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Copyright of Baghdad Science Journal is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) 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.)

Software engineering course is one of the core courses of computer science. The students trained in the current teaching mode can no longer meet the market demand for high-technology talents. Based on this, the research attempts to optimize the traditional software engineering teaching mode by using the project teaching method (PTM). According to the basic concept of PTM and course characteristics, the reform path of PTM in software engineering course is explored in the experiment. Then in the experiment, the indicators that affect teaching reform effect is selected and a evaluation model is built. And GA-BP algorithm is used to evaluate the effect of the evaluation model. To verify the performance of the built model and the final evaluation effect, the research results are tested from the fitness of the algorithm, error performance, prediction in the data set and other aspects. GA-BP algorithm converged when it iterated to the 18th generation, and the final fitness value was 0.61. The average error square value of GA-BP was 0.35 and the minimum error square sum of GA-BP was 0.48. Its prediction accuracy in test set and training set was 93.4% and 94.1% respectively. The maximum prediction error in the training sample was only 0.015, and the performance of the above data was better than the other three algorithms. To sum up, applying PTM to software engineering curriculum reform can achieve better teaching results. [ABSTRACT FROM AUTHOR]

This document is a summary of a special issue of the journal Concurrency and Computation: Practice and Experience, which contains eight selected papers from the 14th International Conference on Parallel Processing and Applied Mathematics (PPAM 2022). The conference focused on topics related to high-performance computing (HPC) and included discussions on emerging technologies, algorithms, and software tools. The selected papers cover a range of subjects, including the convergence of iterative methods for solving nonlinear equations, testing interval arithmetic libraries, automatic performance tuning for symmetric eigenvalue problems, GPU computing, simulation of molecular magnetism, load balancing in distributed memory systems, predicting execution time of computational workloads, and resource selection for cloud-based applications. The papers provide valuable insights and advancements in the field of HPC. [Extracted from the article]

This document is a guest editorial for the special section on SEFM 2020 and 2021 in the journal Software & Systems Modeling. The International Conference on Software Engineering and Formal Methods (SEFM) aims to bring together practitioners and researchers to advance the state of the art in formal methods and their application in the software industry. This special section includes selected papers from SEFM 2020 and 2021, which were held virtually during the COVID pandemic. Each article in this section has undergone rigorous peer review and has been revised and extended compared to its conference version. The selected papers cover topics such as embedded software portability and verification, nontermination inference, quantitative modeling and analysis of BDI agents, lazy model checking for recursive state machines, P-stable abstractions of hybrid systems, fairness and guarantees for extended bounded response LTL+P synthesis, counterexample classification, analyzing the impact of human errors on interactive service robotic scenarios, and active model learning of stochastic reactive systems. [Extracted from the article]

Online judge (OJ) systems are essential in programming education as they efficiently evaluate learners' programming skills and reduce instructor workload. However, these systems often overlook the importance of software-testing concepts. To address this gap, we developed a system called Pytutor that integrates software-testing concepts to assess learners' programming abilities and proficiency by exploring students' test cases and learning behaviors. Drawing on software engineering theory and practical techniques, test capabilities are evaluated by analyzing the code coverage and mutation testing of Defining Test Cases. Since our experiment is conducted in an online environment, we can collect students' learning behaviors and further analyze the relationship between software engineering abilities and learning behaviors. We also analyzed the differences in programming and testing abilities between computer science majors and non-computer-science majors. Our findings suggest that better testing abilities may contribute to the improvement in programming abilities, whereas in the current Taiwanese education context, computer science majors do not necessarily have better testing abilities. This result provides suggestions for us to strengthen software-testing education no matter which type of students it is targeted at. [ABSTRACT FROM AUTHOR]

The document is an editorial from the Bulletin of the Polish Academy of Sciences: Technical Sciences. It introduces a special section on IIoT (Industrial Internet of Things) and AI-based optimization. The aim of the section is to present the latest developments and applications in these areas, including methods and systems in IIoT, IoT infrastructure, and applications of AI in IoT. The topics covered align with current research trends in computer science, telecommunications, and mechanical engineering. The document also highlights the importance of integrating intelligent machines and systems in industry and the role of AI in solving complex problems. [Extracted from the article]

Quantification of microglial activation through morphometric analysis has long been a staple of the neuroimmunologist’s toolkit. Microglial morphological phenomics can be conducted through either manual classification or constructing a digital skeleton and extracting morphometric data from it. Multiple open-access and paid software packages are available to generate these skeletons via semi-automated and/or fully automated methods with varying degrees of accuracy. Despite advancements in methods to generate morphometrics (quantitative measures of cellular morphology), there has been limited development of tools to analyze the datasets they generate, in particular those containing parameters from tens of thousands of cells analyzed by fully automated pipelines. In this review, we compare and critique the approaches using cluster analysis and machine learning driven predictive algorithms that have been developed to tackle these large datasets, and propose improvements for these methods. In particular, we highlight the need for a commitment to open science from groups developing these classifiers. Furthermore, we call attention to a need for communication between those with a strong software engineering/computer science background and neuroimmunologists to produce effective analytical tools with simplified operability if we are to see their wide- spread adoption by the glia biology community. [ABSTRACT FROM AUTHOR]

As a young discipline Computer Science suffers from a crisis of identity when trying to best approach research problems and conduct quality and rigorous scientific work. This is commonly handled by borrowing scientific methods from mature disciplines, such as Mathematics and Logic theories. However, whilst the object of investigation in Computer Science changes both the construction of theories describing it and the growing practical experience in its usage, accepted scientific methods do not respond well to interplaying with theoretical and practical approaches. Particularly, emergent interdisciplinary fields within Computer Science, such as Online Education, show no consensus in literature about well-defined methods to conduct systematic research, thus facing difficulties to deal with interdisciplinary research and methodological gaps. The ultimate goal of this study is to shed light on these difficulties whilst proposing methodological guidelines and good practices in order to foster sound research in the Online Education field. [ABSTRACT FROM AUTHOR]

The Hook model is used in digital products to engage and retain users through the mechanism of habit formation. This paper explores the use of Hook model techniques in two mobile applications, one being a popular taxi service (Uber taxi) and the other a social network (Instagram). The goal of this paper is to explore the Hook cycle patterns in the two products, and to identify commonalities and differences in how they are applied. Our results suggest that Hook cycle patterns appear with similar frequency; however, Instagram includes more internal Trigger calls. Uber uses fewer triggers to encourage usage, most probably because users already have a specific need for the application. For the same reason, Uber has less opportunity to fail in the reward delivery, while Instagram can use the failure (in providing a reward) as another trigger if the usage habit is already established. In addition, we introduce two types of Hook cycle patterns: internal (within a single use case) and external (transition between use cases). The insights obtained through the case studies serve as a practical reference for developing engaging and retention-focused applications. [ABSTRACT FROM AUTHOR]

Model-Based Software Engineering (MBSE) is an architecture-based software development approach. Agile, on the other hand, is a light system development approach that originated in software development. To bring together the benefits of both approaches, this article proposes an integrated Agile MBSE approach that adopts a specific instance of the Agile approach (i.e., Scrum) in combination with a specific instance of an MBSE approach (i.e., Model-Based System Architecture Process—"MBSAP") to create an Agile MBSE approach called the integrated Scrum Model-Based System Architecture Process (sMBSAP). The proposed approach was validated through a pilot study that developed a health technology system over one year, successfully producing the desired software product. This work focuses on determining whether the proposed sMBSAP approach can deliver the desired Product Increments with the support of an MBSE process. The interaction of the Product Development Team with the MBSE tool, the generation of the system model, and the delivery of the Product Increments were observed. The preliminary results showed that the proposed approach contributed to achieving the desired system development outcomes and, at the same time, generated complete system architecture artifacts that would not have been developed if Agile had been used alone. Therefore, the main contribution of this research lies in introducing a practical and operational method for merging Agile and MBSE. In parallel, the results suggest that sMBSAP is a middle ground that is more aligned with federal and state regulations, as it addresses the technical debt concerns. Future work will analyze the results of a quasi-experiment on this approach focused on measuring system development performance through common metrics. [ABSTRACT FROM AUTHOR]

We have developed a new system for measuring dental, gnathic, and facial areas with cephalogram-equivalent images created from computed tomographic imaging data. An advantage of this collaborative system is that a measurement template and automated processing are used. First, experienced orthodontists were provided with the measurement templates; they then moved the measurement markers to the specified landmarks on the cephalogram in the template. Subsequently, the program automatically detected the coordinates of the markers and calculated the distance between those coordinates. The appropriate use of this system leads to highly accurate results in large quantities of measurements in a short time by means of both manual and automatic processing. The system was developed to contribute to worldwide research into dental and craniofacial measurements; the research involved 500 patients, and the system worked successfully. [ABSTRACT FROM AUTHOR]

Arc welding is a thermal plasma process widely used to join metals. An arc welding model that couples fluid dynamic and electromagnetic equations was initially developed as a research tool. Subsequently, it was applied to improve and optimise industrial implementations of arc welding. The model includes the arc plasma, the electrode, and the workpiece in the computational domain. It incorporates several features to ensure numerical accuracy and reduce computation time and memory requirements. The arc welding code has been refactored into commercial-grade Windows software, ArcWeld, to address the needs of industrial customers. The methods used to develop ArcWeld and its extension to new arc welding regimes, which used the Workspace workflow platform, are presented. The transformation of the model to an integrated software application means that non-experts can now run the code after only elementary training. The user can easily visualise the results, improving the ability to analyse and generate insights into the arc welding process being modelled. These changes mean that scientific progress is accelerated, and that the software can be used in industry and assist welders' training. The methods used are transferrable to many other research codes. [ABSTRACT FROM AUTHOR]

Several organizations have invested in business process automation software to improve their processes. Unstandardized processes with high variance and unstructured data encumber the requirements elicitation for business process automation software. This study conducted a systematic literature review to discover methods to understand business processes and elicit requirements for business process automation software. The review revealed many methods used to understand business processes, but only one was employed to elicit requirements for business process automation software. In addition, the review identified some challenges and opportunities. The challenges of developing a business process automation software include dealing with business processes, meeting the needs of the organization, choosing the right approach, and adapting to changes in the process during the development. These challenges open opportunities for proposing specific approaches to elicit requirements in this context. [ABSTRACT FROM AUTHOR]

Building on the computer science concept of code smells, we initiate the study of law smells, i.e., patterns in legal texts that pose threats to the comprehensibility and maintainability of the law. With five intuitive law smells as running examples—namely, duplicated phrase, long element, large reference tree, ambiguous syntax, and natural language obsession—, we develop a comprehensive law smell taxonomy. This taxonomy classifies law smells by when they can be detected, which aspects of law they relate to, and how they can be discovered. We introduce text-based and graph-based methods to identify instances of law smells, confirming their utility in practice using the United States Code as a test case. Our work demonstrates how ideas from software engineering can be leveraged to assess and improve the quality of legal code, thus drawing attention to an understudied area in the intersection of law and computer science and highlighting the potential of computational legal drafting. [ABSTRACT FROM AUTHOR]

In Computer Science, Software Engineering, Business Informatics or Information Systems, conceptual modeling is an important tool and as such also contained in the respective curricular recommendations. Especially in large university courses, an automated assessment of models can improve the quality of teaching and learning. While there are many different approaches to automatically assess conceptual models, these approaches, however, often only tackle a single aspect or a single type of conceptual model. In this paper, we aim to take a comprehensive perspective on the topic and shed light on the current state of the art and technique. Furthermore, as assessment approaches have to be developed in accordance with appropriate teaching or learning activities and desired learning outcomes, we inquire in which settings automated assessment approaches are included and to which extent didactic aspects are taken into account. To this end, we have conducted a systematic literature review in which we identified 110 relevant publications on the topic which we have analyzed in a structured way. The results provide answers to five relevant research questions and pinpoint open issues which should be inquired in further research. [ABSTRACT FROM AUTHOR]

In the early stage of software development, a software requirements specification (SRS) is essential, and whether the requirements are clear and explicit is the key. However, due to various reasons, there may be a large number of misunderstandings. To generate high-quality software requirements specifications, numerous researchers have developed a variety of ways to improve the quality of SRS. In this paper, we propose a questions extraction method based on SRS elements decomposition, which evaluates the quality of SRS in the form of numerical indicators. The proposed method not only evaluates the quality of SRSs but also helps in the detection of defects, especially the description problem and omission defects in SRSs. To verify the effectiveness of the proposed method, we conducted a controlled experiment to compare the ability of checklist-based review (CBR) and the proposed method in the SRS review. The CBR is a classic method of reviewing SRS defects. After a lot of practice and improvement for a long time, CBR has excellent review ability in improving the quality of software requirements specifications. The experimental results with 40 graduate students majoring in software engineering confirmed the effectiveness and advantages of the proposed method. However, the shortcomings and deficiencies of the proposed method are also observed through the experiment. Furthermore, the proposed method has been tried out by engineers with practical work experience in software development industry and received good feedback. [ABSTRACT FROM AUTHOR]

Background. Performing back trajectory and forward trajectory using the Hybrid Single-Particle Lagrangian Integrated Trajectory Model (HYSPLIT) is a reliable approach for assessing particle transport after release among mid-field atmospheric models. HYSPLIT has an externally facing online interface that allows non-expert users to run the model trajectories without requiring extensive training or programming. However, the existing HYSPLIT interface is limited if simulations have a large amount of meteorological data and timesteps that are not coincident. The objective of this study is to design and develop a more robust tool to rapidly evaluate hazard transport conditions and to perform risk analysis, while still maintaining an intuitive and user-friendly interface. Methods. HYSPLIT calculates forward and backward trajectories of particles based on wind speed, wind direction, and the corresponding location, timestamp, and Pasquill stability classes of the regions of the atmosphere in terms of the wind speed, the amount of solar radiation, and the fractional cloud cover. The computed particle transport trajectories, combined with the online Proton Transfer Reaction-Mass Spectrometry (PTR-MS) data (https://figshare.com/articles/dataset/ARL_Data_from_ PROS_station_at_Hanford_site/19993964), can be used to identify and quantify the sources and affected area of the hazardous chemicals' emission using the potential source distribution function (PSDF). PSDF is an improved statistical function based on the well-known potential source contribution function (PSCF) in establishing the air pollutant source and receptor relationship. Performing this analysis requires a range of meteorological and pollutant concentration measurements to be statistically meaningful. The existing HYSPLIT graphical user interface (GUI) does not easily permit computations of trajectories of a dataset of meteorological data in high temporal frequency. To improve the performance of HYSPLIT computations from a large dataset and enhance risk analysis of the accidental release of material at risk, a geospatial risk analysis tool (GRAT-GUI) is created to allow large data sets to be processed instantaneously and to provide ease of visualization. Results. The GRAT-GUI is a native desktop-based application and can be run in any Windows 10 system without any internet access requirements, thus providing a secure way to process large meteorological datasets even on a standalone computer. GRAT-GUI has features to import, integrate, and convert meteorological data with various formats for hazardous chemical emission source identification and risk analysis as a self-explanatory user interface. [ABSTRACT FROM AUTHOR]

10.3390/app12199556 7 Qasem Ali A., Md Sultan A., Abd Ghani A., Zulzalil H. An Empirical Investigation of Software Customization and Its Impact on the Quality of Software as a Service: Perspectives from Software Professionals. Requirements engineering (RE) is a multidisciplinary, human-centered process integrated with both systems engineering and software engineering, which provides a shared vision and understanding of complex systems throughout their life cycles. Topics of interest considered relevant were the following: RE control natural languages (CNLs); RE visual and modeling languages; RE for specific domains, such as cyber-physical systems, big data, or AI applications; automatic analysis of requirement specifications; RE and natural language processing (NLP) techniques; software tools and platforms for RE; RE quality; RE and software testing; RE and document automation techniques; and RE for privacy, security, and safety aspects. With recent developments in cloud and mobile computing technologies, the growing need for secure, trustworthy, and cost-efficient software and the shortage of highly skilled professional software developers have given rise to a new generation of problems that require improved forms of specification and representation of such systems at multiple abstraction levels, with various concerns and stakeholder perspectives. [Extracted from the article]

As software has become more essential to research across disciplines, and as the recognition of this fact has grown, so too has the importance of professionalizing the development and maintenance of this software. The community of software professionals who work on this software have come together under the title "research software engineer" ("RSE") over the last decade. This, in turn, has created the need to understand how RSEs come into this profession and into these groups, how to further promote this career path to potential members as well as the need to understand what training gaps need to be filled for RSEs coming from different entry points. We have categorized three main classifications of entry paths into the RSE profession and identified key elements, both advantages and disadvantages, that should be acknowledged and addressed by the broader research community to attract and retain a talented, diverse pool of future RSEs. [ABSTRACT FROM AUTHOR]

Software modeling is a creative activity in which software components and their relationships are identified based on customer requirements. Based on the literature, object-oriented software modeling is based on four fundamental pillars which are abstraction, encapsulation, decomposition, and inheritance. However, despite the existence of guidelines and recommendations for implementing the object-oriented approach, novice software designers do not make good design decisions, leading to inefficient designs that cannot be modifiable, understandable, or user-friendly distribute at the development level. The literature reveals that the most common difficulties faced by software designers is a lack of understanding and confusion of concepts related to the object-oriented approach, as well as difficulties in creating Unified Modeling Language diagrams, especially class diagrams. The work presented in this article uses a qualitative and quantitative approach to determine, in a group of university students, what are the most recurrent difficulties and their persistence during the time. The qualitative case study is the method that allowed to generate the documents: diagnostic and evaluation tests. Additionally, a thematic analysis was used to identify, analyze and report patterns within the data. In order to know the occurrences of the problems in the case study, as part of our quantitative approach, a comparative study was applied to compare the results obtained between the diagnostic and evaluation tests and thus establish the similarities and differences among the cases observed, through the hierarchical clustering technique. The findings of this study show us 16 difficulties identified after the qualitative analysis, while the quantitative analysis shows us the number of occurrences and their persistence over time. The difficulties reported in both analyzes focus on these three difficulties: a) Definition of attributes that could be a class, b) Classes with inadequate or insufficient behavior and, c) Incorrect use of multiplicity between classes. Each of these difficulties is analyzed in depth in this study.

This document is a guest editorial for a special issue of the journal "Programming & Computer Software" titled "Recent Trends on Advanced Computing: The Converging Technologies." The issue features research and practical implementation results from researchers and industry experts in computer science, engineering, and technology. The papers cover a range of topics including microservices, software quality, user engagement on social media, non-functional requirements, medical software architecture, fallacies in political speeches, intelligent learning environments, and more. The guest editors for this special issue are Prof. Dr. Andrei Tchernykh, Prof. Reyes Juárez Ramírez, Dr. Esteban Mocskos, and Prof. Sergio Nesmachnow. [Extracted from the article]