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Service-based systems (SBSs) are a unique category of software systems that dynamically combine various third-party services at runtime to deliver complex and adaptive functionality. This dynamic composition introduces a high level of unpredictability and uncertainty, creating potential anomalies and exerting significant pressure on system maintenance. To tackle this challenge, the conventional approach involves employing prediction-based proactive self-adaptation. Despite the prevalence of existing approaches emphasizing prediction accuracy, the critical aspect of "earliness" in predictions is often overlooked. Striking a balance between early and accurate predictions is paramount in practice. In response, we propose Proactive Self-Adaptation based on Ensemble Prediction (PSA-EP) to effectively balance the trade-off between the prediction earliness and accuracy of in SBSs. At the heart of PSA-EP lies an ensemble prediction model built upon a deep neural network and an enhanced long short-term memory (DNN-ELSTM) architecture. PSA-EP is crafted to empower SBSs to adapt to the inherent unpredictability and instability, facilitating the achievement of their adaptation goals. This adaptation mechanism not only enables effective prediction and analysis of adaptation goal violations but also addresses the reliability and performance of service level agreements (SLAs) governing quality of service (QoS). We evaluated the performance of PSA-EP in a decentralized tele-assistance system using four key metrics, and the experimental results, examined from various perspectives, underscore its exceptional performance. [ABSTRACT FROM AUTHOR]

The development of hard X‐ray nanoprobe techniques has given rise to a number of experimental methods, like nano‐XAS, nano‐XRD, nano‐XRF, ptychography and tomography. Each method has its own unique data processing algorithms. With the increase in data acquisition rate, the large amount of generated data is now a big challenge to these algorithms. In this work, an intuitive, user‐friendly software system is introduced to integrate and manage these algorithms; by taking advantage of the loosely coupled, component‐based design approach of the system, the data processing speed of the imaging algorithm is enhanced through optimization of the parallelism efficiency. This study provides meaningful solutions to tackle complexity challenges faced in synchrotron data processing. [ABSTRACT FROM AUTHOR]

Quantum software engineering is advancing in the domain of quantum computing research and application, yet the documentation is scattered. The slow transition from Von-Neumann based computation systems to quantum systems, and conserving the fundamental computing principles in software development and software engineering helps in enrichment of quantum software development. The evolution of quantum computing over the past years shows a shift in the domain of classical computation to quantum computation in the years to come. Future applications such as, quantum AI and quantum machine learning will benefit from quantum software engineering. This survey collects and explores the various documentations in the domain of quantum systems and quantum software engineering. The survey provides an in-depth exploration of quantum programming languages, which is combined with explanations of quantum computing's fundamentals. The review also goes in-depth about quantum software engineering and quantum software life cycle development, outlining the quantum software reuse methodology that is introduced in the quantum software lifecycle development domain. [ABSTRACT FROM AUTHOR]

Dependability of software systems is one of the challenging issues for software developers. Main software dependability issues include reliability, security, performability, availability, maintainability, and aging. Software becomes non-dependable due to the overconfidence of developers, lack of knowledge about dependability issues, or ignorance of dependability attributes during software development. Classification and ranking of these non-dependable software modules based on above-mentioned dependability attribute values in early phase are the main aspects of this article. Hence, computation of dependability attribute values becomes a primitive concern here. The values of software dependability attributes depend on various software metrics like: requirement stability, cyclomatic complexity, essential complexity, lines of code, and so on. Neutrosophic inference system (NIS) has been used here to compute the values of dependability attributes accurately, reducing incompleteness, indeterminacy, and impreciseness from metric values by incorporating expert knowledge. An Elman Recurrent Neural Network (ERNN)-based algorithm has been proposed here based on predicted dependability attribute values to classify dependable and non-dependable software modules. Backpropagation algorithm and Genetic Algorithm are used during training of ERNN. Mahalanobis distance (MD) is used to rank software modules based on dependability attributes at early phase of development. This entire process of dependability analysis will help to optimize resource utilization, development cost, and meet the target release time. Different comparison criteria are used to compare the effectiveness of the proposed model with some existing models based on four datasets. Performance analysis demonstrates effectiveness and usefulness for identifying and ranking the non-dependable software modules during early phase of development. [ABSTRACT FROM AUTHOR]

Software systems and product lines often use configurable features to specify a portfolio of product variants from a common core. Typically, their requirements also include constraints on which combinations of features are valid. Especially for larger systems and systems where the specifications are scattered among documents, the analysis of a new product's variability-related requirements is challenging. To address this, we introduce a scalable, tool-supported framework that uses a variability model to automate checks for missing and inconsistent features and constraints. Our approach also extends and scales traditional variability requirements engineering by incorporating combinatorial interaction testing techniques to build valid product variants covering all configurations in the variability model and to automatically discover faulty feature settings in failed builds. Results from evaluation on two configurable systems show that our framework is effective both at early detection of missing, incorrect, and inconsistent variability requirements and at later finding faulty feature configurations. [ABSTRACT FROM AUTHOR]

This article explores the emerging problem of drug- and alcohol-impaired e-scooter riding. Fifteen government and e-scooter hire firm professionals from four jurisdictions (Australia, Belgium, New Zealand, Norway) described and assessed countermeasures designed to deter drug- and alcohol-impaired riding. Government professionals were from local/municipal councils, state and national departments of transport, and policing organisations. We used Braun and Clarke's (Qual Res Psychol 18(3):328–352, 2020.https://doi.org/10.1080/14780887.2020.1769238) six-step method of thematic analysis and the NVivo qualitative analysis software system to analyse the interviews. All participants said traditional police enforcement is ineffective because laws and regulations are underdeveloped, and police have limited resources for patrols. Thematic analysis grouped countermeasures into four themes: enforcement, education, encouragement, and the road environment. Third-party policing initiatives show promise. For example, local/municipal councils required e-scooter hire firms to ban e-scooters from areas that sell alcohol through GPS-based geofencing. However, geofencing and other regulatory controls do not extend to privately owned e-scooters, a growing sector in the market. Other countermeasures used a combination of education and behavioural change techniques (BCTs) to encourage compliance. For example, innovative nudge methods (a type of BCT) have potential to change dangerous riding behaviours by targeting riding norms. However, participants said some people will continue to ride under the influence of alcohol or drugs either because they are addicted to substances or exhibit high-risk behaviours. Government should therefore adopt laws and regulations that build a more forgiving road environment, based on the safe system approach, to limit injuries when crashes occur. [ABSTRACT FROM AUTHOR]

In this paper we introduce a new class of software tools engaged in delivering successful explanations of complex processes on top of basic Explainable AI (XAI) software systems. These tools, that we call cumulatively Explanatory AI (YAI) systems, enhance the quality of the basic output of a XAI by adopting a user-centred approach to explanation that can cater to the individual needs of the explainees with measurable improvements in usability. Our approach is based on Achinstein's theory of explanations, where explaining is an illocutionary (i.e., broad yet pertinent and deliberate) act of pragmatically answering a question. Accordingly, user-centrality enters in the equation by considering that the overall amount of information generated by answering all questions can rapidly become overwhelming and that individual users may perceive the need to explore just a few of them. In this paper, we give the theoretical foundations of YAI, formally defining a user-centred explanatory tool and the space of all possible explanations, or explanatory space, generated by it. To this end, we frame the explanatory space as an hypergraph of knowledge and we identify a set of heuristics and properties that can help approximating a decomposition of it into a tree-like representation for efficient and user-centred explanation retrieval. Finally, we provide some old and new empirical results to support our theory, showing that explanations are more than textual or visual presentations of the sole information provided by a XAI. [ABSTRACT FROM AUTHOR]

Auxiliary functions in software systems, often overlooked due to their perceived simplicity, play a crucial role in overall system reliability. This study focuses on the effectiveness of agile practices, specifically the pair programming and the test-first programming practices. Despite the importance of these functions, there exists a dearth of empirical evidence on the impact of agile practices on their development, raising questions about their potential to enhance correctness without affecting time-to-market. This paper aims to bridge this gap by comparing the application of agile practices with traditional approaches in the context of auxiliary function development. We conducted six experiments involving 122 participants (85 novices and 37 professionals) who used both traditional and agile methods to develop six auxiliary functions across three different domains. Our analysis of 244 implementations suggests the potential benefits of agile practices in auxiliary function development. Pair programming showed a tendency towards improved correctness, while test-first programming did not significantly extend the total development time, particularly among professionals. However, these findings should be interpreted cautiously as they do not conclusively establish that agile practices outperform traditional approaches universally. As indicated by our results, the potential benefits of agile practices may vary depending on factors such as the programmer's experience level and the nature of the functions being developed. Further research is needed to fully understand the contexts in which these practices can be most effectively applied and to address the potential limitations of our study. [ABSTRACT FROM AUTHOR]

Faulty Interaction Localization (FIL) is a process to identify which combination of input parameter values induced test failures in combinatorial testing. An accurate and fast FIL provides helpful information to fix defects causing the test failure. One type of conventional FIL approach, which analyzes test results of whole test cases and estimates the suspiciousness of each combination, has two main concerns; (1) the accuracy is not enough, (2) the huge time cost is sometimes needed. In this paper, we propose two novel approaches to improve those concerns. FROGa attempts to estimate suspiciousness more accurately using logistic regression analysis. FROGb attempts to estimate failure-inducing combinations at high speed by estimating the subsets of them using logistic regression analysis and exploring just their supersets. Through evaluation experiments using a large number of artificial test results based on several real software systems, we observed that FROGa has very high accuracy, and FROGb can drastically reduce time cost for targets that have been difficult to complete by the conventional method. [ABSTRACT FROM AUTHOR]

Summary: Given the escalating magnitude and intricacy of software systems, software measurement data often contains irrelevant and redundant features, resulting in significant resource and storage requirements for software defect prediction (SDP). Feature selection (FS) has a vital impact on the initial data preparation phase of SDP. Nonetheless, existing FS methods suffer from issues such as insignificant dimensionality reduction, low accuracy in classifying chosen optimal feature sets, and neglect of complex interactions and dependencies between defect data and features as well as between features and classes. To tackle the aforementioned problems, this paper proposes a many‐objective SDPFS (MOSDPFS) model and the binary many‐objective PSO algorithm with adaptive enhanced selection strategy (BMaOPSO‐AR2) is proposed within this paper. MOSDPFS selects F1 score, the number of features within subsets, and correlation and redundancy measures based on mutual information (MI) as optimization objectives. BMaOPSO‐AR2 constructs a binary version of MaOPSO using transfer functions specifically for binary classification. Adaptive update formulas and the introduction of the R2 indicator are employed to augment the variety and convergence of algorithm. Additionally, performance of MOSDPFS and BMaOPSO‐AR2 are tested on the NASA‐MDP and PROMISE datasets. Numerical results prove that a proposed model and algorithm effectively reduces feature count while enhancing predictive accuracy and minimizing model complexity. [ABSTRACT FROM AUTHOR]

In this paper, we design a Spider Monkey-based Elman Spike Neural Network (SM-ESNN) to identify intrusion threats in Software Defined Networks (SDN). Utilizing analysis of multidimensional Internet Protocol (IP) flows to find intrusion and flooding assaults against central controllers. Moreover, information is first gathered from the ISCXIDS2012 dataset and updated to the SDN's secure defensive system. The developed software defense system has two sub-modules: a detection module and a mitigation module. The developed technique's key benefit is improving SDN security by quickly and accurately identifying and stopping assaults. First, the proposed SM-ESNN method is implemented in Python. The assessment measures in this scenario include accuracy, specificity, sensitivity, precision, and false alarm rate (FAR). Furthermore, the suggested SM-ESNN approach obtained improved average performances of 98.24% accuracy, 97.34% specificity, 98.68% sensitivity, and 98.33% precision, which highlights its efficiency in detecting the attacks. [ABSTRACT FROM AUTHOR]

Electronic health record (EHR) documentation serves multiple functions, including recording patient health status, enabling interprofessional communication, supporting billing, and providing data to support the quality infrastructure of a Learning Healthcare System. There is no definition and standardized method to assess documentation quality in EHRs. Using a human-centered design (HCD) approach, we define and describe a method to measure documentation quality. Documentation quality was defined as timely, accurate, user-centered, and efficient. Measurement of quality used a virtual simulated standardized patient visit via an EHR vendor platform. By observing and recording documentation efforts, nurse practitioners (NPs) (N = 12) documented the delivery of an Age-Friendly Health System (AFHS) 4Ms (what Matters, Medication, Mentation, and Mobility) clinic visit using a standardized case. Results for timely documentation indicated considerable variability in completion times of documenting the 4Ms. Accuracy varied, as there were many types of episodes of erroneous documentation and extra time in seconds in documenting the 4Ms. The type and frequency of erroneous documentation efforts were related to navigation burden when navigating to different documentation tabs. The evaluated system demonstrated poor usability, with most participants scoring between 60 and 70 on the System Usability Scale (SUS). Efficiency, measured as click burden (the number of clicks used to navigate through a software system), revealed significant variability in the number of clicks required, with the NPs averaging approximately 13 clicks above the minimum requirement. The HCD methodology used in this study to assess the documentation quality proved feasible and provided valuable information on the quality of documentation. By assessing the quality of documentation, the gathered data can be leveraged to enhance documentation, optimize user experience, and elevate the quality of data within a Learning Healthcare System. [ABSTRACT FROM AUTHOR]

Critical systems, in which failure and malfunction may result in severe human, environmental, and financial damages, are essential components in various sectors and particularly in energy domains. Although undesirable, integration error problems in the supervision of critical systems do occur, incurring significant expenses due to an operator's subjective analysis and hardware topology failures. In this work, a malleable model design approach is proposed to formulate and solve the integration error problem in critical systems' supervision in terms of reliability. A real hybrid power plant (HPP) case is considered for a case study with simulated data. A method framework with an informal approach (C4 diagram) and formal approach (hierarchical colored Petri nets) in a radial spectrum is applied to the HPP supervision design. In using formal methods, a formulation and solution to this problem through structured, scalable, and compact mathematical representations are possible. This malleable model is intended to guarantee the functional correctness and also reliability of the plant supervision system based on system software architecture. The outcomes suggest that the malleable model is appropriate for the energy domain and can be used for other types of critical systems, bringing all the benefits of this methodology to the context in which it will be applied. [ABSTRACT FROM AUTHOR]

The Windows registry contains a plethora of information in a hierarchical database. It includes system-wide settings, user preferences, installed programs, and recently accessed files and maintains timestamps that can be used to construct a detailed timeline of user activities. However, these data are unencrypted and thus vulnerable to exploitation by malicious actors who gain access to this repository. To address this security and privacy concern, we propose a novel approach that efficiently encrypts and decrypts sensitive registry data in real time. Our developed proof-of-concept program intercepts interactions between the registry's application programming interfaces (APIs) and other Windows applications using an advanced hooking technique. This enables the proposed system to be transparent to users without requiring any changes to the operating system or installed software. Our approach also implements the data protection API (DPAPI) developed by Microsoft to securely manage each user's encryption key. Ultimately, our research provides an enhanced security and privacy framework for the Windows registry, effectively fortifying the registry against security and privacy threats while maintaining its accessibility to legitimate users and applications. [ABSTRACT FROM AUTHOR]

This article introduces an open-source software stack designed for autonomous 1:10 scale model vehicles. Initially developed for the Bosch Future Mobility Challenge (BFMC) student competition, this versatile software stack is applicable to a variety of autonomous driving competitions. The stack comprises perception, planning, and control modules, each essential for precise and reliable scene understanding in complex environments such as a miniature smart city in the context of BFMC. Given the limited computing power of model vehicles and the necessity for low-latency real-time applications, the stack is implemented in C++, employs YOLO Version 5 s for environmental perception, and leverages the state-of-the-art Robot Operating System (ROS) for inter-process communication. We believe that this article and the accompanying open-source software will be a valuable resource for future teams participating in autonomous driving student competitions. Our work can serve as a foundational tool for novice teams and a reference for more experienced participants. The code and data are publicly available on GitHub. [ABSTRACT FROM AUTHOR]

Log messages from enterprise-level software systems contain crucial runtime details. Engineers can convert log messages into structured data through log parsing, laying the foundation for downstream tasks such as log anomaly detection. Existing log parsing schemes usually underperform in production environments for several reasons: first, they often ignore the semantics of log messages; second, they are often not adapted to different systems, and their performance varies greatly; and finally, they are difficult to adapt to the complexity and variety of log formats in the real environment. In response to the limitations of current approaches, we introduce IPLog (Intelligent Parse Log), a parsing method designed to address these issues. IPLog samples a limited set of log samples based on the distribution of templates in the system's historical logs, and allows the model to make full use of the small number of log samples to recognize common patterns of keywords and parameters through few-shot learning, and thus can be easily adapted to different systems. In addition, IPLog can further improve the grouping accuracy of log templates through a novel manual feedback merge query strategy based on the longest common prefix, thus enhancing the model's adaptability to handle complex log formats in production environments. We conducted experiments on four newly released public log datasets, and the experimental results show that IPLog can achieve an average grouping accuracy (GA) of 0.987 and parsing accuracy (PA) of 0.914 on the four public datasets, which are the best among the mainstream parsing schemes. These results demonstrate that IPLog is effective for log parsing tasks. [ABSTRACT FROM AUTHOR]

When creating a software model, it is necessary that it accurately captures the desired behaviour, while at the same time ensuring that any undesired behaviour is excluded. On the one hand, formal verification tools can be used to check the internal consistency of a software system, ensuring that the behaviour of one software component does not contradict another. On the other hand, software testing is essential to check the external validity of the model more comprehensively. Unfortunately, software testing is often overlooked in curricula, resulting in graduates with inadequate software testing skills for industry. Software testing tools such as TesCaV can be used to help teachers teach software testing topics in a non-intrusive and less time-consuming way. Previous research has shown that TesCaV is easy to use and that novice users produce better quality software tests when using TesCaV. However, it has remained unclear whether learners retain the skills they gain from using TesCaV even when the tool is not offered for help. In order to understand the positive effect of TesCaV on learners' software testing skills, this study conducted an experiment with 45 participants. The experiment used a pretest-treatment-posttest design. The results show that participants feel equally confident about the completeness of their test coverage, even though they identify more test cases. It is concluded that for course design, a capsule such as TesCaV can help students to understand the full complexity of software testing and help them to be more systematic in their approach. [ABSTRACT FROM AUTHOR]

This paper introduces a novel digital triangular–trapezoidal double-channel shaping algorithm to enhance the counting rate of resistive anode detectors. The algorithm is based on the trapezoidal shaping algorithm and improves it. At the extreme counting rate, the trapezoidal shaping algorithm cannot alleviate the pulse pileup, so the counting rate cannot meet the requirements of a high performance detector. The triangular–trapezoidal double-channel shaping algorithm is introduced in the resistance anode detector, which can replace the trapezoidal shaping filtering algorithm to process the output signal of the resistance anode detector and obtain the single photon position information. This improvement improves the counting rate of the resistor anode detector and reduces the resolution degradation caused by pulse pileup. The algorithm is simulated by System Generator software and implemented on FPGA (field programmable gate array). The triangular–trapezoidal double-channel shaping algorithm presented in this paper plays an important role in reducing electronic noise and pulse pileup. The algorithm is subjected to simulation testing, and it can recognize signals with a minimum pulse interval of 1 µs and counting rate up to 1000 kcps. [ABSTRACT FROM AUTHOR]

The transition from an error-prone, slower, and extremely high-volume legacy system like monolithic system to a faster, lighter, and error-free microservices based system is not always so simple. Microservices are independently deployable and allow for a better team autonomy. In this work, several migration efforts to migrate from a legacy based monolithic system to a pure distributed microservicesbased system has been tested and deployed in keeping two DevOps principles, the software code build and deployment time and latency in monolithic and microservices. Some real-time projects are considered to measure the performance and the time taken to execute the experiments. To measure the total build and deployment time and latency, Jenkins, Prometheus, and JMeter are installed which are industryrecommended softwares. It is observed that there is a total of 7 seconds taken to build and deploy at containers for 10 microservices whereas 10 monolith applications took almost 260 seconds to be built and deployed to the application server. While increasing more requests per second it is observed that upto 3000 requests per second, it impacted the response time of monolith applications but microservices stays the same. The main conclusion is that microservices are rarely impacted in response time with respect to requests per second. [ABSTRACT FROM AUTHOR]

The goal of the work described here was to develop the first neuronavigation-guided transcranial histotripsy (NaviTH) system and associated workflow for transcranial ablation. The NaviTH system consists of a 360-element, 700 kHz transmitter–receiver-capable transcranial histotripsy array, a clinical neuronavigation system and associated equipment for patient-to-array co-registration and therapy planning and targeting software systems. A workflow for NaviTH treatments, including pre-treatment aberration correction, was developed. Targeting errors stemming from target registration errors (TREs) during the patient-to-array co-registration process, as well as focal shifts caused by skull-induced aberrations, were investigated and characterized. The NaviTH system was used in treatments of two <96 h post-mortem human cadavers and in experiments in two excised human skullcaps. The NaviTH was successfully used to create ablations in the cadaver brains as confirmed in post-treatment magnetic resonance imaging A total of three ablations were created in the cadaver brains, and targeting errors of 9, 3.4 and 4.4 mm were observed in corpus callosum, septum and thalamus targets, respectively. Errors were found to be caused primarily by TREs resulting from transducer tracking instrument design flaws and imperfections in the treatment workflow. Transducer tracking instrument design and workflow improvements reduced TREs to <2 mm, and skull-induced focal shifts, following pre-treatment aberration correction, were 0.3 mm. Total targeting errors of the NaviTH system following the noted improvements were 2.5 mm. The feasibility of using the first NaviTH system in a human cadaver model has been determined. Although accuracy still needs to be improved, the proposed system has the potential to allow for transcranial histotripsy therapies without requiring active magnetic resonance treatment guidance. [ABSTRACT FROM AUTHOR]

Software managers constantly look out for methods that ensure cost effective development of good quality software products. An important means of accomplishing this is by allocating more resources to weak classes of a software product, which are prone to changes. Therefore, correct prediction of these change‐prone classes is critical. Though various researchers have investigated the performance of several algorithms for identifying them, the search for an optimum classifier still persists. To this end, this study critically investigates the use of six Heterogenous Ensemble Classifiers (HEC) for Software Change Prediction (SCP) by empirically validating datasets obtained from 12 open‐source software systems. The results of the study are statistically assessed using three robust performance indicators (AUC, F‐measure and Mathew Correlation Coefficient) in two different validation scenarios (within project and cross‐project). They indicate the superiority of Average Probability Voting Ensemble, a heterogenous classifier for determining change‐proneness in the investigated systems. The average AUC values of software change prediction models developed using this ensemble classifier exhibited an improvement of 3%‐9% and 3%‐11% respectively when compared with its base learners and homogeneous counter parts. Similar observations were inferred using other investigated performance measures. Furthermore, the evidence obtained from the results suggests that the change in number of base learners or type of meta‐learner does not exhibit significant change in the performance of corresponding heterogenous ensemble classifiers. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to view how public sentiment regarding climate change has shifted over time and to identify any trends in opinion that could prove useful for future climate change and general sentiment analysis. Positive sentiment is defined as the post having a positive emotion while a negative sentiment is defined as a post having a negative emotion. The primary hypothesis was that the average sentiment would remain slightly negative over time (H1). This study analyzes the relationship of location, popularity metrics, and emojis to sentiment in posts from the social media platform X, formerly known as tweets on Twitter. The hypothesis for emojis’ relationship to the sentiment of a post (H2) was that a post with an emoji is more likely to have a positive sentiment. This hypothesis was to determine if emojis can be used as a sentiment indicator in the future. For the locations, the prediction (H3) was that even in countries/groups, there would not be a consensus or similar sentiment. The results did not align with H1; instead of a continuous slightly negative view, the average remained neutral over the years 2015- 2022. However, the results did confirm H2 by showing the sentiment of a post with an emoji tended to have a higher sentiment than a post without one. A “weighted popularity”, which accounted for likes in addition to the original post’s sentiment, was used to confirm the findings of average sentiment per year. The results regarding location show a distribution of sentiment that doesn’t lean one way or another when close together, which aligns with the location-related hypothesis (H3). [ABSTRACT FROM AUTHOR]

The software industry is evolving at a rapid pace, making it necessary to optimize efforts and accelerate the software development process. Software can be reused to achieve quality and productivity goals. Reusability is a crucial measure that can be used to increase the overall level of software quality in less time and effort. To better understand the necessity of enhancing the software reusability of ObjectOriented (O-O) systems, this study employed a semi-automated approach to measure the values of classlevel software metrics on an input dataset collected from the MAVEN repository. This paper explored several previous studies, data strategies, and tools to predict reusability in O-O software systems. This study compares various data mining techniques to identify the most suitable approach for enhancing the reusability of O-O software systems. The analysis was based on performance parameters such as precision, MSE, and accuracy rates. Due to its higher precision and lower MSE, the SOM technique is considered one of the top data mining approaches to increase the reusability of O-O software systems. However, the results show that the different levels of reusability in O-O software systems are not adequately addressed in current solutions. [ABSTRACT FROM AUTHOR]

Software systems are often maintained by a group of experienced software developers in order to ensure that faults that may bring the system down are less likely. Large turnover in organizations such as CERN makes it important to think of ways of onboarding newcomers on a technical project rapidly. This paper focuses on optimizing the way that people get up-to-speed on the business logic and technologies used on the project by using a knowledge-imbued large language model that is enhanced using domain-specific knowledge from the group or team's internal documentation. The novelty of this approach is the gathering of all of these different open-source methods for developing a chatbot and using it in an industrial use-case. [ABSTRACT FROM AUTHOR]

In the current digital landscape, artificial intelligence-driven automation has revolutionized efficiency in various areas, enabling significant time and resource savings. However, the reliability and efficiency of software systems remain crucial challenges. To address this issue, a generation of self-adaptive software has emerged with the ability to rectify errors and autonomously optimize performance. This study focuses on the development of self-adaptive software designed for pre-programmed tasks on the Internet. The software stands out for its self-adaptation, automation, fault tolerance, efficiency, and robustness. Various technologies such as Python, MySQL, Firebase, and others were employed to enhance the adaptability of the software. The results demonstrate the effectiveness of the software, with a continuously growing self-adaptation rate and improvements in response times. Probability models were applied to analyze the software's effectiveness in fault situations. The implementation of virtual cables and multiprocessing significantly improved performance, achieving higher execution speed and scalability. In summary, this study presents self-adaptive software that rectifies errors, optimizes performance, and maintains functionality in the presence of faults, contributing to efficiency in Internet task automation. [ABSTRACT FROM AUTHOR]

Background: Approaches to documenting the software patterns of a system can support intentionally and manually documenting them or automatically extracting them from the source code. Some of the approaches that we review do not maintain proximity between code and documentation. Others do not update the documentation after the code is changed. All of them present a low level of liveness. Approach: This work proposes an approach to improve the understandability of a software system by documenting the design patterns it uses. We regard the creation and the documentation of software as part of the same process and attempt to streamline the two activities. We achieve this by increasing the feedback about the pattern instances present in the code, during development—i.e., by increasing liveness. Moreover, our approach maintains proximity between code and documentation and allows us to visualize the pattern instances under the same environment. We developed a prototype—DesignPatternDoc—for IntelliJ IDEA that continuously identifies pattern instances in the code, suggests them to the developer, generates the respective pattern-instance documentation, and enables live editing and visualization of that documentation. Results: To evaluate this approach, we conducted a controlled experiment with 21 novice developers. We asked participants to complete three tasks that involved understanding and evolving small software systems—up to six classes and 100 lines of code—and recorded the duration and the number of context switches. The results show that our approach helps developers spend less time understanding and documenting a software system when compared to using tools with a lower degree of liveness. Additionally, embedding documentation in the IDE and maintaining it close to the source code reduces context switching significantly. [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.)

The Geometric Algebra Fulcrum Library (GA-FuL) version 1.0 is introduced in this paper as a comprehensive computational library for geometric algebra (GA) and Clifford algebra (CA), in addition to other classical algebras. As a sophisticated software system, GA-FuL is useful for practical applications requiring numerical or symbolic prototyping, optimized code generation, and geometric visualization. A comprehensive overview of the GA-FuL design is provided, including its core design intentions, data-driven programming characteristics, and extensible layered design. The library is capable of representing and manipulating sparse multivectors of any dimension, scalar kind, or metric signature, including conformal and projective geometric algebras. Several practical and illustrative use cases of the library are provided to highlight its potential for mathematical, scientific, and engineering applications. The metaprogramming code optimization capabilities of GA-FuL are found to be unique among other software systems. This allows for the automated production of highly efficient code, based on powerful geometric modeling formulations provided by geometric algebra. [ABSTRACT FROM AUTHOR]

This article summarizes the authors' experiences in the development and application of the General Dynamical Model Approach related to adaptive linearizing control of biotechnological processes. Special attention has been given to some original, innovative solutions in model-based process control theory: new formalization of biotechnological process kinetics, derivation and tuning of the general software sensor of the full kinetics of biotechnological processes, and a general algorithm for fully adaptive linearizing control with software sensors. These theoretical solutions are the basis of three control strategies—fully adaptive control of the main substrate, partially adaptive control of intermediate metabolite, and recognition and stabilization of the desired physiological state based on the proposed theoretical solutions. Each strategy is illustrated in different case studies. The advantages and limitations of each of them are identified and discussed. The derived algorithms for monitoring and controlling the considered biotechnological processes are realized and included in a software platform named Interactive System for Education in Modelling and Control of Bioprocesses (InSEMCoBio). The InSEMCoBio modules and their main functions are discussed. The effectiveness of the proposed control strategies (achieving maximum productivity) has been proven through a series of simulation investigations of the considered case studies. [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]

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

The development vector of modern central processing units, which increasingly involves using a more significant number of cores and prioritizing parallelism over the high power of a single computational unit, presents new challenges for the existing software design. This work investigates and addresses the problem of access to shared memory in multithreaded environments, such as operating systems, interactive distributed computing systems, and high-performance simulation systems. Thus, the object of study is a non-blocking approach to organizing access to memory and performing basic operations with it through non-blocking synchronization. The research methods include developing an approach to organizing access to shared memory using the doubleword compare-and-swap algorithm, followed by a theoretical and practical comparison of the resulting outcome with the standard blocking access algorithm to shared memory for different configurations of the number of threads and the number of simultaneous memory access attempts. Additionally, testing was conducted within the framework of an unnamed closed-source project by integrating the solution into it, followed by A/B testing. The results showed that using non-blocking approaches is advisable, especially in comparison with locking approaches, which demonstrated a performance degradation relative to the standard allocation algorithm by more than 300 %, while non-blocking approaches provided an improvement of 40–90 %. It was also found that using hybrid approaches to the organization of shared memory systems at the software level can lead to more stable results and mitigate application performance degradation compared to classical approaches such as buddy algorithms or free lists. Despite the results obtained, the author remains cautious about the idea of memory management and pool organization at the software level and does not recommend using specialized allocation algorithms without an urgent need to speed up memory allocation itself. The purpose of these structures is still not to improve software performance directly but to enhance and speed up access to the data stored in them. [ABSTRACT FROM AUTHOR]

Owing to increasing competition, the marketing of commercial software products is a challenging task. Diverse marketing strategies are adopted to tap the huge market share. Among these, pricing and warranty are the most significant ones. The warranty policies organizations adopt usually have two dimensions: duration and effort spent during the warranty phase. In this paper, two variables are broadly taken: testing, which is an essential phase during software development, and warranty, which is a crucial component of after‐sales service. Here, we will formulate an optimization problem where returns and expenses will be influenced by price, testing resources spent, the duration for which testing is done, warranty duration and effort spent during warranty for the software product. The product sales are assumed to follow a Stochastic Bass model based on Non‐Homogeneous Poisson Process (NHPP). A free on‐site support warranty with an upper limit on duration and effort covers the 2D failure process. The numerical illustration is presented wherein profit is maximized by conjointly optimizing five variables, that is price, testing duration, testing effort, warranty duration and warranty effort. This study emphasizes on application of a popular soft computing, metaheuristic technique named genetic algorithm (GA) to solve an optimization problem for a software system. The solution to the Optimization problem is obtained using the GA tool in MATLAB. The results obtained have relevance in the industry for finding optimal values of significant variables before the release of newly developed software products to the ultimate client. The concept discussed in this paper has diverse applications in computer engineering, software and hardware industry. [ABSTRACT FROM AUTHOR]

Software is a part of most complex systems used for different purposes in today's world. The reliability of these systems must be verified before putting them to use, because their malfunction may lead to catastrophic failure, the loss of human lives, or huge costs. Software reliability can be verified by performing black‐box testing, without knowledge of the complex internal structure of these systems. In this work, an embedded IoT alarm system for detection of intruders and potential robbery is proposed. It is composed of three subsystems: embedded IoT, web‐server, and mobile phone subsystems. Cause‐effect graphing technique was applied for generating three types of black‐box test suites, by using the forward‐propagation and two different minimization algorithms. These test suites were used for black‐box testing the system, which was performed after injecting a maximum of 12 faults into the system in 100 iterations. The resulting software quality and reliability metrics showed that the forward‐propagation algorithm always achieves maximum fault detection rate (FDR), and this algorithm should be used when the highest demand for reliability must be met. The optimized minimization should only be used in cases where testing is very expensive, because it did not find faults in all testing iterations. The basic optimization retained high FDR, while reducing the impact of the minimized test suite and achieved a significant increase of test effect coverage values. [ABSTRACT FROM AUTHOR]

Wide bandgap (WBG) and ultrawide bandgap (UWBG) semiconductors in n‐type metal–oxide–semiconductor (n‐MOS) integrated circuits (ICs) are increasingly being explored for their potential applications in the rapidly developing field of electronics. This review comprehensively examines the role of n‐MOS inverters underpinned by WBG and UWBG semiconductors and their application possibilities. It delves into various n‐MOS inverter topologies, including resistive, enhancement or diode‐load, depletion‐load, and pseudo‐complementary MOS inverter topologies. Each topology's operational principles, unique advantages, and potential performance are elucidated in detail. Finally, these topologies are simulated using the Advanced Design System software for a fair comparison between various topologies. The literature and simulation results show that the pseudo‐D topology has the best gain and improved noise margin. The review methodology involves an extensive exploration of WBG/UWBG n‐MOS inverters to advance the current understanding of WBG/UWBG n‐MOS‐based ICs. [ABSTRACT FROM AUTHOR]

To build highly reliable software products, software testing is required. The software engineer has a problem determining whether to complete the testing process and when to release the software system to the market. As the focus on high software increases, project managers must consider the cost of testing, testing availability time, and release time strategy. This research article utilizes a phase type non-homogeneous Poisson process to investigate the cost analysis and operational availability of a software reliability model. The software failure inter-arrival time is considered to follow a phase type distribution. The phase type software reliability model is valuable in reducing the time and effort required to select the appropriate models for software reliability evaluation. An explicit expression for the expected, bi-criterion cost analysis and operational availability is derived using standard results of the renewal reward theorem. The optimal software release policy T* is obtained analytically. Numerical illustrations and sensitivity analysis have been presented to illustrate the cost and availability analysis's efficiency and demonstrate conformity with the study's observations. [ABSTRACT FROM AUTHOR]

One of the most popular models that provide computer resources today is cloud computing. Today's dynamic and successful platforms are created to take advantage of various resources available from service providers. Ensuring the performance and availability of such resources and services is a crucial problem. Any software system may be subject to faults that might propagate to cause failures. Such faults with the potential of contributing to failures are critical because they impair performance and result in a delayed reaction, which is regarded as a dependability problem. To ensure that critical faults can be discovered as soon as possible, the impact of such faults on the system must be tested. The performance and dependability of cloud-native systems are examined in this empirical study using fault injection, one of the chaos engineering techniques. The study explores the impacts and results of injecting various delay times into two cloud-native applications with diverse user numbers. The performance of the applications with various numbers of users is measured in relation to these delays, which accordingly reflects measuring the dependability of those systems. Firstly, the systems' architecture were identified, and serverless with two Lambda functions and containerised microservices applications were chosen, which depend on utilising and incorporating cloud-native services. Secondly, faults are injected in order to quantify performance attributes such as throughput and latency. The results of several controlled experiments carried out in real-world cloud environments provide exploratory empirical data, which promoted comparisons and statistical analysis that we utilised to identify the behaviour of the application while experiencing stress. Typical results from this investigation include an overall reduction in performance that is embodied in an increase in latency with injecting delays. However, a remarkable result is noticed at a particular delay in which defects and availability problems appear out of nowhere. These findings assist in highlighting the value of using chaos engineering in general and fault injection in particular to assess the dependability of cloud-native applications and to find unpredicted failures that could arise quickly from defects that aren't supposed to spread and result in dependability issues. [ABSTRACT FROM AUTHOR]

The field of self-adaptive software is becoming increasingly crucial because software has to adapt its behavior at runtime to keep up with dynamic and constantly evolving environments. The ability of software to modify and adjust itself is known as adaptability, which has been acknowledged as an important quality attribute. As software architecture development represents the initial stage of the design process, architectural design has emerged as a critical development activity. The degree to which the architecture can adapt to changes will be the key factor in determining the adaptability of the software ultimately released to users. Existing metrics for software architectural adaptability typically view adaptability as a positive attribute, meaning that any possibility of change in architectural elements is perceived as an improvement in adaptability. However, the application of adaptability that leads to increased costs or complexity can only be seen as a negative attribute. In other words, the side effects brought about solely for the purpose of achieving adaptability are greatly overlooked; that is, the impacts caused by adaptability exhibit both symmetry and asymmetry for software architectural adaptability. On the other hand, the existing measures of adaptability only define it from the perspective of whether a module will change, without considering changes beyond the topological structure under multiple strategies. In this paper, we propose a comprehensive assessment approach to measure software architecture adaptability. Multiple factors are considered, including the number and types of strategies employed, the cost of completing the adaptation, the extent of changes made to the architectural elements, and the overall impact of the adaptation on the architecture. An extended UML (e-UML) component diagram is presented to model software architectural changes under various types of strategies. Finally, an experiment on the znn.com software is conducted to validate the practical feasibility of our proposed method. Our approach can assist software architects in making informed decisions regarding software architecture design options to improve the adaptability and longevity of their software systems. [ABSTRACT FROM AUTHOR]

A safety-critical system is a system in which the software malfunctioning could result in death, injury, or damage to the environment. Addressing safety concerns early on at the architecture design level is critical to guide the subsequent life cycle activities to ensure that the eventual system is reliable. A fundamental approach to address safety at the design level is the adoption of architectural tactics. It is crucial for safety-critical systems to correctly implement the constraints as defined by the selected safety tactics. Given the limitations of traditional safety measures, particularly in conducting thorough analyses of safety tactics at the architectural design level, our motivation is to close this gap by integrating safety tactics and fault-based mutation testing. This article proposes a systematic approach for assessing the adequacy of test suites of safety-critical systems based on these architectural safety tactics. Our proposed approach involves the integration of safety tactics and fault-based mutation testing to comprehensively assess the adequacy of test suites in safety-critical systems. At the core of our methodology is the adoption of architectural safety tactics, addressing potential issues at the design level. We leverage fault-based testing, utilizing a dedicated metamodel and domain-specific language (DSL) to model safety views and their relation to the code. We use a case study to evaluate the effectiveness of our approach using fault-injection techniques. Our study shows that this systematic approach is feasible and effective for test suite assessment of safety-critical systems. [ABSTRACT FROM AUTHOR]

This paper studies the structure of semantic theories over modular computational systems and applies the algebraic Theory of Institutions to provide a logical representation of such theories. A modular semantic theory is here defined by a cluster of semantic theories, each for a single program's module, and by a set of relations connecting models of different semantic theories. A semantic theory of a single module is provided in terms of the set of ∑‐models mapped from the category Th of ∑‐theories and generating a hierarchy of structures from an abstract model to a concrete model of data. The collection of abstract models representing different modules of a program is formalised as the category of institutions INS, where theory morphisms express refinements, integrations, and compositions between couples of modules. Finally, it is required that a morphism in INS at any level occurs iff the same morphism occurs at the lower level alongside the Th hierarchy. [ABSTRACT FROM AUTHOR]

The lack of a common executable modeling framework that integrates systems engineering, software design, and other engineering domains is a major impediment to seamless product development processes. Our research aims to overcome this system‐software modeling gap by integrating computational, software‐related, and model execution capabilities into OPM‐based conceptual modeling, resulting in a holistic unified executable quantitative‐qualitative modeling framework. The gap is overcome via a Methodical Approach to Executable Integrative Modeling—MAXIM, an extension of OPM ISO 19450:2015, a standardization approvement given on 2015. We present the principles of MAXIM and demonstrate its operation within OPCloud—a web‐based collaborative conceptual OPM modeling framework. As a proof‐of‐concept, a model of an Airbus civil aircraft landing gear braking system is constructed and executed. Using MAXIM, engineers from five domains can collaborate at the very early phase of the system development and jointly construct a unified model that fuses qualitative and quantitative aspects of the various disciplines. This case study illustrates an important first step towards satisfying the critical and growing need to integrate systems engineering with software computations into a unified framework that enables a smooth transition from high‐level architecting to detailed, discipline‐oriented design. Such a framework is a key to agile yet robust future development of software‐intensive systems. [ABSTRACT FROM AUTHOR]

Logs play an important role in anomaly detection, fault diagnosis, and trace checking of software and network systems. Log parsing, which converts each raw log line to a constant template and a variable parameter list, is a prerequisite for system security analysis. Traditional parsing methods utilizing specific rules can only parse logs of specific formats, and most parsing methods based on deep learning require labels. However, the existing parsing methods are not applicable to logs of inconsistent formats and insufficient labels. To address these issues, we propose a robust Log parsing method based on Self-supervised Learning (LogSL), which can extract templates from logs of different formats. The essential idea of LogSL is modeling log parsing as a multi-token prediction task, which makes the multi-token prediction model learn the distribution of tokens belonging to the template in raw log lines by self-supervision mode. Furthermore, to accurately predict the tokens of the template without labeled data, we construct a Multi-token Prediction Model (MPM) combining the pre-trained XLNet module, the n-layer stacked Long Short-Term Memory Net module, and the Self-attention module. We validate LogSL on 12 benchmark log datasets, resulting in the average parsing accuracy of our parser being 3.9% higher than that of the best baseline method. Experimental results show that LogSL has superiority in terms of robustness and accuracy. In addition, a case study of anomaly detection is conducted to demonstrate the support of the proposed MPM to system security tasks based on logs. [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]

In the United States, major defense acquisition programs must implement a modular open systems approach (MOSA) as required by U.S. law. (Defense Standardization Program, 2016). Some in the defense community have focused on MOSA as a checklist compliance activity. However, designing economically and operationally competitive system platforms that are truly modular is extremely challenging. Many such modular system platform efforts fail to meet early expectations for convenience, cost, and community uptake. Open standards are challenging as well. If a highly successful, stable, well‐documented, appropriate open standard is available for an interface, then leveraging such a standard can be fairly straightforward. However, often there is no obvious or adequate open standard available and the system platform owners have to organize a community to bring such a standard into existence. This paper will review three concrete examples of such MOSA efforts. The three cases cover both commercial and defense applications as well as covering both hardware and software. The two systems that focused on hardware modularity did eventually go into production but did not completely fulfill early expectations for their programs. The software system used an out‐of‐the‐box approach and achieved a best‐in‐class result in terms of economic effectiveness, time to market, and stakeholder satisfaction. This paper will discuss underlying challenges that seem to be common to all such efforts, review the results of the three cases, and offer some simple guidelines for increasing the probability of success for such a program. [ABSTRACT FROM AUTHOR]

Software and Systems engineering projects in the Automotive industry are often mandated to develop according to the Automotive SPICE® standard. Despite a highly qualified workforce, many projects or organizations fail to implement compliant processes. This paper reinterprets the Automotive SPICE® standard, viewing its base practices as process requirements. Using a set of quality criteria for requirements, the subsequent evaluation of base practices for quality results in an aggregate and individual analysis of the ASPICE base practices. The analyses reveal, amongst others, deficiencies in the aspects of singularity, unambiguity, and reasoning structure. The paper proposes a public, collaborative effort to enrich the requirements with purpose, detail, and structure. [ABSTRACT FROM AUTHOR]

The Brunswick modular framework for modeling unsaturated soil hydraulic properties (SHP) over the full moisture range was implemented in the Hydrus suite. Users can now additionally choose between four different variants of the Brunswick model: (i) van Genuchten–Mualem (VGM), (ii) Brooks–Corey, (iii) Kosugi, and (iv) modified van Genuchten. For demonstration purposes, simulation results for two different setups, (i) bare soil evaporation and (ii) root water uptake, are presented, along with a comparison of the original VGM model and its Brunswick variant. Results show that the original VGM model underestimates the simulated cumulative evaporation and cumulative transpiration due to the inconsistent representation of the SHP in the dry moisture range. We also implemented a two‐step hydro‐PTF (pedotransfer function) into the Hydrus suite that converts the parameters of the original VGM model (from Rosetta) to the corresponding Brunswick variant. In that way, physically comprehensive simulations are ensured if no data on SHP are directly available, but information on physical soil properties (e.g., texture and bulk density) exists. Core Ideas: A modular framework was implemented in Hydrus to describe soil hydraulic properties.The new model is more robust in describing soil hydraulic properties in the dry moisture range.Hydrus simulations with the new model predict high bare soil evaporation and transpiration fluxes. [ABSTRACT FROM AUTHOR]

Software reliability is a critical aspect of ensuring the quality and dependability of software systems. However, existing software reliability models often make assumptions that do not align with real-world scenarios, such as perfect fault removal and independent faults. In this paper, we address this gap by developing a software reliability model that considers fault-dependent detection, imperfect fault removal, and the maximum number of faults that may present in the system. By accounting for these factors, our proposed model aims to provide a more accurate representation of software reliability. We evaluate the effectiveness of our model by comparing it to existing models using three commonly used goodness-of-fit criteria. The results demonstrate the importance of incorporating these considerations in software reliability modeling and highlight the superiority of our approach in capturing the complexities associated with software faults. Additionally, this paper conducts an analysis of optimal release planning, which yields highly encouraging results for software managers and engineers. This analysis adds significant value to the existing literature, further emphasizing the practical relevance of our proposed model. [ABSTRACT FROM AUTHOR]

Software systems incorporating artificial intelligence (AI) technology are called software 2.0, and their development is spreading in various fields. The purpose of this study is to enable quality engineers to easily evaluate the quality of software 2.0. For this purpose, this paper proposes a quality model that can be used by quality engineers. In order to propose a new quality model for quality engineers, this paper selects and reorganizes quality elements from general-purpose quality models and guidelines, and reconstructs quality characteristics and their evaluation methods. The quality model is considered useful for quality engineers. The paper also identifies important quality characteristics in 24 different application areas and confirms the applicability of the proposed model. The result shows that the model is useful for quality engineers to evaluate the quality of software 2.0. [ABSTRACT FROM AUTHOR]