid Squeeze_Long Short-Term Memory (Squeeze_LSTM) is used for choosing the best service and it is designed by the formation of SqueezeNet and LSTM. The Semantic Web Services are finally implemented. The efficiency of the Squeeze_LSTM is evaluated and has achieved a superior precision of 0.909, recall of 0.890, and response time of 6.461S. [ABSTRACT FROM AUTHOR]

This paper presents a refinement of a method that simulates flow- and pressure-regulating valves by replacing them with pipes and adjusting the resistances (diameters) of those pipes to meet the valve settings. The method, referred to as the replacing pipe method (RPM), is readily integrable into the global gradient algorithm, which is the heart of the commonly used EPANET 2 water distribution system modeling software. The RPM proved to be more robust, yet slower than the EPANET 2 valve handling heuristics. In this work, the RPM was modified to increase its computational speed. The modification involved the RPM check valve handling algorithm and condition for updating the diameters of the valve-replacing pipes. The modified RPM was tested as part of a hybrid simulator, which switches from the EPANET 2 valve handling heuristics to the RPM when the former fails. The test networks included a large network with thousands of nodes, pipes, and valves, and a case of an ill-posed problem. As the testing showed, the modified RPM offers a significant reduction in the number of iterations compared to its previous version. [ABSTRACT FROM AUTHOR]

Copyright of Automatisierungstechnik is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objective: This study aimed to assess the feasibility of computer model-based evaluation of knee joint functional capacity in comparison with manual assessment. Methods: This study consisted of two phases: (1) developing an automatic knee joint action recognition and classification system on the basis of improved YOLOX and (2) analyzing the feasibility of assessment by the software system and doctors, identifying the knee joint function of patients, and determining the accuracy of the software system. We collected 40–50 samples for use in clinical experiments. The datasets used in this study were collected from patients admitted to the Joint Surgery Center. In this study, the knee joint assessment items included stair climbing, walking on uneven surfaces, and knee joint function. To assess the computer model's automatic evaluation of knee joint function, MedCalc 20 statistical software was used to analyze the consistency of the Lequesne functional index between the computer model's automated determinations and manual independent assessments. Results: The weighted kappa coefficients between the doctors' assessments and the software system's assessments were 0.76 (95% confidence intervals:0.59 ~ 0.92) for climbing up and down stairs, 0.64 (95% confidence intervals:0.45 ~ 0.82) for walking on uneven floors, and 0.68 (95% confidence intervals:0.53 ~ 0.84) for the Lequesne functional index, indicating good consistency between the assessments of the software system and doctors. Conclusion: This paper introduces an automatic knee joint action recognition and classification method based on improved YOLOX. By comparing the results obtained by orthopedic doctors and the software system, the feasibility of this software system was validated in the clinic. [ABSTRACT FROM AUTHOR]

This article introduces StreamChol, a software for developing and applying mechanistic models to predict cholestasis. StreamChol is a Streamlit application, usable as a desktop application or web-accessible software when installed on a server using a docker container. StreamChol allows a seamless integration of pharmacokinetic analyses with Machine Learning models. This integration not only enables cholestasis prediction but also opens avenues for predicting other toxicological endpoints requiring similar integrations. StreamChol's Docker containerization also streamlines deployment across diverse environments, addressing potential compatibility issues. StreamChol is distributed as open-source under GNU GPL v3, reflecting our commitment to open science. Through StreamChol, researchers are offered a potent tool for predictive modelling in toxicology, harnessing its strengths within an intuitive and user-friendly interface, without the need for any programming knowledge. Scientific contribution This work offers a user-friendly web-based tool for cholestasis prediction and a complete workflow for creating web platforms that require the combination of both programming languages, R and Python. [ABSTRACT FROM AUTHOR]

In prospective advancements, it is anticipated that software-defined networking (SDN) will establish itself as the principal framework for the implementation of heterogeneous networks. Unlike traditional networking paradigms, SDN differentiates the control and data planes, thereby enabling enhanced routing and management of traffic across various domains. The controllers located within the control plane are responsible for programming the forwarding devices situated in the data plane, whereas the highest layer, referred to as the application plane, enforces regulatory policies and coordinates network programming. Interfacing mechanisms are utilized across the different layers of SDN to ensure effective communication. Nonetheless, SDN faces challenges related to traffic distribution, such as load imbalance, which could negatively affect overall network performance. In response, developers have formulated a range of SDN load-balancing solutions intended to improve the efficiency of SDN. Furthermore, owing to the swift advancements in the domain of artificial intelligence (AI), researchers are investigating the viability of incorporating AI methodologies into SDN to optimize resource utilization and enhance overall performance. This survey is organized as follows: First, it provides an analysis of the SDN architecture and investigates the load balancing challenges present within SDN. Second, it classifies AI-driven load balancing strategies and critically assesses these mechanisms from multiple perspectives, including the algorithms/techniques utilized, the particular issues addressed, and their corresponding advantages and disadvantages. Third, it consolidates the metrics used to evaluate the effectiveness of these techniques. Finally, it identifies the emerging trends and challenges associated with AI-enhanced load balancing for future research endeavors. [ABSTRACT FROM AUTHOR]

Background: Since 2015, the Complex Reviews Synthesis Unit (CRSU) has developed a suite of web-based applications (apps) that conduct complex evidence synthesis meta-analyses through point-and-click interfaces. This has been achieved in the R programming language by combining existing R packages that conduct meta-analysis with the shiny web-application package. The CRSU apps have evolved from two short-term student projects into a suite of eight apps that are used for more than 3,000 h per month. Aim: Here, we present our experience of developing production grade web-apps from the point-of-view of individuals trained primarily as statisticians rather than software developers in the hopes of encouraging and inspiring other groups to develop valuable open-source statistical software whilst also learning from our experiences. Key challenges: We discuss how we have addressed challenges to research software development such as responding to feedback from our real-world users to improve the CRSU apps, the implementation of software engineering principles into our app development process and gaining recognition for non-traditional research work within the academic environment. Future developments: The CRSU continues to seek funding opportunities both to maintain and further develop our shiny apps. We aim to increase our user base by implementing new features within the apps and building links with other groups developing complementary evidence synthesis tools. [ABSTRACT FROM AUTHOR]

Early‐bird communication is a communication/computation overlap technique that leverages fine‐grained communication to improve application run‐time. Communication is divided such that each individual thread can initiate transmission of its portion of the data upon completion rather than waiting for a dedicated communication phase. The benefit of early‐bird communication depends on the completion timing of the individual threads: On the one hand, if all threads are complete at nearly the same time, the overheads of sending multiple messages will accumulate, leading to performance that is worse than if a single message had been sent. On the other hand, if thread completions are spread out in time, those that complete earlier can send data while others continue working, leading to performance that is better than if a single message had been sent. The challenge is that the completion times are currently unknown and can vary based on application, problem size, system software, and underlying hardware. In this paper, we address this lacuna by measuring and evaluating the potential overlap afforded by early‐bird communication for a selection of proxy applications. These measurements help us understand whether a given application could benefit from early‐bird communication. We present our technique for gathering this data and evaluate data collected from three proxy applications: MiniFE, MiniMD, and MiniQMC. Each application is run on three systems with distinct CPU architectures and strong scales across three run sizes. To characterize the behavior of these workloads, we study the trends of thread timings at both a macro level, across all threads across all runs of an application, and a micro level, that is, within a single process of a single run. We observe that our tested applications exhibit significantly different thread arrival distributions. The machine used had a significant impact, with the window of potential overlap varying by as much as an order of magnitude. [ABSTRACT FROM AUTHOR]

The rising craze of sensor enabled mobile devices promotes its usage in Mobile Cloud Computing (MCC), Mobile Edge Computing (MEC) and other distributed computing environments derived from the cloud computing environment. As the computing paradigm shifts from centralized to distributed computing and mobile devices are getting smarter and resource rich, it facilitate the user to do computation to its proximity. Hence, it is quite useful to incorporate the Wireless Sensor Networks (WSN) with distributed computing environment to better cater to the user needs. The proposed work enhances the MCC and MEC by incorporating sensor enabled computing along with the application of energy optimization techniques such as coyote optimization, Fuzzy Logic (FL), data redundancy and data compression. A new framework called Sensor Enabled-Scalable Key Parameter Yield of Resources (SE-SKYR) framework is proposed in this research work by integrating SKYR framework with cluster-based sensing mechanism. The proposed work uses SKYR framework which is a cloudlet based MCC framework and works well for MEC as well. Cloudlet is used as the main computing component available at the local level which suits both MEC and MCC. The existing system uses the concept of relay node to transmit data packets in transmission path from sensor nodes to server via edge cloud and hence causes delay in transmission of data. In the proposed work, we have introduced a Scalable Energy Optimization of Resource (SEOR) algorithm to optimize the energy consumption by various resources. SE-SKYR framework along with SEOR algorithm addresses the problems faced by the existing system. The complexity of the proposed SEOR algorithm is less as compared to its existing counterparts and is also comprehended from the results. [ABSTRACT FROM AUTHOR]

This study investigated the effectiveness of deep learning models in assessing the reliability of software systems and the application of recurrent neural network algorithms in reliability prediction. A hybrid model consisting of a combination of LSTM and GRU models is proposed to predict the reliability of software systems. Along with historical data collected during testing and implementation, several environmental factors covering the software life cycle and affecting its reliability, as well as the complexity of the software code, are taken as input. Based on these data, a new method for expert assessment of software reliability is proposed, and the calculated expert scores are taken as output. The proposed model is trained based on these values. This is a comprehensive approach to assessing the reliability of software systems. [ABSTRACT FROM AUTHOR]

Testing query-based systems (QBSs) presents significant challenges due to the absence of ground truth for validation and the extensive time and effort required for manual testing. This paper addresses these challenges by proposing an approach that assists testers in identifying metamorphic relations (MRs) for metamorphic testing (MT) instead of solely und exhaustively relying on prerequisite domain knowledge. MT is an approach rising in popularity employed to alleviate the oracle problem by applying. input transformation rules (MRs) to a program. The proposed approach helps the tester by prompting MRs that describe the relationships between inputs and outputs. enabling fault detection when the expected relationship is not met. Unlike traditional testing approaches. this approach does not rely on pre-defined test cases or concrete ground truth. making it suitable for the testing of real-world QBSs. Furthermore, the proposed approach can be combined with other testing methods such as combinatorial testing and fuzz testing. expanding the possibilities for QBS testing. A conducted case study of a real-world web application demonstrates the applicability and potential of the proposed approach. Overall. this research contributes to advancing the field of metamorphic testing and provides a valuable tool for QBS testers to enhance their testing efficiency. [ABSTRACT FROM AUTHOR]

Copyright of Computer und Recht is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Summary: Software architecture assists developers in addressing non‐functional requirements and in maintaining, debugging, and upgrading their software systems. Consequently, consistency between the designed architecture and the implemented software system itself is important; without this consistency the non‐functional requirements targeted may not be addressed and architectural documentation may mis‐direct maintenance efforts that target the associated code‐base. But often, when software is initially implemented or subsequently evolved, the designed architecture and software architecture become inconsistent, with the implemented structure degraded due to issues like developer time‐pressures, or ambiguous communication of the designed architecture. In such cases, Software Architecture Recovery (SAR) or consistency approaches can be applied to reconstruct the architecture of the software system and possibly to compare it to/re‐align it with the designed architecture. Many SAR approaches have been proposed in the research. However, choosing an appropriate architecture recovery approach for software systems is still an open issue. Consequently, this research aims to conduct a tertiary‐mapping study based on available secondary studies of architecture recovery approaches, to uncover important characteristics, towards the selection of appropriate SAR approaches. This research has aggregated 13 secondary studies and 10 primary studies beyond 2020 from 5 databases and, in doing so, identified 111 architecture recovery approaches. Based on these approaches, a taxonomy, containing nine main SAR‐selection categories is proposed and a framework (in the form of a supporting tool to help developers select an appropriate SAR approach) has been developed. Finally, this research identifies six potential open research gaps related to the underlying research that could be helpful for guiding research in the future. [ABSTRACT FROM AUTHOR]

The article discusses the importance of measuring and reporting benefits in project management, emphasizing the need to choose meaningful metrics that align with stakeholders' interests. Lead and lag measures are explained, with lead measures being crucial for making timely project decisions. The article also touches on the significance of framing benefits in terms of mission advancement rather than just financial gains. Practical tips for effective benefits management are provided, along with a case study on nuclear decommissioning. The author, Dr. Hugo Minney, is an experienced project management consultant with expertise in benefits management and stakeholder engagement. [Extracted from the article]

Performance is arguably the most crucial attribute that reflects the quality of a configurable software system. However, given the increasing scale and complexity of modern software, modeling and predicting how various configurations can impact performance becomes one of the major challenges in software maintenance. As such, performance is often modeled without having a thorough knowledge of the software system, but relying mainly on data, which fits precisely with the purpose of deep learning. In this article, we conduct a comprehensive review exclusively on the topic of deep learning for performance learning of configurable software, covering 1,206 searched papers spanning six indexing services, based on which 99 primary papers were extracted and analyzed. Our results outline key statistics, taxonomy, strengths, weaknesses, and optimal usage scenarios for techniques related to the preparation of configuration data, the construction of deep learning performance models, the evaluation of these models, and their utilization in various software configuration-related tasks. We also identify the good practices and potentially problematic phenomena from the studies surveyed, together with a comprehensive summary of actionable suggestions and insights into future opportunities within the field. To promote open science, all the raw results of this survey can be accessed at our repository: https://github.com/ideas-labo/DCPL-SLR. [ABSTRACT FROM AUTHOR]

To meet the increasing demand for customized software, highly configurable systems become essential in practice. Such systems offer many options to configure, and ensuring the reliability of these systems is critical. A widely used evaluation metric for testing these systems is \(t\) -wise coverage, where \(t\) represents testing strength, and its value typically ranges from 2 to 6. It is crucial to design effective and efficient methods for generating test suites that achieve high \(t\) -wise coverage. However, current state-of-the-art methods need to generate large test suites for achieving high \(t\) -wise coverage. In this work, we propose a novel method called LS-Sampling-Plus that can efficiently generate test suites with high \(t\) -wise coverage for \(2\leq t\leq 6\) while being smaller in size compared to existing state-of-the-art methods. LS-Sampling-Plus incorporates many core algorithmic techniques, including two novel scoring functions, a dynamic mechanism for updating sampling probabilities, and a validity-guaranteed systematic search method. Our experiments on various practical benchmarks show that LS-Sampling-Plus can achieve higher \(t\) -wise coverage than current state-of-the-art methods, through building a test suite of the same size. Moreover, our evaluations indicate the effectiveness of all core algorithmic techniques of LS-Sampling-Plus. Furthermore, LS-Sampling-Plus exhibits better scalability and fault detection capability than existing state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Recent years have seen a rise in Neural Program Repair (NPR) systems in the software engineering community, which adopt advanced deep learning techniques to automatically fix bugs. Having a comprehensive understanding of existing systems can facilitate new improvements in this area and provide practical instructions for users. However, we observe two potential weaknesses in the current evaluation of NPR systems: ① published systems are trained with varying data, and ② NPR systems are roughly evaluated through the number of totally fixed bugs. Questions such as what types of bugs are repairable for current systems cannot be answered yet. Consequently, researchers cannot make target improvements in this area and users have no idea of the real affair of existing systems. In this article, we perform a systematic evaluation of the existing nine state-of-the-art NPR systems. To perform a fair and detailed comparison, we (1) build a new benchmark and framework that supports training and validating the nine systems with unified data and (2) evaluate re-trained systems with detailed performance analysis, especially on the effectiveness and the efficiency. We believe our benchmark tool and evaluation results could offer practitioners the real affairs of current NPR systems and the implications of further facilitating the improvements of NPR. [ABSTRACT FROM AUTHOR]

Background: Recently, machine and deep learning (ML/DL) algorithms have been increasingly adopted in many software systems. Due to their inductive nature, ensuring the quality of these systems remains a significant challenge for the research community. Traditionally, software systems were constructed deductively, by writing explicit rules that govern the behavior of the system as program code. However, ML/DL systems infer rules from training data i.e., they are generated inductively. Recent research in ML/DL quality assurance has adapted concepts from traditional software testing, such as mutation testing, to improve reliability. However, it is unclear if these proposed testing techniques are adopted in practice, or if new testing strategies have emerged from real-world ML deployments. There is little empirical evidence about the testing strategies. Aims: To fill this gap, we perform the first fine-grained empirical study on ML testing in the wild to identify the ML properties being tested, the testing strategies, and their implementation throughout the ML workflow. Method: We conducted a mixed-methods study to understand ML software testing practices. We analyzed test files and cases from 11 open-source ML/DL projects on GitHub. Using open coding, we manually examined the testing strategies, tested ML properties, and implemented testing methods to understand their practical application in building and releasing ML/DL software systems. Results: Our findings reveal several key insights: (1) The most common testing strategies, accounting for less than 40%, are Grey-box and White-box methods, such as Negative Testing, Oracle Approximation, and Statistical Testing. (2) A wide range of \(17\) ML properties are tested, out of which only 20% to 30% are frequently tested, including Consistency, Correctness, and Efficiency. (3) Bias and Fairness is more tested in Recommendation (6%) and Computer Vision (CV) (3.9%) systems, while Security and Privacy is tested in CV (2%), Application Platforms (0.9%), and NLP (0.5%). (4) We identified 13 types of testing methods, such as Unit Testing, Input Testing, and Model Testing. Conclusions: This study sheds light on the current adoption of software testing techniques and highlights gaps and limitations in existing ML testing practices. [ABSTRACT FROM AUTHOR]

Software-Defined Radio (SDR) technology has been a very popular and powerful prototyping device for decades. It finds applications in both fundamental research or application-oriented tasks. Additionally, the continuing rise of the Internet of Things (IoT) necessitates the validation, processing, and decoding of a large number of received signals. This is where SDRs can be a valuable instrument. In this work, we present an open-source software system using GNU Radio and SDRs, which improves the comprehension of the physical layer aspects of Internet of Things communication systems. Our implementation is generic and application-agnostic. Therefore, it can serve as a learning and investigation instrument for any IoT communication system. Within this work, we implement the open-source DASH7 Alliance Protocol (D7AP). The developed software tool can simulate synthetic DASH7 signals, process recorded data sets, and decode the received DASH7 packets in real time using an SDR front-end. The software is accompanied by three data sets collected in controlled, indoor, and suburban environments. The experimental results revealed that the total packet losses of the data sets were 0%, 2.33%, and 16.67%, respectively. Simultaneously, the three data sets were received by a dedicated DASH7 gateway with total packet losses of 0%, 3.83%, and 7.92%, respectively. [ABSTRACT FROM AUTHOR]

In today's dynamic development environments, agile methodologies like Scrum are essential for effective software project management. Despite its popularity, the Scrum framework's reliance on subjective intuition during the sprint planning process can lead to inconsistencies and project delays. This study aims to enhance the sprint planning phase by integrating the BeCoMe method, which is a mathematical approach designed to optimize task selection through structured compromise solutions. Utilizing a soft systems methodology, this research identifies and analyzes the existing inefficiencies in Scrum's planning process. The implementation of the BeCoMe method in a real-world case study demonstrated significant improvements in task completion rates and overall project efficiency. The method's structured process reduces biases, fosters team consensus, and enhances decision-making accuracy. The findings suggest that incorporating the BeCoMe method into Scrum can substantially mitigate risks, save time, and improve project outcomes by ensuring a more objective and data-driven approach to sprint planning. These insights are crucial for developers managing modern software projects, offering a robust framework for enhancing planning efficiency and success rates. [ABSTRACT FROM AUTHOR]

System‐level testing of avionics software systems requires compliance with different international safety standards such as DO‐178C. An important consideration of the avionics industry is automated test data generation according to the criteria suggested by safety standards. One of the recommended criteria by DO‐178C is the modified condition/decision coverage (MC/DC) criterion. Current model‐based test data generation approaches use constraints written in Object Constraint Language (OCL) and apply search techniques to generate test data. These approaches either do not support MC/DC criterion or suffer from performance issues while generating test data for large‐scale avionics systems. In this paper, we propose an effective way to automate MC/DC test data generation during model‐based testing. We develop a strategy that utilizes case‐based reasoning (CBR) and range reduction heuristics designed to solve MC/DC‐tailored OCL constraints. We performed an empirical study to compare our proposed strategy for MC/DC test data generation using CBR, range reduction, both CBR and range reduction, with an original search algorithm, and random search. We also empirically compared our strategy with existing constraint‐solving approaches. The results show that both CBR and range reduction for MC/DC test data generation outperform the baseline approach. Moreover, the combination of both CBR and range reduction for MC/DC test data generation is an effective approach compared to existing constraint solvers. [ABSTRACT FROM AUTHOR]

During the early phases of software system development, error detection can be challenging due to the complexity of both the requirements and the operating environments. This paper advocates for the utilization of formal modelling and verification throughout the first phases of systems development to promptly detect and correct errors. The formalism employed throughout is Event‐B, which is backed by the Rodin toolset. To conquer requirements complexity, the frameworks of set theory and first‐order logic are employed, which provide the necessary tools for formalizing and analysing the properties and behaviours associated with Event‐B. Also, we detail the way in which modelling may be used to achieve abstraction, as well as the way in which refinement can be used to manage complexity through layering. Furthermore, we emphasize the significance of model validation and verification in improving the precision of formal models and requirements in IoT communication systems. The model is exemplified using a Content‐Based Publish Subscribe System (CBPS), with a special emphasis on a fire alarm system as a motivating example. [ABSTRACT FROM AUTHOR]

The development and use of scientific applications have become an integral part of conducting large-scale experiments in various fields of research that require high-performance computing and big data processing. In the context of developing such applications, non-trivial problems arise in the concerted description and further use of schemes, software, and computational resources to solve subject domain problems of a specific application. Research productivity has become highly dependent on the degree of automation in the preparation and execution of experiments in a computing environment whose resources may be distributed and heterogeneous. Many approaches to the experiment automation are based on workflows as a structure for formalizing and specifying data processing and high-performance computing using distributed applications. Within such approaches, developers and end-users work with workflow management systems for the collaborative development and use of distributed scientific applications. Nowadays, service-oriented applications are coming to the fore. However, there is a wide range spectrum of problems related to the support of modular scientific applications, the standardization of their components and interfaces, the use of heterogeneous information and computing resources, and organization of interdisciplinary research within service-oriented architecture. Known workflow management systems do not fully address the above problems. In this regards, we consider relevant aspects of organizing service-oriented computing in a heterogeneous distributed computing environment. We propose a new framework for creating service-oriented and workflow-based scientific applications. The paper shows that the proposed framework significantly extends and complements the capabilities of systems for such purposes. We also demonstrate the reduction in labour costs associated with the preparation and execution of experiments. [ABSTRACT FROM AUTHOR]

Addressing software defects is an ongoing challenge in software development, and effectively managing and resolving defects is vital for ensuring software reliability, which is in turn a crucial quality attribute of any software system. Software defect prediction supported by machine learning (ML) methods offers a promising approach to address the problem of software defects. However, one common challenge in ML-based software defect prediction is the issue of data imbalance. In this paper, we present an empirical study aimed at assessing the impact of various class balancing methods on the issue of class imbalance in software defect prediction. We conducted a set of experiments that involved nine distinct class balancing methods across seven different classifiers. We used datasets from the PROMISE repository, provided by the NASA software project. We also employed various metrics including AUC, Accuracy, Precision, Recall, and the F1 measure to gauge the effectiveness of the different class balancing methods. Furthermore, we applied hypothesis testing to determine any significant differences in metric results between datasets with balanced and unbalanced classes. Based on our findings, we conclude that balancing the classes in software defect prediction yields significant improvements in overall performance. Therefore, we strongly advocate for the inclusion of class balancing as a preprocessing step in this domain. [ABSTRACT FROM AUTHOR]

With the increment in software development complexity, approaches such as domain-driven design (DDD) are needed to tackle contemporary business domains. DDD is already being used in various software projects with different architectural styles. Evidence in the literature suggests using DDD to mitigate challenges in the Microservices Architecture style. Although some studies have explored the decomposition of business domains or legacy monolithic systems into microservices, there is a lack of concrete information regarding the practical implementation of DDD in this architectural style. This has led to ambiguity regarding the use of DDD in developing microservices-based systems. The paper systematizes findings on the purpose of using DDD, its patterns, associated technologies, and techniques. A systematic literature review of 35 articles was conducted. Thematic analysis was employed to identify five high-order themes and 11 themes. Based on our analysis, we have concluded that microservice identification emerges as the primary motivation behind developers' adoption of DDD. However, it is important to note that this utilization manifests with variations across the literature. Finally, our analysis found several challenges and benefits in the use of DDD in microservices development. [ABSTRACT FROM AUTHOR]

Behavior-driven development (BDD) has been gaining relevance since its inception due to its focus on specifying system behavior through concrete examples, facilitating collaboration, and ensuring that development aligns with business requirements. However, it is essential to understand the scenarios in which BDD has been used as a development method and to provide a documented overview of its implementation. This research offers a thematic synthesis of BDD, elucidating its challenges, benefits, and implications in software development. The study identifies prevalent trends and common themes in BDD adoption and implementation by drawing insights from various sources. The primary objective was to analyze BDD's characteristics, scenarios, benefits, and challenges in software projects while furnishing actionable insights to bolster BDD's adaptability among development teams. This work adheres to the guidelines of search-based software engineering to compile 23 studies in four academic databases, and a robust method for thematic synthesis is presented. The results underscore BDD's value as a methodology bridging the technical-non-technical stakeholder divide, ensuring close alignment of software development with business objectives. While initial adoption hurdles may exist, the study reveals substantial long-term benefits regarding software quality and stakeholder satisfaction. Future research avenues should prioritize developing more efficient training programs and tools to streamline BDD adoption across diverse development environments. This study contributes to the comprehension of BDD by offering a thematic synthesis of its application in software development, explaining critical success factors, and proffering recommendations to address common challenges. [ABSTRACT FROM AUTHOR]

The Message Queuing Telemetry Transport (MQTT) is one of the most commonly used standard messaging protocols that enable the Internet of Things (IoT). The messaging standard specification document defines the transactions, attributes, conventions, timers, values, types, structures, etc. MQTT encounters wide implementation possibilities: hardware vendors need to implement MQTT drivers for their devices, software vendors need to implement it in their solutions, developers and researchers need an MQTT simulation framework, etc. The IoT technology can enable unlimited number of use cases, and each case has its implementation of the messaging protocol. From the above facts, on the basis that any user needs to have a deep understanding of the specification in order to implement the protocol and also developers need to study it well for improving the protocol, there is a need for a standard conceptual way that conveys the messaging standard specifications, integrates the specifications of the use cases built upon with a more abstracted mental representation, improves the communication between developers, and facilitates the protocols and applications developments. This paper addresses this issue by proposing a graphical conceptual modeling approach for MQTT-based IoT applications called Example-oriented System Conceptual Modeling for MQTT use cases (EoSCM-MQTT). The paper explains the EoSCM-MQTT and gives an example of its use in an MQTT simulation implementation for a specific IoT application: smart parking. EoSCM-MQTT was validated using three methods: comparison, analysis, and implementation. The details and results of the implementation and the conclusions from the comparison and analysis indicated that EoSCM-MQTT has the required characteristics of conceptual models: good syntactic semantic, and pragmatic quality, clarity, coherence, accuracy, flexibility, reliability, and effectiveness. [ABSTRACT FROM AUTHOR]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

At a time when technology is spreading rapidly and widely, technology has become a necessity in daily life and practical life, and this led to the emergence of many cyber-physical systems (CPS), among which the medical cyber-physical systems (MCPS) have emerged, which is one application of CPS that is particularly concerned with patients and health care providers. These systems generate a large amount of data that may be difficult to process and store, in addition to unauthorized access to these systems, which affects their protection. This paper presents a proposed Authenticated Medical Cyber-Physical Blockchain (AMCB) model in Medical Cyber-Physical Systems (MCPS) using Blockchain technology, machine learning, and physically unclonable function (PUF) to enhance the authentication process by controlling the access to electronic health records (EHR) that stored on the cloud server and physical authentication. In addition, it analyzes the data generated from the authorized devices to ensure that the devices do not contain malicious. This paper presents an experiment based on the medical Internet of Things (IoMT) dataset using K-nearest neighbors (k-NN), Random Forest (RF), Naive Bayes and Supporting Vector Machine (SVM) for malicious detection to test the proposed model's accuracy. The Random Forest (RF) classifier gave more accurate results based on the preliminary results with a slight difference from k-NN. [ABSTRACT FROM AUTHOR]

Aims Methods Results Conclusion The Drug Utilization 90% Index (DU90%), the number of medicines making up 90% of a doctor's prescribing, is a simple tool that can be used to describe core prescribing patterns. This research aimed to pilot the application of the DU90% in the Irish context, to investigate the relationship between the DU90% and prescriber and practice characteristics and prescribing quality.Retrospective observational study using anonymous prescription data from a sample of Irish general practitioners (GPs). Participating GPs provided demographic details and extracted prescription data for 2018–2022 using their existing software systems. The DU90% was calculated annually at both the practice and prescriber level. Prescribing quality indicators included antibiotic, benzodiazepine prescribing rates and high‐risk nonsteroidal anti‐inflammatory drug prescribing. The association of the DU90% with prescriber and practice characteristics and prescribing quality indicators was explored with multilevel modelling.Thirty‐eight prescribers from 22 different practices were included. The mean DU90% for prescribers was 141.5 (standard deviation 12.9) and for practices was 145.62 (standard deviation 11.87). Practices in receipt of the rural deprivation grant had a significantly lower DU90% (incidence rate ratio 0.94, 95% confidence interval 0.88–0.98). There was no evidence of an association between prescriber‐level characteristics and the DU90% (sex, years qualified, number of sessions worked). There was a small positive relationship between the prescriber DU90% and total prescriptions, antibiotic and benzodiazepine prescribing rates, and higher rates of high‐risk nonsteroidal anti‐inflammatory drug prescriptions.Applying the DU90% to Irish general practice prescriptions is feasible, revealing that GPs typically use 140 medicines in the bulk of their prescribing. [ABSTRACT FROM AUTHOR]

Development of a software facility for joint operations with experimental data on thermonuclear fusion, including software for information interaction and software for analysis and display of experimental data, is presented. The architecture, functionality, and concepts of constructing a graphical user interface were determined; the technological approach to their implementation was justified, and technological solutions were selected and tested. The considered solutions support remote participation in scientific experiments and operations with their results. Conclusions based on the results of the work carried out are formulated, and plans for the further development of the software facility are presented. [ABSTRACT FROM AUTHOR]

The basic principles are described that have been applied for automation of the accelerator mass spectrometer (AMS) developed under the original project by the Budker Institute of Nuclear Physics (INP) as part of the integration project for needs of the Siberian Branch of the Russian Academy of Sciences' institutes. The facility is a tandem ion accelerator with a charge-exchange target with particle energies as high as 4 MeV. A distributed control system has been developed to control the facility, which simplifies the upgrade of the system software and hardware. By designing control programs and user interfaces in the LabVIEW environment, it is possible to easy adapt the system and increase the operating efficiency of the facility. [ABSTRACT FROM AUTHOR]

Ensuring quality and reliability mandates thorough software testing at every stage of the development cycle. As software systems grow in size, complexity, and functionality, the parallel expansion of the test suite leads to an inefficient utilization of computational power and time, presenting challenges to optimization. Therefore, the Test Suite Reduction (TSR) process is of great importance, contributing to the reduction of time and costs in executing test suites for complex software by minimizing the number of test cases to be executed. Over the past decade, machine learning-based solutions have emerged, demonstrating remarkable effectiveness and efficiency. Recent studies have delved into the application of Machine Learning (ML) in the software testing domain, where the high cost and time consumption associated with data annotation have prompted the use of unsupervised algorithms. In this research, we conducted a Systematic Mapping Study (SMS), examining the types of unsupervised algorithms implemented in developed models and thoroughly exploring the evaluation metrics employed. This study highlighted the prevalence of the K-Means clustering algorithm and the coverage metric for validation in various studies. Additionally, we identified a gap in the literature regarding scalability considerations. Our findings underscore the effective use of unsupervised learning approaches in test suite reduction. [ABSTRACT FROM AUTHOR]

This paper presents a novel method for conducting load flow (LF) calculations in radial distribution networks. The method is based on solving LF calculations as a constrained minimization problem using nonlinear programming CONOPT solver, integrated into general algebraic modeling system (GAMS) environment. The performance of the proposed CONOPT solver is evaluated on IEEE 33-bus and IEEE 69-bus test systems, where four cases were considered. In the first case, LF calculations were performed on standard test systems, and the results obtained using the proposed CONOPT solver were compared with three well-known power systems analysis software: EPSA, NEPLAN, and DigSilent. In the second and third cases, CONOPT is used for LF calculation in the presence of distributed generators (DGs) and shunt capacitors (SCs), respectively. In the fourth case, the proposed CONOPT solver is employed for LF calculation in the presence of electric vehicles (EVs). The simulation results show that the CONOPT solver outputs are accurate and fast in their convergence to global solutions in standard test systems in the presence of DGs, SCs, and EVs. [ABSTRACT FROM AUTHOR]