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Increasingly, durable goods have embedded software and are technologically tethered to the vendor's server through this software, be it digital products such as smartphones or predominantly non-digital products such as automobiles and tractors. Tethering can enable the vendor to change the product's performance or features through software updates, with implications for optimal product policy. We study a monopolist technology vendor's decision to partially degrade or fully retire their installed base of tethered goods when they release their newer version. Our work presents several novel theoretical contributions with practical implications for industry. Installed base degradation can emerge in equilibrium. To obsolete the previous version, it is no longer necessary for the seller to produce an improved version. Firms may engage in economically suboptimal partial degradation for technical reasons. Firms sometimes engage in suboptimal product obsolescence despite it harming their profit, due to their inability to commit to a no-obsolescence policy. Under some conditions the seller's commitment problem could cause them to offer excessive durability. Our work suggests important policy guidelines: when releasing new models, firms should consider releasing updated versions of their older operating systems, and allow existing customers to opt out of significant software upgrades, other than essential security patches. [ABSTRACT FROM AUTHOR]

This document is an editorial introduction from the Journal of Management Information Systems. It emphasizes the importance of incorporating artificial intelligence (AI) into the field of Information Systems (IS) research. The editorial discusses the potential benefits and risks of AI, including its impact on societal well-being, economic benefits, and long-term threats. It also highlights several research papers published in the journal that explore topics such as trust in AI, AI investments, cybersecurity, value creation through AI systems, and the effects of customization in e-commerce. The editorial encourages the IS community to contribute to the understanding and deployment of AI in order to enhance organizational development and human capabilities. [Extracted from the article]

The advent of smart cities has revolutionized urban living by providing innovative solutions, such as smart homes, smart hospitals, and smart parking. These smart applications have made life easier for people by improving infrastructure and accessibility. However, the development of smart cities also poses significant challenges for cybersecurity. The smooth operation of smart applications is essential to ensure the well‐being of users, and any disruption caused by cyber‐attacks can lead to critical situations. Malware, malicious software that can cause harm to devices or systems, is the most common type of cyber‐attack. Smart applications may consist of various heterogeneous devices, each with different security requirements and specifications, making it difficult to present an efficient mechanism against malicious software for all devices within different smart applications. Hence, developing a flexible and efficient solution to overcome this challenge is vital. This research presents a framework termed as Secure Software Update for the Internet‐of‐Things (SSUIT), which is designed to protect IoT devices from malicious software updates. This framework includes three primary components: publishers hosted on the cloud platform, an intelligent broker implemented on edge devices, and IoT devices as the subscribers. The publishers send software updates to the intelligent broker, which detects whether the update is malicious or not. The intelligent broker includes a secure software engine that integrates a disassembler, a preprocessor, and predictive models to detect malicious software. The predictive models are designed by taking into account the resource‐constrained nature of IoT systems. The end‐to‐end time taken for complete execution of a software update is also reported. [ABSTRACT FROM AUTHOR]

Scenario-based verification defines the current state of the art for examining a vessel's control systems for reliability and safety. However, software updates after release can only be covered to a limited extent. To take changes to a deployed system into account, the design and test phase must be harmonized with the operational phase. For all phases, regulatory, technical and safety requirements provide the scope to which the development process and the scenario-based tests need to be aligned and whose specifications the System under Test (SuT) must adhere to during operation. For this reason, a procedure is needed that converts the requirements into a format that can be utilized across all phases and measured in a structured manner comparing the original system to the updated version. This work does so by combining scenario-based verification methods with formal composition and monitoring techniques based on contract-based design into an integrated development approach. It is shown how safety requirements can be transferred into a Verification Descriptor that in turn provides the foundation for the division into model-based system development, contract-based virtual integration testing and a scenario-based test environment. For the entire lifecycle of the System under Test (SuT) to be included, the extended scenario and contract descriptors are carried forward up to the operational phase, such that the previously defined properties of the SuT can be monitored and validated during runtime. The approach is designed alongside a minimal-viable system and evaluated on an actual implementation of a safety-critical maritime LiDAR-based positioning system. [ABSTRACT FROM AUTHOR]

Bug reports (BRs) play a major role in the software maintenance process; they alert developers about the bugs discovered by the end-users. Software applications utilize bug tracking systems (BTS) to manage submitted bug reports. Recent studies showed that the majority of BRs in BTS belong to the default severity category, which does not represent their actual severity. In this paper, we propose an approach that can automatically classify default bug reports into severe or non-severe categories. We curated a dataset based on the history of bug report logs. After that, we used the Support Vector Machine algorithm and Term Frequency-Inverse Document Frequency feature extraction method to classify default bug reports into severe or non-severe categories. The results show that building customized models for default severity bug reports provides better and more reliable results than training one model for all severity. Overall, the proposed Log model outperformed the three models (approaches) from the literature; it achieved an improvement of up to ~ 4% f-measure compared to others, and in some projects, it achieved an improvement of 11.2% f-measure. Moreover, we investigated the impact of sentiment analysis on default bug severity prediction; the results show no noticeable influence. [ABSTRACT FROM AUTHOR]

Code Smell is a term that indicates flaws in design and coding practice. God Class is a type of code smell that shows an irregular distribution of functionalities in large-sized classes. God Classes are less cohesive and more coupled in nature, thereby increasing software maintenance efforts and costs. Refactoring all such classes can disturb other related classes with code smell instances, puzzle the developers, and increase the refactoring budget. This paper proposes an automated method to prioritize God Class smell-associated classes with the fuzzy inference system. The fuzzy inference system is used to fuzzy the selected criteria—number of code smell instances, type of code smells, and changes in history. For effective refactoring, first, we moderate the dataset with the CodeMR tool and then highlight that the prioritization criteria are imperative after detecting code smells. Using five metric-based heuristics, a comparative result analysis is done to determine the fore reason for correlation (40–43%) with our results and the gravity of our prioritization criteria. Finally, we provide a severity index of classes with five type classifications and evaluate runtime performance (in seconds) to improve quality. [ABSTRACT FROM AUTHOR]

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Some programmers have difficulty when they are debugging or developing a new feature. Program comprehension is an activity to understand the behavior of a specific code. A prior understanding of a code could help the performance of a programmer. The feature location is the activity to identify which part of the source code correlated with a specific feature. The feature location mostly uses information retrieval and text processing. The usage of known structure was limited to the model on a specific domain. The proposed method combines textual-based information retrieval and class structure extraction (TESA) of elements of codes to help increase the precision and recall of feature location. The textual processing includes natural language processing and indexing using Vector Space Model (VSM)-Lucene to determine what the best token as the query. The structural extraction process was done by extracting the class member from the class relationship to determine where the correct class to expand the direction. The class relationship is used as the base of searching feature location expansion. The dataset was based on Java and using Model View Controller (MVC) design. The proposed method achieved 91% of precision and 95% of recall. [ABSTRACT FROM AUTHOR]

Code smell identification is crucial in software maintenance. The existing literature mostly focuses on single code smell identification. However, in practice, a software artefact typically exhibits multiple code smells simultaneously where their diffuseness has been assessed, suggesting that 59% of smelly classes are affected by more than one smell. So to meet this complexity found in real-world projects, we propose a multi-label learning-based approach to identify eight code smells at the class-level, i.e. the most sever software artefacts that need to be prioritized in the refactoring process. In our experiments, we have used 12 algorithms from different multi-label learning methods across 30 open-source Java projects, where significant findings have been presented. We have explored co-occurrences between class code smells and examined the impact of correlations on prediction results. Additionally, we assess multi-label learning methods to compare data adaptation versus algorithm adaptation. Our findings highlight the effectiveness of the Ensemble of Classifier Chains and Binary Relevance in achieving high-performance results. [ABSTRACT FROM AUTHOR]

To deliver a quality project on time during software maintenance, selecting the most suitable developer to assign a newly reported bug is a complex task. The proposed Data-Driven Developer Recommendation System (DDRS) examines developer performance metrics based on bug reports. It recommends using machine learning techniques such as Random Forest, Decision Tree, and Support Vector Machine (SVM). The dataset from four open-source projects (SWT, Eclipse UI, BIRT, and JDT) is initially preprocessed. The algorithm computes severity and priority scores, average bug resolution time, and developer effort before merging these parameters into a Developer Weighted Score for ranking. Machine learning models are trained on the complete dataset with textual features converted to numerical representations via TF-IDF. These algorithms predict developer appropriateness for new bugs by combining predictions and precomputed scores to produce a top ten list. When tested using 10-fold cross-validation, the model displayed better accuracy, scoring up to 98.85% on BIRT, 97.7% on JDT, 91.5% on Eclipse UI, and 96.8% on SWT. The study emphasizes the importance of bug priority, resolution time, workload, and severity in triage. The suggested methodology successfully automates developer assignment, resulting in more efficient and accurate defect resolution in software development projects. [ABSTRACT FROM AUTHOR]

During the 1950s, numerous small-capacity hydroelectric power plants were built in Spain. Seventy-five years on, it must now be decided whether to continue their operation or demolish them. In order to provide a valid answer, it is necessary to have access to decision-making tools that enable sustainable economic and environmental decision making. The present work proposes a methodology that employs an economic indicator of life cycle cost and environmental indicators of carbon footprint and embodied energy by means of life cycle data analysis. Quantification of the impacts was carried out with the support of construction cost databases and the PREDICE software tool for the quantification of environmental impacts incorporated into maintenance tasks. The case study of the "Cerrajón" power plant was analyzed, where historical hydrological cycles were considered. A life cycle scenario was evaluated in which renovation extended the life of the power plant by a further 75 years. The results show savings in environmental impacts with respect to the impacts of the Spanish energy mix of up to 175 kgCO2 per MWh produced, although no economic benefit was found. It was also shown that in climate change scenarios, the profit price breakeven increases. Rehabilitation appears to present the best choice when combining the two criteria. [ABSTRACT FROM AUTHOR]

The use of search‐based software engineering to address model‐driven engineering activities (SBMDE) is becoming more popular. Many maintenance tasks can be reformulated as a search problem, and, when those tasks are applied to software models, the search strategy has to retrieve a model fragment. There are no studies on the influence of the search parameters when applied to software models. This article evaluates the impact of different search parameter values on the performance of an evolutionary algorithm whose population is in the form of software models. Our study takes into account the nature of the model fragment location problems (MFLPs) in which the evolutionary algorithm is applied. The evaluation searches 1895 MFLPs (characterized through five measures that define MFLPs) from two industrial case studies and uses 625 different combinations of search parameter values. The results show that the impact on the performance when varying the population size, the replacement percentage, or the crossover rate produces changes of around 30% in performance. With regard to the nature of the problems, the size of the search space has the largest impact. Search parameter values and the nature of the MFLPs influence the performance when applying an evolutionary algorithm to perform fragment location on models. Search parameter values have a greater effect on precision values, and the nature of the MFLPs has a greater effect on recall values. Our results should raise awareness of the relevance of the search parameters and the nature of the problems for the SBMDE community. [ABSTRACT FROM AUTHOR]

The software maintenance process is costly, accounting for up to 70% of the total cost in the software development life cycle (SDLC). The difficulty of maintaining software increases with its size and complexity, requiring significant time and effort. One way to alleviate these costs is to automate parts of the maintenance process. This research focuses on the automation of the classification phase using decision trees (DT) to sort, rank, and accept/reject maintenance requests (MRs) for mobile applications. Our dataset consisted of 1,656 MRs. We found that DTs could automate sorting and accepting/rejecting MRs with accuracies of 71.08% and 64.15%, respectively, though ranking accuracy was lower at 50%. While DTs can reduce costs, effort, and time, human verification is still necessary. [ABSTRACT FROM AUTHOR]

Software reliability growth model (SRGM) is used as one of the reliability assessment methods to assess the software reliability. In SRGM, the degree of reliability growth may fluctuate greatly according to change in the internal state of the software. It is called the change point (CP). Several researchers proposed the SRGM considering CP. In the open source software (OSS), there are many projects that continue development even after the software is released. Therefore, major updates with breaking changes may occur in it. The major updates can be a factor that causes a CP because it greatly changes the internal state of the OSS. This paper focuses on the relationship between CP and software updates. We collect OSS fault data from a bug tracking system. Moreover, we examine the behavior of SRGM before and after software updates. Furthermore, we discuss the applicability of SRGM for CP in OSS. Also, we compare the proposed model based on CP with the model without CP. As a result, we have confirmed that the SRGM can evaluate the reliability in the environment with major updates. Moreover, the proposed method performs better than without considering CP model. Especially, the exponential model's mean value function is the suitable method to assess the OSS reliability for the proposed method. [ABSTRACT FROM AUTHOR]

Optical Earth observation satellites provide vast amounts of data on a daily basis. The top-of-atmosphere radiance measured by these satellites is usually converted to bottom-of-atmosphere radiance or reflectance which is then used for deriving numerous higher level products used for monitoring environmental conditions, climate change, stock of natural resources, etc. The increase of available remote sensing data impacts decision-making on both regional and global scales, and demands appropriate quality control and validation procedures. A HYperspectral Pointable System for Terrestrial and Aquatic Radiometry (HYPSTAR®) has been designed to provide automated, insitu multiangular reflectance measurements of land and water targets. HYPSTARSR covers 380-1020 nm spectral range at 3 nm spectral resolution and is used at water sites. For land sites the HYPSTAR-XR variant is used with the spectral range extended to 1680 nm at 10 nm spectral resolution. The spectroradiometer has multiplexed radiance and irradiance entrances, an internal mechanical shutter, and an integrated imaging camera for capturing snapshots of the targets. The spectroradiometer is mounted on a two-axis pointing system with 360° range of free movement in both axes. The system also incorporates a stable light emitting diode as a light source, used for monitoring the stability of the radiometric calibration during the long-term unattended field deployment. Autonomous operation is managed by a host system which handles data acquisition, storage, and transmission to a central WATERHYPERNET or LANDHYPERNET server according to a pre-programmed schedule. The system is remotely accessible over the internet for configuration changes and software updates. The HYPSTAR systems have been deployed at 10 water and 11 land sites for different periods ranging from a few days to a few years. The data are automatically processed at the central servers by the HYPERNETS processor and the derived radiance, irradiance, and reflectance products with associated measurement uncertainties are distributed at the WATERHYPERNET and LANDHYPERNET data portals. [ABSTRACT FROM AUTHOR]

Summary: Cloud computing is quickly becoming a common commercial model for software delivery and services, enabling companies to save maintenance, infrastructure, and labor expenses. Also, Internet of Things (IoT) apps are designed to ease developers' and users' access to networks of smart services, devices, and data. Although cloud services give nearly infinite resources, their reach is constrained. Designing coherent and organized apps is made possible by integrating the cloud and IoT. Expanding facilities by combining services is a critical component of this technology. Various services may be presented in this environment based on the user's demands. Considering their Quality of Service (QoS) attributes, discovering the appropriate available atomic services to construct the needed composite service with their collaboration in an orchestration model is an NP‐hard issue. This article suggests a service composition method using Grey Wolf Optimization (GWO) and MapReduce framework to compose services with optimized QoS. The simulation outcomes illustrate cost, availability, response time, and energy‐saving improvements through the suggested approach. Comparing the suggested technique to three baseline algorithms, the average gain is a 40% improvement in energy savings, a 14% decrease in response time, an 11% increase in availability, and a 24% drop in cost. [ABSTRACT FROM AUTHOR]

Spectral synthesis codes are essential for inferring stellar parameters and detailed chemical abundances. These codes require many physical inputs to predict an emergent spectrum. Developers adopt the best measurements of those inputs at the time they release their code, but those measurements usually improve over time faster than the software is updated. In general, the impact of using incorrect or uncertain dissociation energies is largely unknown. Here, we evaluate how incorrect dissociation energies impact abundances measured from C2, CN, CH, TiO, and MgO features. For each molecule, we synthesized optical spectra of FGKM-type main-sequence and giant stars using the literature dissociation energy and an incorrect (perturbed) dissociation energy. We find that the uncertainties in the dissociation energies adopted by spectral synthesis codes for CN, CH, TiO, and MgO lead to negligible differences in flux or abundance. C2 is the only diatomic molecule where the uncertainty of the inputted dissociation energy translates to a significant difference in flux and carbon abundance differences of up to 0.2 dex. For solar-like stars, the impact on carbon abundance is up to 0.09 dex. These large abundance differences demonstrate the importance of updating the inputs adopted by spectral synthesis codes, as well as a consensus on appropriate values between different codes. [ABSTRACT FROM AUTHOR]

Source code complexity of legacy object-oriented (OO) software has a trickle-down effect over the key activities of software development and maintenance. Package-based OO design is widely believed to be an effective modularization. Recently, theories and methodologies have been proposed to assess the complementary aspects of legacy OO systems through package-modularization metrics. These package-modularization metrics basically address non-API-based object-oriented principles, like encapsulation, commonality-of-goal, changeability, maintainability, and analyzability. Despite their ability to characterize package organization, their application towards cost-effective fault-proneness prediction is yet to be determined. In this paper, we present theoretical illustration and empirical perspective of non-API-based package-modularization metrics towards effort-aware fault-proneness prediction. First, we employ correlation analysis to evaluate the relationship between faults and package-level metrics. Second, we use multivariate logistic regression with effort-aware performance indicators (ranking and classification) to investigate the practical application of proposed metrics. Our experimental analysis over open-source Java software systems provides statistical evidence for fault-proneness prediction and relatively better explanatory power than traditional metrics. Consequently, these results guide developers for reliable and modular package-based software design. [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]

As more vehicles are being connected to the Internet and equipped with autonomous driving features, more robust safety and security measures are required for connected and autonomous vehicles (CAVs). Therefore, threat analysis and risk assessment are essential to prepare against cybersecurity risks for CAVs. Although prior studies have measured the possibility of attack and damage from attack as risk assessment indices, they have not analyzed the expanding attack surface or risk assessment indices that rely upon real-time resilience. This study proposes the PIER method to evaluate the cybersecurity risks of CAVs. We implemented cyber resilience for CAVs by presenting new criteria, such as exposure and recovery, in addition to probability and impact, as indices for the threat analysis and risk assessment of vehicles. To verify its effectiveness, the PIER method was evaluated with respect to software update over-the-air and collision avoidance features. Furthermore, we found that implementing security requirements that mitigate serious risks successfully diminishes the risk indices. Using the risk assessment matrix, the PIER method can shorten the risk determination time through high-risk coverage and a simple process. [ABSTRACT FROM AUTHOR]

Purpose: Automatic dependent surveillance-broadcast (ADS-B) is the foundational technology of the next generation air transportation system defined by Federal Aviation Authority and is one of the most precise ways for tracking aircraft position. ADS-B is intended to provide greater situational awareness to the pilots by displaying the traffic information like aircraft ID, altitude, speed and other critical parameters on the Cockpit Display of Traffic Information displays in the cockpit. Unfortunately, due to the initial proposed nature of ADS-B protocol, it is neither encrypted nor has any other innate security mechanisms, which makes it an easy target for malicious attacks. The system is vulnerable to various active and passive attacks like message ingestion, message deletion, eavesdropping, jamming, etc., which has become an area of concern for the aviation industry. The purpose of this study is to propose a method based on modified advanced encryption standard (AES) algorithm to secure the ADS=B messages and increase the integrity of ADS-B data transmissions. Design/methodology/approach: Though there are various cryptographic and non-cryptographic methods proposed to secure ADS-B data transmissions, it is evident that most of these systems have limitations in terms of cost, implementation or feasibility. The new proposed method implements AES encryption techniques on the ADS-B data on the sender side and correlated decryption mechanism at the receiver end. The system is designed based on the flight schedule data available from any flight planning systems and implementing the AES algorithm on the ADS-B data from each aircraft in the flight schedule. Findings: The suitable hardware was developed using Raspberry pi, ESP32 and Ra-02. Several runs were done to verify the original message, transmitted data and received data. During transmission, encryption algorithm was being developed, which has got very high secured transmission, and during the reception, the data was secured. Field test was conducted to validate the transmission and quality. Several trials were done to validate the transmission process. The authors have successfully shown that the ADS-B data can be encrypted using AES algorithm. The authors are successful in transmitting and receiving the ADS-B data packet using the discussed hardware and software methodology. One major advantage of using the proposed solution is that the information received is encrypted, and the receiver ADS-B system can decrypt the messages on the receiving end. This clearly proves that when the data is received by an unknown receiver, the messages cannot be decrypted, as the receiver is not capable of decrypting the AES-authenticated messages transmitted by the authenticated source. Also, AES encryption is highly unlikely to be decrypted if the encryption key and the associated decryption key are not known. Research limitations/implications: Implementation of the developed solution in actual onboard avionics systems is not within the scope of this research. Hence, assessing in the real-time distances is not covered. Social implications: The authors propose to extend this as a software solution to the onboard avionics systems by considering the required architectural changes. This solution can also bring in positive results for unmanned air vehicles in addition to the commercial aircrafts. Enhancement of security to the key operational and navigation data elements is going to be invaluable for future air traffic management and saving lives of people. Originality/value: The proposed solution has been practically implemented by developing the hardware and software as part of this research. This has been clearly brought out in the paper. The implementation has been tested using the actual ADS-B data/messages received from using the ADS-B receiver. The solution works perfectly, and this brings immense value to the aircraft-to-aircraft and aircraft-to-ground communications, specifically while using ADS-B data for communicating the position information. With the proposed architecture and minor software updates to the onboard avionics, this solution can enhance safety of flights. [ABSTRACT FROM AUTHOR]

Software maintenance is a challenging and laborious software management activity, especially for open‐source software. The bugs reports of such software allow tracking maintenance activities and were used in several empirical studies to better predict the bug resolution effort. These reports are known for their large size and contain nonrelevant instances that need to be preprocessed to be suitable for use. To this end, instance selection (IS) has been proposed in the literature as a way to reduce the size of the datasets, while keeping the relevant instances. The objective of this study is to perform an empirical study that investigates the impact of data preprocessing through IS on the performance of bug resolution prediction classifiers. To deal with this, four IS algorithms, namely, edited nearest neighbor (ENN), repeated ENN, all‐k nearest neighbors, and model class selection, are applied on five large datasets, together with five machine learning techniques. Overall, 125 experiments were performed and compared. The findings of this study highlight the positive impact of IS in providing better estimates for bug resolution prediction classifiers, in particular using repeated ENN and ENN algorithms. [ABSTRACT FROM AUTHOR]

Agile methods have emerged to overcome the obstacles of structured methodologies, such as the waterfall, prototype, spiral, and so on. There are studies showing the usefulness of agile approaches in software development. However, studies on Agile maintenance are very limited in number. Regardless of the chosen methodology, software maintenance can be carried out in either a local (on‐the‐premise) or global (distributed) environment. In a local environment, the software maintenance team is co‐located on the same premises, while in a global environment, the team is geographically dispersed from the customer. The main objective of this Systematic Mapping (SM) study is to identify the practices useful for software maintenance using the Agile approaches in the Cloud environment. We have conducted a comprehensive search in well‐known digital databases and examined the articles that map to the pre‐defined inclusion criteria. The study selected and analyzed 48 articles out of 320 published between 2000 and 2022. The findings of the mapping study reveal that Agile can resolve the major issues faced in traditional software maintenance, making the role of this approach significant in global/distributed software maintenance. Cloud computing plays a vital role in software maintenance. Most of the studies highlight the application of XP‐ and Scrum‐based Agile maintenance models. The study found a need for more Agile maintenance solutions in the cloud, highlighting the importance of agile in software maintenance, both locally and globally. Irrespective of the environment, Cloud computing provides a centralized platform for collaboration and communication, while also offering scalability and flexibility to adapt to diverse infrastructure needs. This allows agile maintenance practices to be implemented across both local and global environments, leveraging the cloud's capabilities to overcome geographical and infrastructural challenges. [ABSTRACT FROM AUTHOR]

Design patterns represent a means of communicating reusable solutions to common problems, provided they are implemented and maintained correctly. However, many design pattern instances erode as they age, sacrificing qualities they once provided. Identifying such instances of pattern decay is valuable because it allows for proactive attempts to extend the longevity and quality attributes of pattern components. Apart from structural decay, design patterns can exhibit symptoms of behavioral decay. We utilized a taxonomy that characterizes these negative behaviors and designed a case study wherein we measured structural and behavioral decay, hereafter referred to as pattern grime, as well as pattern quality and size, across pattern evolutions. We evaluated the relationships between structural and behavioral grime and found statistically significant cases of strong correlations between specific types of structural and behavioral grime. Furthermore, we extended the QATCH operational software quality model to incorporate design pattern evolution metrics and measured and correlated software quality to the presence of behavioral grime in software systems. Our results suggest a strong inverse relationship between software quality and behavioral grime. [ABSTRACT FROM AUTHOR]

Code clone detection is a critical problem in software development and maintenance domains. It aims to identify functionally identical or similar code fragments within an application. Existing works formulate the code clone detection task as a binary classification problem which predicts a code pair as a clone or not based on a pre‐defined threshold. In reality, there are various types of code clone subject to the degree of how a pair of code fragments are similar to each other. To investigate the effect of different code clone detection manners on the clone detection result, we propose Gated Recurrent Residual Learning Networks (GRRLN), a novel neural network model for code clone detection. To train GRRLN, we first represent each code fragment as a statement‐level tree sequence derived from the whole abstract syntax tree (AST). Then, a gated recurrent neural network with residual connections is adopted to fully extract the semantics of all individual statement trees together with their dependency relationships across the input statement sequence. Finally, the output representations of code fragments by GRRLN are used for similarity calculation and clone detection. We evaluate GRRLN using two real‐world datasets for code clone detection and clone type classification. Experiments show that GRRLN achieves promising and compelling results and meanwhile needs significantly less time and memory consumption compared with the state‐of‐the‐art methods. [ABSTRACT FROM AUTHOR]

Issue tracking systems are now prevalent in software development, which would help developers submit and discuss issues to solve development problems on software projects. Most previous studies have been conducted to analyze issue relations within projects, such as recommending similar or duplicate bug issues. However, along with the popularization of co‐developing through multiple projects, many issues are cross‐project correlated (CPC), that is, one issue is associated with another issue in a different project. When developers meet with CPC issues, it may primarily increase the difficulties of solving them because they need information from not only their projects but also other related projects that developers are not familiar with. Identifying a CPC issue as early as possible is a fundamental challenge for both managers and developers to allocate the resources for software maintenance and estimate the effort to solve it. This paper proposes 11 issue metrics of two groups to describe textual summary and reporters' activity, which can be extracted just after the issue was reported. We employ these 11 issue metrics to construct just‐in‐time (JIT) prediction models to identify CPC issues. To evaluate the effect of CPC issue prediction models, we conduct experiments on 16 open‐source data science and deep learning projects and compare our prediction model with two baseline models based on textual features (i.e., Term Frequency‐Inverse Document Frequency [TF‐IDF] and Word Embedding), which are commonly adopted by previous studies on issue prediction. The results show that the JIT prediction model based on issue metrics has significantly improved the performance of CPC issue prediction under two evaluation indicators, Matthew's correlation coefficient (MCC) and F1. In addition, we find that the prediction model is more suitable for large‐scale complex core projects in the open‐source ecosystem. [ABSTRACT FROM AUTHOR]

The emergence of connected vehicles has transformed the automotive sector by enhancing the vehicle's functionality, efficiency, and safety. The performance and security of these vehicles significantly rely on the deployment of the over-the-air software update. However, the execution of OTA comes with many challenges, especially with regard to security vulnerabilities and risks. The current paper delves into the complexities of the secure OTA software update for connected vehicles addressing the most critical issues; authentication; encryption and integrity verification, and risk management. Through advanced cryptographic methodologies, stringent authentication processes, and secure communication channels, automotive manufacturers and other service providers can guarantee the integrity and confidentiality of the updates, and consumers' data from malicious attack. Moreover, the paper explores the regulatory and other standards-related matters that control the use of OTA in the automotive sector. An understanding of the secure OTA update mechanisms aids the stakeholders in establishing a resilient connection in connected vehicles boosting consumer trust and the future of the automobiles industry. [ABSTRACT FROM AUTHOR]

Objective: To examine the increased use of chairside CAD/CAM among Chinese dental practitioners, and to explore the existing barriers influencing its further application and satisfaction levels. Methods: A semi-structured questionnaire was developed to gather respondents' demographic information, as well as their experiences and behaviours regarding the implementation of chairside CAD/CAM. A specialised web-based survey system and WeChat were used to display and distribute the final questionnaire. Then, the data were analysed with Chi-square tests and regression analyses to determine the effects of various demographic variables on chairside CAD/CAM applications. Results: A total of 1,969 questionnaire responses were included in the analyses. Chairside CAD/CAM systems were used by 36.9% of participants, with a higher usage rate observed among prosthodontists (60.0%) and dental practitioners holding a PhD degree (57.7%). Chairside CAD/CAM-fabricated prostheses were most commonly used in the posterior maxilla (83.3%) and mandible (86.0%), followed by the anterior maxilla and mandible (63.8% and 48.6%, respectively). Major barriers to further application included high initial investment, frequent updates of equipment and software programs, and a lack of expertise in chairside CAD/CAM usage. Conclusion: Most dental practitioners did not use chairside CAD/CAM systems. The application rate was significantly influenced by sex, location, educational background, department and type of healthcare facility. Chairside CAD/CAM users showed limited satisfaction with the aesthetic performance of the fabricated prostheses. To improve the popularity of chairside CAD/CAM systems, especially among dental practitioners lacking advanced academic degrees, it is highly advisable to optimise CAD software programs and offer comprehensive training opportunities. [ABSTRACT FROM AUTHOR]

In the rapidly evolving landscape of scientific semiconductor laboratories (commonly known as, cleanrooms), integrated with Internet of Things (IoT) technology and Cyber-Physical Systems (CPSs), several factors including operational changes, sensor aging, software updates and the introduction of new processes or equipment can lead to dynamic and non-stationary data distributions in evolving data streams. This phenomenon, known as concept drift, poses a substantial challenge for traditional data-driven digital twin static machine learning (ML) models for anomaly detection and classification. Subsequently, the drift in normal and anomalous data distributions over time causes the model performance to decay, resulting in high false alarm rates and missed anomalies. To address this issue, we present TWIN-ADAPT, a continuous learning model within a digital twin framework designed to dynamically update and optimize its anomaly classification algorithm in response to changing data conditions. This model is evaluated against state-of-the-art concept drift adaptation models and tested under simulated drift scenarios using diverse noise distributions to mimic real-world distribution shift in anomalies. TWIN-ADAPT is applied to three critical CPS datasets of Smart Manufacturing Labs (also known as "Cleanrooms"): Fumehood, Lithography Unit and Vacuum Pump. The evaluation results demonstrate that TWIN-ADAPT's continual learning model for optimized and adaptive anomaly classification achieves a high accuracy and F1 score of 96.97% and 0.97, respectively, on the Fumehood CPS dataset, showing an average performance improvement of 0.57% over the offline model. For the Lithography and Vacuum Pump datasets, TWIN-ADAPT achieves an average accuracy of 69.26% and 71.92%, respectively, with performance improvements of 75.60% and 10.42% over the offline model. These significant improvements highlight the efficacy of TWIN-ADAPT's adaptive capabilities. Additionally, TWIN-ADAPT shows a very competitive performance when compared with other benchmark drift adaptation algorithms. This performance demonstrates TWIN-ADAPT's robustness across different modalities and datasets, confirming its suitability for any IoT-driven CPS framework managing diverse data distributions in real time streams. Its adaptability and effectiveness make it a versatile tool for dynamic industrial settings. [ABSTRACT FROM AUTHOR]

Context: Cross-project defect prediction (CPDP), due to the potential of adaption by industry in realistic scenarios, had gained significant attention from the research community. Currently, existing CPDP studies use static statistical features designed by experts, which might not capture the semantic and structural aspects of software, resulting in low accuracy in defect prediction. Meanwhile, they tend to overlook the valuable iterative information brought about by version updates in mature software projects. Objective: This paper introduces DETECTOR, a novel CPDP approach based on coDE semanTic and cross-vErsion struCTural infORmation to leverage cross-versions features of the software and improve the performance of CPDP. Methods: DETECTOR parses source code to exploit Abstract Syntax Trees (ASTs) and cross-version software network (Cross-SN) that consists of internal class dependency network and cross-version class dependency edges. It utilizes Attention-based Bi-LSTM and simplified graph convolutional neural networks to automatically extract software features from ASTs and Cross-SN. The extracted features are fused using gate(⋅) to generate more effective cross-version features. Finally the source project is selected to carry out the data used to train the classifier to predict the defects. Results: Empirical studies on seven open-source Java projects, the experiment results show that: (1) DETECTOR outperforms the state-of-the-art models in CPDP; (2) our proposed cross-version dependent edges positively contribute to DETECTOR performance; (3) gate(⋅) outperforms existing strategies in fusion features; (4) more multi-versions information enhance DETECTOR's performance. Conclusion: DETECTOR can predict more defects in CPDP and improve the accuracy and effectiveness of prediction. [ABSTRACT FROM AUTHOR]

Though a myriad of changes take place in a software system during maintenance, behavioral changes carry the bulk of the reasons for software modifications. In assessing the impact of the changes made in the software, static source code analysis can be a little complex depending on the reason for the expedition. Despite the works done so far, little focus has been directed on the potential of changed methods during static source code analysis, in assessing the impact of the changes made in software. This study investigates a method-level static source code analysis technique that would generate information on the methods affected by changes made in the software. The work analyzed three Java projects. The results indicate an improvement in leveraging on the knowledge of edited methods in change impact assessment during regression testing. The approach enhances code review efforts in light of assessing operational behavior impacted by the changes made. [ABSTRACT FROM AUTHOR]